output_unbalanced.txt

Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: None
Fold 1. AUC score: 0.822
Fold 2. AUC score: 0.8
Fold 3. AUC score: 0.582
Fold 4. AUC score: 0.749
Fold 5. AUC score: 0.589
Fold 6. AUC score: 0.718
Fold 7. AUC score: 0.854
Fold 8. AUC score: 0.655
Fold 9. AUC score: 0.594
Fold 10. AUC score: 0.702
Median AUC: 0.71
total runtime: 70s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: None
Fold 1. AUC score: 0.694
Fold 2. AUC score: 0.77
Fold 3. AUC score: 0.67
Fold 4. AUC score: 0.704
Fold 5. AUC score: 0.84
Fold 6. AUC score: 0.76
Fold 7. AUC score: 0.852
Fold 8. AUC score: 0.856
Fold 9. AUC score: 0.925
Fold 10. AUC score: 0.85
Median AUC: 0.805
total runtime: 83s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: None
Fold 1. AUC score: 0.642
Fold 2. AUC score: 0.623
Fold 3. AUC score: 0.604
Fold 4. AUC score: 0.48
Fold 5. AUC score: 0.734
Fold 6. AUC score: 0.568
Fold 7. AUC score: 0.817
Fold 8. AUC score: 0.875
Fold 9. AUC score: 0.846
Fold 10. AUC score: 0.875
Median AUC: 0.688
total runtime: 21s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: None
Fold 1. AUC score: 0.625
Fold 2. AUC score: 0.588
Fold 3. AUC score: 0.549
Fold 4. AUC score: 0.583
Fold 5. AUC score: 0.647
Fold 6. AUC score: 0.744
Fold 7. AUC score: 0.756
Fold 8. AUC score: 0.692
Fold 9. AUC score: 0.787
Fold 10. AUC score: 0.811
Median AUC: 0.67
total runtime: 16s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: None
Fold 1. AUC score: 0.854
Fold 2. AUC score: 0.85
Fold 3. AUC score: 0.587
Fold 4. AUC score: 0.637
Fold 5. AUC score: 0.612
Fold 6. AUC score: 0.698
Fold 7. AUC score: 0.752
Fold 8. AUC score: 0.724
Fold 9. AUC score: 0.698
Fold 10. AUC score: 0.69
Median AUC: 0.698
total runtime: 97s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: None
Fold 1. AUC score: 0.84
Fold 2. AUC score: 0.902
Fold 3. AUC score: 0.623
Fold 4. AUC score: 0.594
Fold 5. AUC score: 0.693
Fold 6. AUC score: 0.754
Fold 7. AUC score: 0.793
Fold 8. AUC score: 0.734
Fold 9. AUC score: 0.652
Fold 10. AUC score: 0.755
Median AUC: 0.744
total runtime: 115s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: None
Fold 1. AUC score: 0.804
Fold 2. AUC score: 0.839
Fold 3. AUC score: 0.553
Fold 4. AUC score: 0.575
Fold 5. AUC score: 0.657
Fold 6. AUC score: 0.707
Fold 7. AUC score: 0.666
Fold 8. AUC score: 0.709
Fold 9. AUC score: 0.704
Fold 10. AUC score: 0.601
Median AUC: 0.685
total runtime: 21s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: None
Fold 1. AUC score: 0.835
Fold 2. AUC score: 0.788
Fold 3. AUC score: 0.539
Fold 4. AUC score: 0.449
Fold 5. AUC score: 0.756
Fold 6. AUC score: 0.677
Fold 7. AUC score: 0.697
Fold 8. AUC score: 0.604
Fold 9. AUC score: 0.597
Fold 10. AUC score: 0.608
Median AUC: 0.643
total runtime: 13s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: None
Fold 1. AUC score: 0.833
Fold 2. AUC score: 0.667
Fold 3. AUC score: 0.0
Fold 4. AUC score: 0.833
Fold 5. AUC score: 0.333
Fold 6. AUC score: 0.25
Fold 7. AUC score: 0.0
Fold 8. AUC score: 1.0
Fold 9. AUC score: 0.875
Fold 10. AUC score: 0.75
Median AUC: 0.708
total runtime: 13s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: None
Fold 1. AUC score: 0.167
Fold 2. AUC score: 1.0
Fold 3. AUC score: 1.0
Fold 4. AUC score: 0.167
Fold 5. AUC score: 0.5
Fold 6. AUC score: 0.25
Fold 7. AUC score: 0.0
Fold 8. AUC score: 0.5
Fold 9. AUC score: 0.75
Fold 10. AUC score: 1.0
Median AUC: 0.5
total runtime: 24s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.685, AUC variance: 0.004
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.673, AUC variance: 0.001
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.673, AUC variance: 0.003
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.682, AUC variance: 0.002
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.647, AUC variance: 0.002
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.637, AUC variance: 0.001
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.662, AUC variance: 0.001
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.649, AUC variance: 0.004
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.648, AUC variance: 0.002
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.638, AUC variance: 0.002
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.656, AUC variance: 0.004
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.682, AUC variance: 0.005
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.651, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.708, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.699, AUC variance: 0.001
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.717, AUC variance: 0.001
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.636, AUC variance: 0.002
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.666, AUC variance: 0.001
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.702, AUC variance: 0.002
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.699, AUC variance: 0.001
Chosen strategy: LogOddsRatio_1000. Average score 0.7165551839464882 and variance 0.0013415932562708098
AutoFilter InternalCV end. Selected filter strategy : LogOddsRatio with k = 1000
Applying filter LogOddsRatio with k: 1000
Fold 1. AUC score: 0.819
Applying filter LogOddsRatio with k: 1000
Fold 2. AUC score: 0.792
Applying filter LogOddsRatio with k: 1000
Fold 3. AUC score: 0.601
Applying filter LogOddsRatio with k: 1000
Fold 4. AUC score: 0.693
Applying filter LogOddsRatio with k: 1000
Fold 5. AUC score: 0.622
Applying filter LogOddsRatio with k: 1000
Fold 6. AUC score: 0.643
Applying filter LogOddsRatio with k: 1000
Fold 7. AUC score: 0.833
Applying filter LogOddsRatio with k: 1000
Fold 8. AUC score: 0.66
Applying filter LogOddsRatio with k: 1000
Fold 9. AUC score: 0.641
Applying filter LogOddsRatio with k: 1000
Fold 10. AUC score: 0.669
Median AUC: 0.665
total runtime: 2400s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.802, AUC variance: 0.007
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.8, AUC variance: 0.015
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.802, AUC variance: 0.008
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.812, AUC variance: 0.007
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.78, AUC variance: 0.014
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.787, AUC variance: 0.01
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.807, AUC variance: 0.009
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.801, AUC variance: 0.009
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.797, AUC variance: 0.008
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.799, AUC variance: 0.007
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.803, AUC variance: 0.006
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.808, AUC variance: 0.007
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.698, AUC variance: 0.01
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.707, AUC variance: 0.005
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.731, AUC variance: 0.005
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.792, AUC variance: 0.008
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.694, AUC variance: 0.007
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.718, AUC variance: 0.012
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.734, AUC variance: 0.01
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.739, AUC variance: 0.008
Chosen strategy: InfoGain_1000. Average score 0.8122507122507123 and variance 0.007119819540175702
AutoFilter InternalCV end. Selected filter strategy : InfoGain with k = 1000
Applying filter InfoGain with k: 1000
Fold 1. AUC score: 0.637
Applying filter InfoGain with k: 1000
Fold 2. AUC score: 0.683
Applying filter InfoGain with k: 1000
Fold 3. AUC score: 0.655
Applying filter InfoGain with k: 1000
Fold 4. AUC score: 0.708
Applying filter InfoGain with k: 1000
Fold 5. AUC score: 0.833
Applying filter InfoGain with k: 1000
Fold 6. AUC score: 0.762
Applying filter InfoGain with k: 1000
Fold 7. AUC score: 0.844
Applying filter InfoGain with k: 1000
Fold 8. AUC score: 0.847
Applying filter InfoGain with k: 1000
Fold 9. AUC score: 0.933
Applying filter InfoGain with k: 1000
Fold 10. AUC score: 0.85
Median AUC: 0.798
total runtime: 3628s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.659, AUC variance: 0.032
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.659, AUC variance: 0.029
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.641, AUC variance: 0.03
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.629, AUC variance: 0.026
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.689, AUC variance: 0.013
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.674, AUC variance: 0.025
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.596, AUC variance: 0.026
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.619, AUC variance: 0.027
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.642, AUC variance: 0.026
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.638, AUC variance: 0.028
