
 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541818
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1957240937658011
 pos diff: [-0.017950993524715197  0.                  ], inv diff: [0.9510571625642207 0.                ], topk inv diff: [0.6992580024349291 0.                ]
 Variance: 0.47011355520217035
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [174.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587838389968995
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [622, 65] samples with noise 5.0
Average adj diff: [0.815112540192926]
Average feat diff: [1.4308681672025723]
Average noise diff: [1.4308681672025723]
Average mAP: [0.9115988763309401]
AUC: 0.9362381867116026
AUC_ind: [0.9789802073242735 0.9439965005668715]
nDCG: [0.9887259875326093]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [535, 152] samples with noise 10.0
Average adj diff: [2.2934579439252336]
Average feat diff: [4.]
Average noise diff: [4.]
Average mAP: [0.8078359595541762]
AUC: 0.9036583356811678
AUC_ind: [0.943526939881895  0.9151951010386526]
nDCG: [0.9713615168075416]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [486, 201] samples with noise 15.0
Average adj diff: [3.4958847736625516]
Average feat diff: [6.325102880658436]
Average noise diff: [6.325102880658436]
Average mAP: [0.7290581554042198]
AUC: 0.874522535977307
AUC_ind: [0.9125300286193897 0.8929619539626437]
nDCG: [0.9579415423550679]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [445, 242] samples with noise 20.0
Average adj diff: [5.202247191011236]
Average feat diff: [8.561797752808989]
Average noise diff: [8.561797752808989]
Average mAP: [0.6653212441234855]
AUC: 0.8499765483713123
AUC_ind: [0.8809063058058715 0.8633783693307848]
nDCG: [0.9464341941515919]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [405, 282] samples with noise 25.0
Average adj diff: [6.128395061728395]
Average feat diff: [11.392592592592592]
Average noise diff: [11.392592592592592]
Average mAP: [0.5901988863221855]
AUC: 0.8135562552661502
AUC_ind: [0.8465061606111953 0.8329308144560321]
nDCG: [0.9316709195718985]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [374, 313] samples with noise 30.0
Average adj diff: [7.481283422459893]
Average feat diff: [13.828877005347593]
Average noise diff: [13.828877005347593]
Average mAP: [0.5570668150315806]
AUC: 0.7896189712720999
AUC_ind: [0.826214934660226  0.8067914462936229]
nDCG: [0.922290520604099]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9789802073242735 0.9439965005668715], [622, 65]
0.1, [0.943526939881895  0.9151951010386526], [535, 152]
0.15, [0.9125300286193897 0.8929619539626437], [486, 201]
0.2, [0.8809063058058715 0.8633783693307848], [445, 242]
0.25, [0.8465061606111953 0.8329308144560321], [405, 282]
0.3, [0.826214934660226  0.8067914462936229], [374, 313]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865154587745
 pos diff: [-0.01797054688482826  0.                 ], inv diff: [0.9513489541094723 0.                ], topk inv diff: [0.6961142249416229 0.                ]
 Variance: 0.4720040143183669
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9578071579633387
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [636, 51] samples with noise 5.0
Average adj diff: [0.8883647798742138]
Average feat diff: [1.4119496855345912]
Average noise diff: [1.4119496855345912]
Average mAP: [0.9187044775077732]
AUC: 0.9375651747276561
AUC_ind: [0.9802944096872129 0.9578782701130795]
nDCG: [0.9892741367331961]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [543, 144] samples with noise 10.0
Average adj diff: [2.2117863720073663]
Average feat diff: [3.7495395948434624]
Average noise diff: [3.7495395948434624]
Average mAP: [0.8124050684278721]
AUC: 0.9031405776070689
AUC_ind: [0.9453039318859295 0.927864647209597 ]
nDCG: [0.9731286301578439]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [468, 219] samples with noise 15.0
Average adj diff: [3.606837606837607]
Average feat diff: [6.299145299145299]
Average noise diff: [6.299145299145299]
Average mAP: [0.7216155571573514]
AUC: 0.8702032617981452
AUC_ind: [0.9061273026545127 0.896795808780826 ]
nDCG: [0.9583144656919748]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [441, 246] samples with noise 20.0
Average adj diff: [4.988662131519274]
Average feat diff: [8.639455782312925]
Average noise diff: [8.639455782312925]
Average mAP: [0.654857825670291]
AUC: 0.8416888414682557
AUC_ind: [0.8726612783278617 0.8696846086020392]