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.625, AUC variance: 0.025
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.634, AUC variance: 0.026
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.682, AUC variance: 0.026
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.695, AUC variance: 0.033
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.619, AUC variance: 0.026
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.598, AUC variance: 0.024
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.642, AUC variance: 0.024
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.64, AUC variance: 0.027
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.638, AUC variance: 0.026
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.629, AUC variance: 0.025
Chosen strategy: LogOddsRatio_500. Average score 0.6948160535117057 and variance 0.033129675593984165
AutoFilter InternalCV end. Selected filter strategy : LogOddsRatio with k = 500
Applying filter LogOddsRatio with k: 500
Fold 1. AUC score: 0.696
Applying filter LogOddsRatio with k: 500
Fold 2. AUC score: 0.616
Applying filter LogOddsRatio with k: 500
Fold 3. AUC score: 0.543
Applying filter LogOddsRatio with k: 500
Fold 4. AUC score: 0.493
Applying filter LogOddsRatio with k: 500
Fold 5. AUC score: 0.644
Applying filter LogOddsRatio with k: 500
Fold 6. AUC score: 0.64
Applying filter LogOddsRatio with k: 500
Fold 7. AUC score: 0.799
Applying filter LogOddsRatio with k: 500
Fold 8. AUC score: 0.879
Applying filter LogOddsRatio with k: 500
Fold 9. AUC score: 0.825
Applying filter LogOddsRatio with k: 500
Fold 10. AUC score: 0.895
Median AUC: 0.67
total runtime: 880s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 25
Running InternalCV for AutoFilter. Method: InfoGain k: 50
Running InternalCV for AutoFilter. Method: InfoGain k: 75
Running InternalCV for AutoFilter. Method: InfoGain k: 100
Running InternalCV for AutoFilter. Method: Chi2 k: 25
Running InternalCV for AutoFilter. Method: Chi2 k: 50
Running InternalCV for AutoFilter. Method: Chi2 k: 75
Running InternalCV for AutoFilter. Method: Chi2 k: 100
Running InternalCV for AutoFilter. Method: DecisionStump k: 25
Running InternalCV for AutoFilter. Method: DecisionStump k: 50
Running InternalCV for AutoFilter. Method: DecisionStump k: 75
Running InternalCV for AutoFilter. Method: DecisionStump k: 100
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 25
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 50
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 75
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 100
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 25
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 50
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 75
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 100
Candidate Filter: InfoGain, k: 25. Median AUC Score: 0.626, AUC variance: 0.003
Candidate Filter: InfoGain, k: 50. Median AUC Score: 0.661, AUC variance: 0.008
Candidate Filter: InfoGain, k: 75. Median AUC Score: 0.602, AUC variance: 0.011
Candidate Filter: InfoGain, k: 100. Median AUC Score: 0.62, AUC variance: 0.004
Candidate Filter: Chi2, k: 25. Median AUC Score: 0.628, AUC variance: 0.006
Candidate Filter: Chi2, k: 50. Median AUC Score: 0.674, AUC variance: 0.007
Candidate Filter: Chi2, k: 75. Median AUC Score: 0.634, AUC variance: 0.008
Candidate Filter: Chi2, k: 100. Median AUC Score: 0.623, AUC variance: 0.009
Candidate Filter: DecisionStump, k: 25. Median AUC Score: 0.568, AUC variance: 0.004
Candidate Filter: DecisionStump, k: 50. Median AUC Score: 0.618, AUC variance: 0.007
Candidate Filter: DecisionStump, k: 75. Median AUC Score: 0.548, AUC variance: 0.006
Candidate Filter: DecisionStump, k: 100. Median AUC Score: 0.563, AUC variance: 0.012
Candidate Filter: LogOddsRatio, k: 25. Median AUC Score: 0.653, AUC variance: 0.007
Candidate Filter: LogOddsRatio, k: 50. Median AUC Score: 0.602, AUC variance: 0.011
Candidate Filter: LogOddsRatio, k: 75. Median AUC Score: 0.607, AUC variance: 0.013
Candidate Filter: LogOddsRatio, k: 100. Median AUC Score: 0.618, AUC variance: 0.011
Candidate Filter: AsymmetricOptimalPrediction, k: 25. Median AUC Score: 0.638, AUC variance: 0.007
Candidate Filter: AsymmetricOptimalPrediction, k: 50. Median AUC Score: 0.572, AUC variance: 0.013
Candidate Filter: AsymmetricOptimalPrediction, k: 75. Median AUC Score: 0.637, AUC variance: 0.013
Candidate Filter: AsymmetricOptimalPrediction, k: 100. Median AUC Score: 0.685, AUC variance: 0.011
Chosen strategy: AsymmetricOptimalPrediction_100. Average score 0.6851309921962095 and variance 0.010576058676548507
AutoFilter InternalCV end. Selected filter strategy : AsymmetricOptimalPrediction with k = 100
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 1. AUC score: 0.564
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 2. AUC score: 0.436
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 3. AUC score: 0.534
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 4. AUC score: 0.598
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 5. AUC score: 0.546
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 6. AUC score: 0.506
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 7. AUC score: 0.694
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 8. AUC score: 0.697
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 9. AUC score: 0.708
Applying filter AsymmetricOptimalPrediction with k: 100
Fold 10. AUC score: 0.83
Median AUC: 0.581
total runtime: 482s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.6, AUC variance: 0.005
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.665, AUC variance: 0.002
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.67, AUC variance: 0.001
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.659, AUC variance: 0.0
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.64, AUC variance: 0.005
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.614, AUC variance: 0.0
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.664, AUC variance: 0.002
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.667, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.65, AUC variance: 0.004
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.673, AUC variance: 0.002
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.667, AUC variance: 0.0
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.662, AUC variance: 0.001
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.601, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.595, AUC variance: 0.001
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.646, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.645, AUC variance: 0.0
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.62, AUC variance: 0.004
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.62, AUC variance: 0.002
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.565, AUC variance: 0.004
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.63, AUC variance: 0.005
Chosen strategy: DecisionStump_500. Average score 0.6731821106821106 and variance 0.001900241355158417
AutoFilter InternalCV end. Selected filter strategy : DecisionStump with k = 500
Applying filter DecisionStump with k: 500
Fold 1. AUC score: 0.752
Applying filter DecisionStump with k: 500
Fold 2. AUC score: 0.817
Applying filter DecisionStump with k: 500
Fold 3. AUC score: 0.486
Applying filter DecisionStump with k: 500
Fold 4. AUC score: 0.651
Applying filter DecisionStump with k: 500
Fold 5. AUC score: 0.585
Applying filter DecisionStump with k: 500
Fold 6. AUC score: 0.568
Applying filter DecisionStump with k: 500
Fold 7. AUC score: 0.791
Applying filter DecisionStump with k: 500
Fold 8. AUC score: 0.651
Applying filter DecisionStump with k: 500
Fold 9. AUC score: 0.624
Applying filter DecisionStump with k: 500
Fold 10. AUC score: 0.571
Median AUC: 0.638
total runtime: 1687s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.645, AUC variance: 0.002
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.706, AUC variance: 0.0
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.701, AUC variance: 0.001
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.712, AUC variance: 0.001
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.615, AUC variance: 0.002
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.633, AUC variance: 0.0
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.628, AUC variance: 0.0
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.658, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.714, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.719, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.716, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.713, AUC variance: 0.001
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.624, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.647, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.606, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.618, AUC variance: 0.004
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.616, AUC variance: 0.003