nDCG: [0.9462278324054924]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [397, 290] samples with noise 25.0
Average adj diff: [6.30478589420655]
Average feat diff: [11.163727959697733]
Average noise diff: [11.163727959697733]
Average mAP: [0.6172781417461866]
AUC: 0.8219626706870607
AUC_ind: [0.8580550727857533 0.8443500276312033]
nDCG: [0.9367961081641742]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [378, 309] samples with noise 30.0
Average adj diff: [7.9523809523809526]
Average feat diff: [14.206349206349206]
Average noise diff: [14.206349206349206]
Average mAP: [0.5453533877730403]
AUC: 0.7889495218779378
AUC_ind: [0.8167155314766893 0.8221465146225845]
nDCG: [0.9232071027625728]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9802944096872129 0.9578782701130795], [636, 51]
0.1, [0.9453039318859295 0.927864647209597 ], [543, 144]
0.15, [0.9061273026545127 0.896795808780826 ], [468, 219]
0.2, [0.8726612783278617 0.8696846086020392], [441, 246]
0.25, [0.8580550727857533 0.8443500276312033], [397, 290]
0.3, [0.8167155314766893 0.8221465146225845], [378, 309]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566680654741622
 pos diff: [-0.018529517768598888  0.                  ], inv diff: [0.9512605871002053 0.                ], topk inv diff: [0.7093897339992301 0.                ]
 Variance: 0.4716781089233646
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587212862153611
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [618, 69] samples with noise 5.0
Average adj diff: [0.8964401294498382]
Average feat diff: [1.4368932038834952]
Average noise diff: [1.4368932038834952]
Average mAP: [0.9108824300888414]
AUC: 0.9345487753798944
AUC_ind: [0.9793498112695102 0.9384051943214731]
nDCG: [0.9884374391602812]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [549, 138] samples with noise 10.0
Average adj diff: [2.1839708561020035]
Average feat diff: [3.9234972677595628]
Average noise diff: [3.9234972677595628]
Average mAP: [0.807249368776742]
AUC: 0.9057028824852713
AUC_ind: [0.9411839165978001 0.9190509882266035]
nDCG: [0.9706014067715513]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [481, 206] samples with noise 15.0
Average adj diff: [3.627858627858628]
Average feat diff: [6.108108108108108]
Average noise diff: [6.108108108108108]
Average mAP: [0.7343797076421612]
AUC: 0.8745341745439182
AUC_ind: [0.9074357834531509 0.9024013407418745]
nDCG: [0.9585484699171841]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [439, 248] samples with noise 20.0
Average adj diff: [5.034168564920273]
Average feat diff: [8.633257403189067]
Average noise diff: [8.633257403189067]
Average mAP: [0.6612598361114759]
AUC: 0.8428652428982726
AUC_ind: [0.8800644699089668 0.8706334486003985]
nDCG: [0.9462575865384494]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [387, 300] samples with noise 25.0
Average adj diff: [6.1937984496124034]
Average feat diff: [11.369509043927648]
Average noise diff: [11.369509043927648]
Average mAP: [0.6116057952576961]
AUC: 0.8230714925860456
AUC_ind: [0.8561184365001876 0.8393931709536421]
nDCG: [0.9340815982070564]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [401, 286] samples with noise 30.0
Average adj diff: [7.6458852867830425]
Average feat diff: [14.114713216957606]
Average noise diff: [14.114713216957606]
Average mAP: [0.5647395753910319]
AUC: 0.7934048827707235
AUC_ind: [0.829771545638255  0.8156185685585974]
nDCG: [0.9236140388427497]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9793498112695102 0.9384051943214731], [618, 69]
0.1, [0.9411839165978001 0.9190509882266035], [549, 138]
0.15, [0.9074357834531509 0.9024013407418745], [481, 206]
0.2, [0.8800644699089668 0.8706334486003985], [439, 248]
0.25, [0.8561184365001876 0.8393931709536421], [387, 300]
0.3, [0.829771545638255  0.8156185685585974], [401, 286]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541818
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409403617055
 pos diff: [-0.017950994479083597  0.                  ], inv diff: [0.9510571631281657 0.                ], topk inv diff: [0.698303516948761 0.               ]
 Variance: 0.4701135551046446
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9585968789201633
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [631, 56] samples with noise 5.0
Average adj diff: [0.786053882725832]
Average feat diff: [1.565768621236133]
Average noise diff: [1.565768621236133]
Average mAP: [0.9060884766709606]
AUC: 0.936278510317668
AUC_ind: [0.974906364563736  0.9610720975113171]
nDCG: [0.9875475050223906]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [556, 131] samples with noise 10.0
Average adj diff: [2.303956834532374]
Average feat diff: [3.985611510791367]
Average noise diff: [3.985611510791367]
Average mAP: [0.8068619468403266]
AUC: 0.903230857472296
AUC_ind: [0.9414657496007495 0.9240136795910568]
nDCG: [0.9709811690073671]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [467, 220] samples with noise 15.0
Average adj diff: [3.612419700214133]
Average feat diff: [6.214132762312634]
Average noise diff: [6.214132762312634]
Average mAP: [0.7297412057158891]
AUC: 0.873873561159748
AUC_ind: [0.9149391133303856 0.8986725575528794]
nDCG: [0.9592403369788591]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [448, 239] samples with noise 20.0
Average adj diff: [4.917410714285714]
Average feat diff: [8.348214285714286]
Average noise diff: [8.348214285714286]
Average mAP: [0.6635331557945496]
AUC: 0.8477332749453671
AUC_ind: [0.8833271670312185 0.8630176114981155]
nDCG: [0.9456638182811865]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [404, 283] samples with noise 25.0
Average adj diff: [6.188118811881188]
Average feat diff: [11.242574257425742]
Average noise diff: [11.242574257425742]
Average mAP: [0.6072165271802653]
AUC: 0.8181380356168422
AUC_ind: [0.8519752607409408 0.843397319444073 ]
nDCG: [0.9345873311185244]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [383, 304] samples with noise 30.0
Average adj diff: [7.315926892950392]
Average feat diff: [14.240208877284596]
Average noise diff: [14.240208877284596]
Average mAP: [0.5564353422846405]
AUC: 0.7945802840051777
AUC_ind: [0.817786591931813 0.82970628900039 ]
nDCG: [0.9228022011272639]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.974906364563736  0.9610720975113171], [631, 56]
0.1, [0.9414657496007495 0.9240136795910568], [556, 131]
0.15, [0.9149391133303856 0.8986725575528794], [467, 220]
0.2, [0.8833271670312185 0.8630176114981155], [448, 239]
0.25, [0.8519752607409408 0.843397319444073 ], [404, 283]
0.3, [0.817786591931813 0.82970628900039 ], [383, 304]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1919186515458776
 pos diff: [-0.01797054688482826  0.                 ], inv diff: [0.9513489541094723 0.                ], topk inv diff: [0.6961142249416229 0.                ]
 Variance: 0.4720040143183668
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9578071579633387
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [631, 56] samples with noise 5.0
Average adj diff: [0.8938193343898574]
Average feat diff: [1.4167987321711568]
Average noise diff: [1.4167987321711568]
Average mAP: [0.9095268679222982]
AUC: 0.9356751075739433
AUC_ind: [0.9793499432507022 0.9518894514706128]
nDCG: [0.98898051791014]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [534, 153] samples with noise 10.0
Average adj diff: [2.3220973782771535]
Average feat diff: [4.007490636704119]
Average noise diff: [4.007490636704119]
Average mAP: [0.8111642736538065]
AUC: 0.9024378654304854
AUC_ind: [0.9430002632866561 0.9250665345035949]
nDCG: [0.9730122854681137]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [500, 187] samples with noise 15.0
Average adj diff: [3.76]
Average feat diff: [6.1]
Average noise diff: [6.1]
Average mAP: [0.7343846099594931]
AUC: 0.8739615356855721
AUC_ind: [0.91118078757791   0.9002956436281296]
nDCG: [0.9600023023256581]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [428, 259] samples with noise 20.0
Average adj diff: [5.018691588785047]
Average feat diff: [8.514018691588785]
Average noise diff: [8.514018691588785]
Average mAP: [0.6587810007787471]
AUC: 0.8459117400755891
AUC_ind: [0.8787058805144319 0.8686600150226292]
nDCG: [0.9456469925053913]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [414, 273] samples with noise 25.0
Average adj diff: [6.550724637681159]
Average feat diff: [11.468599033816425]
Average noise diff: [11.468599033816425]
Average mAP: [0.6090287069304375]
AUC: 0.8193226507921035
AUC_ind: [0.8516576530946514 0.8480686420059584]
nDCG: [0.9349361058139676]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [385, 302] samples with noise 30.0
Average adj diff: [7.602597402597403]
Average feat diff: [13.61038961038961]
Average noise diff: [13.61038961038961]
Average mAP: [0.5613284796938811]
AUC: 0.7934410345965124
AUC_ind: [0.8267383649141129 0.821691639940161 ]
nDCG: [0.9210424748080861]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9793499432507022 0.9518894514706128], [631, 56]