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.634, AUC variance: 0.003
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.646, AUC variance: 0.004
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.615, AUC variance: 0.003
Chosen strategy: DecisionStump_500. Average score 0.7194374607165305 and variance 0.0008606295646444139
AutoFilter InternalCV end. Selected filter strategy : DecisionStump with k = 500
Applying filter DecisionStump with k: 500
Fold 1. AUC score: 0.846
Applying filter DecisionStump with k: 500
Fold 2. AUC score: 0.886
Applying filter DecisionStump with k: 500
Fold 3. AUC score: 0.569
Applying filter DecisionStump with k: 500
Fold 4. AUC score: 0.639
Applying filter DecisionStump with k: 500
Fold 5. AUC score: 0.701
Applying filter DecisionStump with k: 500
Fold 6. AUC score: 0.737
Applying filter DecisionStump with k: 500
Fold 7. AUC score: 0.778
Applying filter DecisionStump with k: 500
Fold 8. AUC score: 0.728
Applying filter DecisionStump with k: 500
Fold 9. AUC score: 0.634
Applying filter DecisionStump with k: 500
Fold 10. AUC score: 0.756
Median AUC: 0.732
total runtime: 3163s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 100
Running InternalCV for AutoFilter. Method: InfoGain k: 200
Running InternalCV for AutoFilter. Method: InfoGain k: 300
Running InternalCV for AutoFilter. Method: InfoGain k: 400
Running InternalCV for AutoFilter. Method: Chi2 k: 100
Running InternalCV for AutoFilter. Method: Chi2 k: 200
Running InternalCV for AutoFilter. Method: Chi2 k: 300
Running InternalCV for AutoFilter. Method: Chi2 k: 400
Running InternalCV for AutoFilter. Method: DecisionStump k: 100
Running InternalCV for AutoFilter. Method: DecisionStump k: 200
Running InternalCV for AutoFilter. Method: DecisionStump k: 300
Running InternalCV for AutoFilter. Method: DecisionStump k: 400
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 100
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 200
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 300
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 400
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 100
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 200
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 300
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 400
Candidate Filter: InfoGain, k: 100. Median AUC Score: 0.651, AUC variance: 0.002
Candidate Filter: InfoGain, k: 200. Median AUC Score: 0.616, AUC variance: 0.002
Candidate Filter: InfoGain, k: 300. Median AUC Score: 0.646, AUC variance: 0.003
Candidate Filter: InfoGain, k: 400. Median AUC Score: 0.653, AUC variance: 0.004
Candidate Filter: Chi2, k: 100. Median AUC Score: 0.557, AUC variance: 0.004
Candidate Filter: Chi2, k: 200. Median AUC Score: 0.606, AUC variance: 0.003
Candidate Filter: Chi2, k: 300. Median AUC Score: 0.652, AUC variance: 0.005
Candidate Filter: Chi2, k: 400. Median AUC Score: 0.657, AUC variance: 0.003
Candidate Filter: DecisionStump, k: 100. Median AUC Score: 0.66, AUC variance: 0.003
Candidate Filter: DecisionStump, k: 200. Median AUC Score: 0.663, AUC variance: 0.003
Candidate Filter: DecisionStump, k: 300. Median AUC Score: 0.673, AUC variance: 0.002
Candidate Filter: DecisionStump, k: 400. Median AUC Score: 0.667, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 100. Median AUC Score: 0.569, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 200. Median AUC Score: 0.564, AUC variance: 0.003
Candidate Filter: LogOddsRatio, k: 300. Median AUC Score: 0.6, AUC variance: 0.004
Candidate Filter: LogOddsRatio, k: 400. Median AUC Score: 0.625, AUC variance: 0.003
Candidate Filter: AsymmetricOptimalPrediction, k: 100. Median AUC Score: 0.563, AUC variance: 0.001
Candidate Filter: AsymmetricOptimalPrediction, k: 200. Median AUC Score: 0.61, AUC variance: 0.008
Candidate Filter: AsymmetricOptimalPrediction, k: 300. Median AUC Score: 0.631, AUC variance: 0.003
Candidate Filter: AsymmetricOptimalPrediction, k: 400. Median AUC Score: 0.657, AUC variance: 0.002
Chosen strategy: DecisionStump_300. Average score 0.67253861003861 and variance 0.0024250267237397906
AutoFilter InternalCV end. Selected filter strategy : DecisionStump with k = 300
Applying filter DecisionStump with k: 300
Fold 1. AUC score: 0.801
Applying filter DecisionStump with k: 300
Fold 2. AUC score: 0.83
Applying filter DecisionStump with k: 300
Fold 3. AUC score: 0.551
Applying filter DecisionStump with k: 300
Fold 4. AUC score: 0.553
Applying filter DecisionStump with k: 300
Fold 5. AUC score: 0.645
Applying filter DecisionStump with k: 300
Fold 6. AUC score: 0.685
Applying filter DecisionStump with k: 300
Fold 7. AUC score: 0.663
Applying filter DecisionStump with k: 300
Fold 8. AUC score: 0.732
Applying filter DecisionStump with k: 300
Fold 9. AUC score: 0.69
Applying filter DecisionStump with k: 300
Fold 10. AUC score: 0.619
Median AUC: 0.674
total runtime: 868s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 25
Running InternalCV for AutoFilter. Method: InfoGain k: 50
Running InternalCV for AutoFilter. Method: InfoGain k: 75
Running InternalCV for AutoFilter. Method: InfoGain k: 99
Running InternalCV for AutoFilter. Method: Chi2 k: 25
Running InternalCV for AutoFilter. Method: Chi2 k: 50
Running InternalCV for AutoFilter. Method: Chi2 k: 75
Running InternalCV for AutoFilter. Method: Chi2 k: 99
Running InternalCV for AutoFilter. Method: DecisionStump k: 25
Running InternalCV for AutoFilter. Method: DecisionStump k: 50
Running InternalCV for AutoFilter. Method: DecisionStump k: 75
Running InternalCV for AutoFilter. Method: DecisionStump k: 99
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 25
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 50
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 75
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 99
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 25
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 50
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 75
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 99
Candidate Filter: InfoGain, k: 25. Median AUC Score: 0.579, AUC variance: 0.002
Candidate Filter: InfoGain, k: 50. Median AUC Score: 0.633, AUC variance: 0.001
Candidate Filter: InfoGain, k: 75. Median AUC Score: 0.65, AUC variance: 0.001
Candidate Filter: InfoGain, k: 99. Median AUC Score: 0.653, AUC variance: 0.001
Candidate Filter: Chi2, k: 25. Median AUC Score: 0.64, AUC variance: 0.003
Candidate Filter: Chi2, k: 50. Median AUC Score: 0.625, AUC variance: 0.001
Candidate Filter: Chi2, k: 75. Median AUC Score: 0.658, AUC variance: 0.0
Candidate Filter: Chi2, k: 99. Median AUC Score: 0.653, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 25. Median AUC Score: 0.609, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 50. Median AUC Score: 0.624, AUC variance: 0.0
Candidate Filter: DecisionStump, k: 75. Median AUC Score: 0.65, AUC variance: 0.001
Candidate Filter: DecisionStump, k: 99. Median AUC Score: 0.654, AUC variance: 0.001
Candidate Filter: LogOddsRatio, k: 25. Median AUC Score: 0.622, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 50. Median AUC Score: 0.624, AUC variance: 0.002
Candidate Filter: LogOddsRatio, k: 75. Median AUC Score: 0.656, AUC variance: 0.0
Candidate Filter: LogOddsRatio, k: 99. Median AUC Score: 0.65, AUC variance: 0.001
Candidate Filter: AsymmetricOptimalPrediction, k: 25. Median AUC Score: 0.612, AUC variance: 0.001
Candidate Filter: AsymmetricOptimalPrediction, k: 50. Median AUC Score: 0.645, AUC variance: 0.003
Candidate Filter: AsymmetricOptimalPrediction, k: 75. Median AUC Score: 0.629, AUC variance: 0.002
Candidate Filter: AsymmetricOptimalPrediction, k: 99. Median AUC Score: 0.651, AUC variance: 0.001
Chosen strategy: Chi2_75. Average score 0.6579195474544313 and variance 6.536943346219866e-05
AutoFilter InternalCV end. Selected filter strategy : Chi2 with k = 75
Applying filter Chi2 with k: 75
Fold 1. AUC score: 0.782
Applying filter Chi2 with k: 75
Fold 2. AUC score: 0.724
Applying filter Chi2 with k: 75
Fold 3. AUC score: 0.489
Applying filter Chi2 with k: 75
Fold 4. AUC score: 0.525
Applying filter Chi2 with k: 75
Fold 5. AUC score: 0.657
Applying filter Chi2 with k: 75
Fold 6. AUC score: 0.619
Applying filter Chi2 with k: 75
Fold 7. AUC score: 0.594
Applying filter Chi2 with k: 75
Fold 8. AUC score: 0.629
Applying filter Chi2 with k: 75
Fold 9. AUC score: 0.551
Applying filter Chi2 with k: 75
Fold 10. AUC score: 0.623
Median AUC: 0.621
total runtime: 345s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 25
Running InternalCV for AutoFilter. Method: InfoGain k: 50
Running InternalCV for AutoFilter. Method: InfoGain k: 75
Running InternalCV for AutoFilter. Method: InfoGain k: 100
Running InternalCV for AutoFilter. Method: Chi2 k: 25
Running InternalCV for AutoFilter. Method: Chi2 k: 50
Running InternalCV for AutoFilter. Method: Chi2 k: 75
Running InternalCV for AutoFilter. Method: Chi2 k: 100
Running InternalCV for AutoFilter. Method: DecisionStump k: 25
Running InternalCV for AutoFilter. Method: DecisionStump k: 50
Running InternalCV for AutoFilter. Method: DecisionStump k: 75