0.1, [0.9430002632866561 0.9250665345035949], [534, 153]
0.15, [0.91118078757791   0.9002956436281296], [500, 187]
0.2, [0.8787058805144319 0.8686600150226292], [428, 259]
0.25, [0.8516576530946514 0.8480686420059584], [414, 273]
0.3, [0.8267383649141129 0.821691639940161 ], [385, 302]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1956668065474162
 pos diff: [-0.018529517768598888  0.                  ], inv diff: [0.9512605871002053 0.                ], topk inv diff: [0.7093897339992301 0.                ]
 Variance: 0.4716781089233636
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587212862153611
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [626, 61] samples with noise 5.0
Average adj diff: [0.9009584664536742]
Average feat diff: [1.4920127795527156]
Average noise diff: [1.4920127795527156]
Average mAP: [0.9141600954240909]
AUC: 0.9371270892769219
AUC_ind: [0.9801623595799064 0.9507418230171932]
nDCG: [0.9886265799994]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [543, 144] samples with noise 10.0
Average adj diff: [2.3112338858195214]
Average feat diff: [3.6832412523020257]
Average noise diff: [3.6832412523020257]
Average mAP: [0.8214541122505377]
AUC: 0.9069073483825512
AUC_ind: [0.9470871355992447 0.9233991809317028]
nDCG: [0.9739204136452091]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [499, 188] samples with noise 15.0
Average adj diff: [3.67935871743487]
Average feat diff: [6.152304609218437]
Average noise diff: [6.152304609218437]
Average mAP: [0.7350717422546927]
AUC: 0.8751028900607136
AUC_ind: [0.9159337496668415 0.8908870090699205]
nDCG: [0.9587683154992203]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [443, 244] samples with noise 20.0
Average adj diff: [5.067720090293454]
Average feat diff: [8.654627539503386]
Average noise diff: [8.654627539503386]
Average mAP: [0.6598018108539894]
AUC: 0.8474333153782968
AUC_ind: [0.88741131364478  0.861042055389664]
nDCG: [0.945320357291039]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 25.0
Average adj diff: [6.1725888324873095]
Average feat diff: [11.436548223350254]
Average noise diff: [11.436548223350254]
Average mAP: [0.6042103196593415]
AUC: 0.817498431327766
AUC_ind: [0.8420692295343862 0.8476303454722071]
nDCG: [0.9343512326564803]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [406, 281] samples with noise 30.0
Average adj diff: [7.642857142857143]
Average feat diff: [14.12807881773399]
Average noise diff: [14.12807881773399]
Average mAP: [0.5602186462450824]
AUC: 0.7942926655032245
AUC_ind: [0.8209656548810584 0.8232811534892549]
nDCG: [0.9245736325387075]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9801623595799064 0.9507418230171932], [626, 61]
0.1, [0.9470871355992447 0.9233991809317028], [543, 144]
0.15, [0.9159337496668415 0.8908870090699205], [499, 188]
0.2, [0.88741131364478  0.861042055389664], [443, 244]
0.25, [0.8420692295343862 0.8476303454722071], [394, 293]
0.3, [0.8209656548810584 0.8232811534892549], [406, 281]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541818
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409403617017
 pos diff: [-0.017950994479083597  0.                  ], inv diff: [0.9510571631281657 0.                ], topk inv diff: [0.698303516948761 0.               ]
 Variance: 0.470113555104645
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9585968789201633
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [624, 63] samples with noise 5.0
Average adj diff: [0.9166666666666666]
Average feat diff: [1.3365384615384615]
Average noise diff: [1.3365384615384615]
Average mAP: [0.9252157039028442]
AUC: 0.9388889141861443
AUC_ind: [0.982649836638057  0.9481538499281368]
nDCG: [0.989371783722331]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [533, 154] samples with noise 10.0
Average adj diff: [2.292682926829268]
Average feat diff: [3.7898686679174483]
Average noise diff: [3.7898686679174483]
Average mAP: [0.805346412718223]
AUC: 0.904423191022047
AUC_ind: [0.9465055344523884 0.9183084932111315]
nDCG: [0.9727885583172523]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [503, 184] samples with noise 15.0
Average adj diff: [3.542743538767396]
Average feat diff: [6.10337972166998]
Average noise diff: [6.10337972166998]
Average mAP: [0.7358511671659816]
AUC: 0.8786118371397436
AUC_ind: [0.9117891497068775 0.9023460836580416]
nDCG: [0.9606282294026478]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [426, 261] samples with noise 20.0