Running InternalCV for AutoFilter. Method: DecisionStump k: 100
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 25
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 50
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 75
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 100
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 25
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 50
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 75
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 100
Candidate Filter: InfoGain, k: 25. Median AUC Score: 0.625, AUC variance: 0.031
Candidate Filter: InfoGain, k: 50. Median AUC Score: 0.625, AUC variance: 0.016
Candidate Filter: InfoGain, k: 75. Median AUC Score: 0.688, AUC variance: 0.014
Candidate Filter: InfoGain, k: 100. Median AUC Score: 0.719, AUC variance: 0.012
Candidate Filter: Chi2, k: 25. Median AUC Score: 0.812, AUC variance: 0.008
Candidate Filter: Chi2, k: 50. Median AUC Score: 0.781, AUC variance: 0.014
Candidate Filter: Chi2, k: 75. Median AUC Score: 0.719, AUC variance: 0.014
Candidate Filter: Chi2, k: 100. Median AUC Score: 0.719, AUC variance: 0.012
Candidate Filter: DecisionStump, k: 25. Median AUC Score: 0.812, AUC variance: 0.009
Candidate Filter: DecisionStump, k: 50. Median AUC Score: 0.719, AUC variance: 0.018
Candidate Filter: DecisionStump, k: 75. Median AUC Score: 0.719, AUC variance: 0.014
Candidate Filter: DecisionStump, k: 100. Median AUC Score: 0.719, AUC variance: 0.012
Candidate Filter: LogOddsRatio, k: 25. Median AUC Score: 0.812, AUC variance: 0.008
Candidate Filter: LogOddsRatio, k: 50. Median AUC Score: 0.75, AUC variance: 0.011
Candidate Filter: LogOddsRatio, k: 75. Median AUC Score: 0.719, AUC variance: 0.014
Candidate Filter: LogOddsRatio, k: 100. Median AUC Score: 0.719, AUC variance: 0.012
Candidate Filter: AsymmetricOptimalPrediction, k: 25. Median AUC Score: 0.812, AUC variance: 0.008
Candidate Filter: AsymmetricOptimalPrediction, k: 50. Median AUC Score: 0.719, AUC variance: 0.014
Candidate Filter: AsymmetricOptimalPrediction, k: 75. Median AUC Score: 0.719, AUC variance: 0.014
Candidate Filter: AsymmetricOptimalPrediction, k: 100. Median AUC Score: 0.719, AUC variance: 0.012
Chosen strategy: Chi2_25. Average score 0.8125 and variance 0.0078125
AutoFilter InternalCV end. Selected filter strategy : Chi2 with k = 25
Applying filter Chi2 with k: 25
Fold 1. AUC score: 0.583
Applying filter Chi2 with k: 25
Fold 2. AUC score: 0.5
Applying filter Chi2 with k: 25
Fold 3. AUC score: 1.0
Applying filter Chi2 with k: 25
Fold 4. AUC score: 0.667
Applying filter Chi2 with k: 25
Fold 5. AUC score: 0.667
Applying filter Chi2 with k: 25
Fold 6. AUC score: 1.0
Applying filter Chi2 with k: 25
Fold 7. AUC score: 0.75
Applying filter Chi2 with k: 25
Fold 8. AUC score: 1.0
Applying filter Chi2 with k: 25
Fold 9. AUC score: 0.875
Applying filter Chi2 with k: 25
Fold 10. AUC score: 1.0
Median AUC: 0.812
total runtime: 249s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: AutoFilter
Running InternalCV for AutoFilter. Method: InfoGain k: 250
Running InternalCV for AutoFilter. Method: InfoGain k: 500
Running InternalCV for AutoFilter. Method: InfoGain k: 750
Running InternalCV for AutoFilter. Method: InfoGain k: 1000
Running InternalCV for AutoFilter. Method: Chi2 k: 250
Running InternalCV for AutoFilter. Method: Chi2 k: 500
Running InternalCV for AutoFilter. Method: Chi2 k: 750
Running InternalCV for AutoFilter. Method: Chi2 k: 1000
Running InternalCV for AutoFilter. Method: DecisionStump k: 250
Running InternalCV for AutoFilter. Method: DecisionStump k: 500
Running InternalCV for AutoFilter. Method: DecisionStump k: 750
Running InternalCV for AutoFilter. Method: DecisionStump k: 1000
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 250
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 500
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 750
Running InternalCV for AutoFilter. Method: LogOddsRatio k: 1000
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 250
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 500
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 750
Running InternalCV for AutoFilter. Method: AsymmetricOptimalPrediction k: 1000
Candidate Filter: InfoGain, k: 250. Median AUC Score: 0.562, AUC variance: 0.079
Candidate Filter: InfoGain, k: 500. Median AUC Score: 0.438, AUC variance: 0.077
Candidate Filter: InfoGain, k: 750. Median AUC Score: 0.5, AUC variance: 0.051
Candidate Filter: InfoGain, k: 1000. Median AUC Score: 0.438, AUC variance: 0.056
Candidate Filter: Chi2, k: 250. Median AUC Score: 0.562, AUC variance: 0.055
Candidate Filter: Chi2, k: 500. Median AUC Score: 0.5, AUC variance: 0.049
Candidate Filter: Chi2, k: 750. Median AUC Score: 0.438, AUC variance: 0.057
Candidate Filter: Chi2, k: 1000. Median AUC Score: 0.406, AUC variance: 0.059
Candidate Filter: DecisionStump, k: 250. Median AUC Score: 0.562, AUC variance: 0.035
Candidate Filter: DecisionStump, k: 500. Median AUC Score: 0.562, AUC variance: 0.037
Candidate Filter: DecisionStump, k: 750. Median AUC Score: 0.5, AUC variance: 0.046
Candidate Filter: DecisionStump, k: 1000. Median AUC Score: 0.375, AUC variance: 0.059
Candidate Filter: LogOddsRatio, k: 250. Median AUC Score: 0.5, AUC variance: 0.051
Candidate Filter: LogOddsRatio, k: 500. Median AUC Score: 0.5, AUC variance: 0.043
Candidate Filter: LogOddsRatio, k: 750. Median AUC Score: 0.5, AUC variance: 0.048
Candidate Filter: LogOddsRatio, k: 1000. Median AUC Score: 0.438, AUC variance: 0.049
Candidate Filter: AsymmetricOptimalPrediction, k: 250. Median AUC Score: 0.562, AUC variance: 0.042
Candidate Filter: AsymmetricOptimalPrediction, k: 500. Median AUC Score: 0.5, AUC variance: 0.049
Candidate Filter: AsymmetricOptimalPrediction, k: 750. Median AUC Score: 0.438, AUC variance: 0.048
Candidate Filter: AsymmetricOptimalPrediction, k: 1000. Median AUC Score: 0.438, AUC variance: 0.062
Chosen strategy: DecisionStump_250. Average score 0.5625 and variance 0.035312500000000004
AutoFilter InternalCV end. Selected filter strategy : DecisionStump with k = 250
Applying filter DecisionStump with k: 250
Fold 1. AUC score: 0.0
Applying filter DecisionStump with k: 250
Fold 2. AUC score: 0.833
Applying filter DecisionStump with k: 250
Fold 3. AUC score: 1.0
Applying filter DecisionStump with k: 250
Fold 4. AUC score: 0.0
Applying filter DecisionStump with k: 250
Fold 5. AUC score: 0.5
Applying filter DecisionStump with k: 250
Fold 6. AUC score: 0.0
Applying filter DecisionStump with k: 250
Fold 7. AUC score: 0.25
Applying filter DecisionStump with k: 250
Fold 8. AUC score: 0.25
Applying filter DecisionStump with k: 250
Fold 9. AUC score: 0.75
Applying filter DecisionStump with k: 250
Fold 10. AUC score: 1.0
Median AUC: 0.375
total runtime: 1301s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 1. AUC score: 0.813
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 2. AUC score: 0.818
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 3. AUC score: 0.57
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 4. AUC score: 0.749
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 5. AUC score: 0.581
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 6. AUC score: 0.719
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 7. AUC score: 0.87
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 8. AUC score: 0.652
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 9. AUC score: 0.594
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 10. AUC score: 0.702
Median AUC: 0.71
total runtime: 266s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 1. AUC score: 0.678
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 2. AUC score: 0.754
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 3. AUC score: 0.671
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 4. AUC score: 0.72
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 5. AUC score: 0.835
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 6. AUC score: 0.765
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 7. AUC score: 0.848
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 8. AUC score: 0.869
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 9. AUC score: 0.934
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 10. AUC score: 0.828
Median AUC: 0.796
total runtime: 348s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 1. AUC score: 0.611
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 2. AUC score: 0.64
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 3. AUC score: 0.607
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 4. AUC score: 0.48
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 5. AUC score: 0.731
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 6. AUC score: 0.579
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 7. AUC score: 0.824
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 8. AUC score: 0.871
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 9. AUC score: 0.825
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 10. AUC score: 0.87
Median AUC: 0.686
total runtime: 61s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 1. AUC score: 0.625
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 2. AUC score: 0.591
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 3. AUC score: 0.54
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 4. AUC score: 0.577
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 5. AUC score: 0.636
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 6. AUC score: 0.724
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 7. AUC score: 0.738
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 8. AUC score: 0.695
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 9. AUC score: 0.794
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 10. AUC score: 0.797
Median AUC: 0.666
total runtime: 25s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 1. AUC score: 0.834
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 2. AUC score: 0.853
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 3. AUC score: 0.573
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 4. AUC score: 0.636
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 5. AUC score: 0.622
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 6. AUC score: 0.707
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 7. AUC score: 0.764
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 8. AUC score: 0.724
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 9. AUC score: 0.706
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 10. AUC score: 0.699
Median AUC: 0.707
total runtime: 179s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 1. AUC score: 0.838
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 2. AUC score: 0.897
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 3. AUC score: 0.624
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 4. AUC score: 0.598
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 5. AUC score: 0.69
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 6. AUC score: 0.761
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 7. AUC score: 0.801
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 8. AUC score: 0.739
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 9. AUC score: 0.645
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 10. AUC score: 0.758
Median AUC: 0.749
total runtime: 339s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 1. AUC score: 0.795
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 2. AUC score: 0.834
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 3. AUC score: 0.535
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 4. AUC score: 0.57
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 5. AUC score: 0.646
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 6. AUC score: 0.722
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 7. AUC score: 0.663
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 8. AUC score: 0.718
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 9. AUC score: 0.705
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 10. AUC score: 0.61
Median AUC: 0.684
total runtime: 69s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 1. AUC score: 0.755
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 2. AUC score: 0.783
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 3. AUC score: 0.532
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 4. AUC score: 0.46
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 5. AUC score: 0.756
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 6. AUC score: 0.681
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 7. AUC score: 0.694
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 8. AUC score: 0.619
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 9. AUC score: 0.606
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 10. AUC score: 0.628
Median AUC: 0.655
total runtime: 16s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 1. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 3. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 4. AUC score: 0.917
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 6. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 7. AUC score: 0.25
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 8. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.75
total runtime: 15s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: InfoGain
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 1. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 2. AUC score: 0.833
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 3. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 4. AUC score: 0.167
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 6. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 7. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 8. AUC score: 0.25
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.375
total runtime: 43s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 1. AUC score: 0.813
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 2. AUC score: 0.818
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 3. AUC score: 0.57
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 4. AUC score: 0.749
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 5. AUC score: 0.581
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 6. AUC score: 0.719
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 7. AUC score: 0.87
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 8. AUC score: 0.652
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 9. AUC score: 0.594
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 10. AUC score: 0.702
Median AUC: 0.71
total runtime: 71s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 1. AUC score: 0.678
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 2. AUC score: 0.754
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 3. AUC score: 0.671
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 4. AUC score: 0.72
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 5. AUC score: 0.835
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 6. AUC score: 0.765
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 7. AUC score: 0.848
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 8. AUC score: 0.869
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 9. AUC score: 0.934
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 10. AUC score: 0.828
Median AUC: 0.796
total runtime: 83s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 1. AUC score: 0.611
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 2. AUC score: 0.64
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 3. AUC score: 0.607
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 4. AUC score: 0.48
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 5. AUC score: 0.731
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 6. AUC score: 0.579
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 7. AUC score: 0.824
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 8. AUC score: 0.871
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 9. AUC score: 0.825
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 10. AUC score: 0.87
Median AUC: 0.686
total runtime: 22s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 1. AUC score: 0.625
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 2. AUC score: 0.591
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 3. AUC score: 0.54
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 4. AUC score: 0.577
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 5. AUC score: 0.636
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 6. AUC score: 0.724
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 7. AUC score: 0.738
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 8. AUC score: 0.695
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 9. AUC score: 0.794
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 10. AUC score: 0.797
Median AUC: 0.666
total runtime: 15s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 1. AUC score: 0.834
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 2. AUC score: 0.853
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 3. AUC score: 0.573
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 4. AUC score: 0.636
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 5. AUC score: 0.622
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 6. AUC score: 0.707
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 7. AUC score: 0.764
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 8. AUC score: 0.724
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 9. AUC score: 0.706
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 10. AUC score: 0.699
Median AUC: 0.707
total runtime: 95s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 1. AUC score: 0.838
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 2. AUC score: 0.897
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 3. AUC score: 0.624
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 4. AUC score: 0.598
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 5. AUC score: 0.69
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 6. AUC score: 0.761
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 7. AUC score: 0.801
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 8. AUC score: 0.739
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 9. AUC score: 0.645
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 10. AUC score: 0.758
Median AUC: 0.749
total runtime: 122s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 1. AUC score: 0.795
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 2. AUC score: 0.834
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 3. AUC score: 0.535
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 4. AUC score: 0.57
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 5. AUC score: 0.646
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 6. AUC score: 0.722
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 7. AUC score: 0.663
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 8. AUC score: 0.718
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 9. AUC score: 0.705
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 10. AUC score: 0.61
Median AUC: 0.684
total runtime: 22s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 1. AUC score: 0.755
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 2. AUC score: 0.783
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 3. AUC score: 0.532
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 4. AUC score: 0.46
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 5. AUC score: 0.756
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 6. AUC score: 0.681
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 7. AUC score: 0.694
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 8. AUC score: 0.619
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 9. AUC score: 0.606
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 10. AUC score: 0.628
Median AUC: 0.655
total runtime: 13s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 1. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 3. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 4. AUC score: 0.917
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 6. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 7. AUC score: 0.25
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 8. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.75
total runtime: 13s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: Chi2
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 1. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 2. AUC score: 0.833
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 3. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 4. AUC score: 0.167
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 6. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 7. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 8. AUC score: 0.25
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.375
total runtime: 21s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 1. AUC score: 0.82
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 2. AUC score: 0.82
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 3. AUC score: 0.577
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 4. AUC score: 0.752
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 5. AUC score: 0.582
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 6. AUC score: 0.716
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 7. AUC score: 0.871
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 8. AUC score: 0.645
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 9. AUC score: 0.606
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 10. AUC score: 0.694
Median AUC: 0.705
total runtime: 871s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 1. AUC score: 0.697
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 2. AUC score: 0.764
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 3. AUC score: 0.688
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 4. AUC score: 0.714
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 5. AUC score: 0.832
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 6. AUC score: 0.759
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 7. AUC score: 0.838
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 8. AUC score: 0.889
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 9. AUC score: 0.928
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 10. AUC score: 0.839
Median AUC: 0.798
total runtime: 1257s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 1. AUC score: 0.641
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 2. AUC score: 0.64
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 3. AUC score: 0.598
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 4. AUC score: 0.475
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 5. AUC score: 0.743
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 6. AUC score: 0.592
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 7. AUC score: 0.82
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 8. AUC score: 0.868
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 9. AUC score: 0.838
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 10. AUC score: 0.874
Median AUC: 0.692
total runtime: 215s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 1. AUC score: 0.634
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 2. AUC score: 0.594
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 3. AUC score: 0.536
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 4. AUC score: 0.58
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 5. AUC score: 0.631
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 6. AUC score: 0.728
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 7. AUC score: 0.75
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 8. AUC score: 0.694
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 9. AUC score: 0.765
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 10. AUC score: 0.815
Median AUC: 0.664
total runtime: 77s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 1. AUC score: 0.83
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 2. AUC score: 0.855
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 3. AUC score: 0.583
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 4. AUC score: 0.623
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 5. AUC score: 0.618
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 6. AUC score: 0.717
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 7. AUC score: 0.76
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 8. AUC score: 0.722
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 9. AUC score: 0.701
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 10. AUC score: 0.694
Median AUC: 0.709
total runtime: 537s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 1. AUC score: 0.845
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 2. AUC score: 0.901
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 3. AUC score: 0.608
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 4. AUC score: 0.605
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 5. AUC score: 0.692
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 6. AUC score: 0.776
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 7. AUC score: 0.809
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 8. AUC score: 0.731
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 9. AUC score: 0.648
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 10. AUC score: 0.76
Median AUC: 0.745
total runtime: 1030s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 1. AUC score: 0.806
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 2. AUC score: 0.83
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 3. AUC score: 0.546
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 4. AUC score: 0.578
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 5. AUC score: 0.655
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 6. AUC score: 0.707
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 7. AUC score: 0.664
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 8. AUC score: 0.725
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 9. AUC score: 0.71
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 10. AUC score: 0.597
Median AUC: 0.686
total runtime: 186s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 1. AUC score: 0.753
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 2. AUC score: 0.781
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 3. AUC score: 0.531
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 4. AUC score: 0.452
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 5. AUC score: 0.757
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 6. AUC score: 0.679
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 7. AUC score: 0.683
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 8. AUC score: 0.62
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 9. AUC score: 0.606
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 10. AUC score: 0.649
Median AUC: 0.664
total runtime: 41s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 1. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 3. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 4. AUC score: 0.917
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 6. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 7. AUC score: 0.25
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 8. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.75
total runtime: 51s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: DecisionStump
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 1. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 2. AUC score: 0.833
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 3. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 4. AUC score: 0.167
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 6. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 7. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 8. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.5
total runtime: 374s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 1. AUC score: 0.816
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 2. AUC score: 0.818
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 3. AUC score: 0.576
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 4. AUC score: 0.74
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 5. AUC score: 0.567
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 6. AUC score: 0.71
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 7. AUC score: 0.869
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 8. AUC score: 0.639
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 9. AUC score: 0.596
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 10. AUC score: 0.692
Median AUC: 0.701
total runtime: 125s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 1. AUC score: 0.678
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 2. AUC score: 0.76
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 3. AUC score: 0.677
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 4. AUC score: 0.713
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 5. AUC score: 0.837
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 6. AUC score: 0.791
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 7. AUC score: 0.853
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 8. AUC score: 0.874
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 9. AUC score: 0.928
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 10. AUC score: 0.82
Median AUC: 0.806
total runtime: 121s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 1. AUC score: 0.663
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 2. AUC score: 0.61
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 3. AUC score: 0.6
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 4. AUC score: 0.488
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 5. AUC score: 0.722
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 6. AUC score: 0.563
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 7. AUC score: 0.814
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 8. AUC score: 0.875
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 9. AUC score: 0.841
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 10. AUC score: 0.878
Median AUC: 0.693
total runtime: 31s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 1. AUC score: 0.625
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 2. AUC score: 0.594
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 3. AUC score: 0.535
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 4. AUC score: 0.571
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 5. AUC score: 0.63
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 6. AUC score: 0.736
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 7. AUC score: 0.74
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 8. AUC score: 0.698
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 9. AUC score: 0.784
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 10. AUC score: 0.826
Median AUC: 0.664
total runtime: 30s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 1. AUC score: 0.834
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 2. AUC score: 0.845
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 3. AUC score: 0.571
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 4. AUC score: 0.616
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 5. AUC score: 0.625
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 6. AUC score: 0.7
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 7. AUC score: 0.756
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 8. AUC score: 0.719
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 9. AUC score: 0.707
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 10. AUC score: 0.695
Median AUC: 0.703
total runtime: 136s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 1. AUC score: 0.849
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 2. AUC score: 0.9
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 3. AUC score: 0.622
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 4. AUC score: 0.604
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 5. AUC score: 0.684
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 6. AUC score: 0.749
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 7. AUC score: 0.793
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 8. AUC score: 0.744
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 9. AUC score: 0.635
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 10. AUC score: 0.763
Median AUC: 0.747
total runtime: 168s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 1. AUC score: 0.806
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 2. AUC score: 0.831
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 3. AUC score: 0.556
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 4. AUC score: 0.576
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 5. AUC score: 0.671
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 6. AUC score: 0.71
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 7. AUC score: 0.66
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 8. AUC score: 0.703
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 9. AUC score: 0.711
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 10. AUC score: 0.606
Median AUC: 0.687
total runtime: 29s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 1. AUC score: 0.747
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 2. AUC score: 0.796
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 3. AUC score: 0.539
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 4. AUC score: 0.46
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 5. AUC score: 0.757
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 6. AUC score: 0.668
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 7. AUC score: 0.69
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 8. AUC score: 0.619
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 9. AUC score: 0.61
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 10. AUC score: 0.631
Median AUC: 0.65
total runtime: 15s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 1. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 3. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 4. AUC score: 0.917
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 6. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 7. AUC score: 0.25
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 8. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.75
total runtime: 15s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: LogOddsRatio
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 1. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 3. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 4. AUC score: 0.167
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 6. AUC score: 0.25
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 7. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 8. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.5
total runtime: 28s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Protein Interactors dataset_v1.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 1. AUC score: 0.814
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 2. AUC score: 0.825
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 3. AUC score: 0.58
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 4. AUC score: 0.753
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 5. AUC score: 0.568
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 6. AUC score: 0.719
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 7. AUC score: 0.874
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 8. AUC score: 0.646
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 9. AUC score: 0.613
Warning, invalid k value. k: 0 features: 5789. Setting k as all.
Fold 10. AUC score: 0.698
Median AUC: 0.708
total runtime: 120s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GOTerms dataset_v1.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 1. AUC score: 0.713
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 2. AUC score: 0.75
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 3. AUC score: 0.674
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 4. AUC score: 0.711
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 5. AUC score: 0.836
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 6. AUC score: 0.772
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 7. AUC score: 0.85
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 8. AUC score: 0.879
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 9. AUC score: 0.928
Warning, invalid k value. k: 0 features: 7707. Setting k as all.
Fold 10. AUC score: 0.827
Median AUC: 0.799
total runtime: 129s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans Phenotypes dataset_v1.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 1. AUC score: 0.61
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 2. AUC score: 0.606
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 3. AUC score: 0.599
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 4. AUC score: 0.519
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 5. AUC score: 0.736
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 6. AUC score: 0.555
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 7. AUC score: 0.826
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 8. AUC score: 0.867
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 9. AUC score: 0.837
Warning, invalid k value. k: 0 features: 1273. Setting k as all.
Fold 10. AUC score: 0.878
Median AUC: 0.673
total runtime: 35s


Opening the file: C Elegans dataset examples\Version-1 datasets (no score threshold)\CElegans GenAge dataset_v1.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 1. AUC score: 0.604
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 2. AUC score: 0.589
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 3. AUC score: 0.533
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 4. AUC score: 0.583
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 5. AUC score: 0.631
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 6. AUC score: 0.742
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 7. AUC score: 0.738
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 8. AUC score: 0.694
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 9. AUC score: 0.781
Warning, invalid k value. k: 0 features: 366. Setting k as all.
Fold 10. AUC score: 0.823
Median AUC: 0.663
total runtime: 23s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Protein Interactors dataset_v2.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 1. AUC score: 0.833
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 2. AUC score: 0.847
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 3. AUC score: 0.575
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 4. AUC score: 0.631
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 5. AUC score: 0.622
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 6. AUC score: 0.708
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 7. AUC score: 0.761
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 8. AUC score: 0.724
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 9. AUC score: 0.706
Warning, invalid k value. k: 0 features: 2773. Setting k as all.
Fold 10. AUC score: 0.699
Median AUC: 0.707
total runtime: 126s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GOTerms dataset_v2.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 1. AUC score: 0.846
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 2. AUC score: 0.894
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 3. AUC score: 0.622
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 4. AUC score: 0.602
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 5. AUC score: 0.694
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 6. AUC score: 0.757
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 7. AUC score: 0.803
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 8. AUC score: 0.747
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 9. AUC score: 0.642
Warning, invalid k value. k: 0 features: 5778. Setting k as all.
Fold 10. AUC score: 0.759
Median AUC: 0.752
total runtime: 169s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans Phenotypes dataset_v2.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 1. AUC score: 0.811
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 2. AUC score: 0.835
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 3. AUC score: 0.545
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 4. AUC score: 0.571
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 5. AUC score: 0.657
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 6. AUC score: 0.711
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 7. AUC score: 0.661
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 8. AUC score: 0.71
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 9. AUC score: 0.7
Warning, invalid k value. k: 0 features: 1036. Setting k as all.
Fold 10. AUC score: 0.617
Median AUC: 0.681
total runtime: 34s


Opening the file: C Elegans dataset examples\Version-2 datasets (45% minimum score threshold)\CElegans GenAge dataset_v2.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 1. AUC score: 0.746
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 2. AUC score: 0.784
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 3. AUC score: 0.529
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 4. AUC score: 0.454
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 5. AUC score: 0.756
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 6. AUC score: 0.679
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 7. AUC score: 0.695
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 8. AUC score: 0.623
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 9. AUC score: 0.609
Warning, invalid k value. k: 0 features: 153. Setting k as all.
Fold 10. AUC score: 0.637
Median AUC: 0.658
total runtime: 16s


Opening the file: Drosophila datasets (validation experiment)\Drosophila Protein Interactors .tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 1. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 2. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 3. AUC score: 0.667
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 4. AUC score: 0.917
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 6. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 7. AUC score: 0.25
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 8. AUC score: 1.0
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 131. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.75
total runtime: 16s


Opening the file: Drosophila datasets (validation experiment)\Drosophila GO Terms.tsv, for filter: AsymmetricOptimalPrediction
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 1. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 2. AUC score: 0.833
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 3. AUC score: 1.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 4. AUC score: 0.25
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 5. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 6. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 7. AUC score: 0.0
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 8. AUC score: 0.5
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 9. AUC score: 0.75
Warning, invalid k value. k: 0 features: 2345. Setting k as all.
Fold 10. AUC score: 1.0
Median AUC: 0.5
total runtime: 28s