Average adj diff: [4.967136150234742]
Average feat diff: [8.830985915492958]
Average noise diff: [8.830985915492958]
Average mAP: [0.6638721929961324]
AUC: 0.8495225573701982
AUC_ind: [0.8822061500101658 0.8750618086091969]
nDCG: [0.9450905569435961]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [392, 295] samples with noise 25.0
Average adj diff: [6.678571428571429]
Average feat diff: [11.826530612244898]
Average noise diff: [11.826530612244898]
Average mAP: [0.5906290938691031]
AUC: 0.8094455638895413
AUC_ind: [0.8432150400891297 0.83627450071338  ]
nDCG: [0.9311609693389763]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [380, 307] samples with noise 30.0
Average adj diff: [7.626315789473685]
Average feat diff: [14.2]
Average noise diff: [14.2]
Average mAP: [0.5505511738562168]
AUC: 0.7981316129765347
AUC_ind: [0.827191107618819  0.8233772651500929]
nDCG: [0.9225994310408823]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.982649836638057  0.9481538499281368], [624, 63]
0.1, [0.9465055344523884 0.9183084932111315], [533, 154]
0.15, [0.9117891497068775 0.9023460836580416], [503, 184]
0.2, [0.8822061500101658 0.8750618086091969], [426, 261]
0.25, [0.8432150400891297 0.83627450071338  ], [392, 295]
0.3, [0.827191107618819  0.8233772651500929], [380, 307]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865154587767
 pos diff: [-0.01797054688482826  0.                 ], inv diff: [0.9513489541094723 0.                ], topk inv diff: [0.6961142249416229 0.                ]
 Variance: 0.4720040143183668
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9578071579633387
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [637, 50] samples with noise 5.0
Average adj diff: [0.8979591836734694]
Average feat diff: [1.3751962323390894]
Average noise diff: [1.3751962323390894]
Average mAP: [0.9146230567349359]
AUC: 0.9364070823392365
AUC_ind: [0.9780726358406499 0.9418579926953903]
nDCG: [0.9886907556219569]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [538, 149] samples with noise 10.0
Average adj diff: [2.2769516728624537]
Average feat diff: [3.7881040892193307]
Average noise diff: [3.7881040892193307]
Average mAP: [0.8015992093576545]
AUC: 0.9040043830162178
AUC_ind: [0.9417074709397132 0.9156492248478744]
nDCG: [0.9710082474450272]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [468, 219] samples with noise 15.0
Average adj diff: [3.606837606837607]
Average feat diff: [6.2094017094017095]
Average noise diff: [6.2094017094017095]
Average mAP: [0.7284600554578838]
AUC: 0.869893068568907
AUC_ind: [0.9156538322798063 0.8891957191098042]
nDCG: [0.9596377351823775]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [417, 270] samples with noise 20.0
Average adj diff: [4.9928057553956835]
Average feat diff: [8.75779376498801]
Average noise diff: [8.75779376498801]
Average mAP: [0.6568082110060623]
AUC: 0.8393254359720597
AUC_ind: [0.883069793844856  0.8618775730548895]
nDCG: [0.9421852102733486]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [393, 294] samples with noise 25.0
Average adj diff: [6.073791348600509]
Average feat diff: [11.287531806615776]
Average noise diff: [11.287531806615776]
Average mAP: [0.606658052104261]
AUC: 0.8177878029182685
AUC_ind: [0.8555686335285267 0.8331108101728597]
nDCG: [0.9346448809282726]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [368, 319] samples with noise 30.0
Average adj diff: [7.635869565217392]
Average feat diff: [13.61413043478261]
Average noise diff: [13.61413043478261]
Average mAP: [0.565085332210237]
AUC: 0.7949890646519859
AUC_ind: [0.829649540166544  0.8170745039030066]
nDCG: [0.9249054365405193]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9780726358406499 0.9418579926953903], [637, 50]
0.1, [0.9417074709397132 0.9156492248478744], [538, 149]
0.15, [0.9156538322798063 0.8891957191098042], [468, 219]
0.2, [0.883069793844856  0.8618775730548895], [417, 270]
0.25, [0.8555686335285267 0.8331108101728597], [393, 294]
0.3, [0.829649540166544  0.8170745039030066], [368, 319]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1956668065474161
 pos diff: [-0.018529517768598888  0.                  ], inv diff: [0.9512605871002053 0.                ], topk inv diff: [0.7093897339992301 0.                ]
 Variance: 0.4716781089233643
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587212862153611
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [627, 60] samples with noise 5.0
Average adj diff: [0.9122807017543859]
Average feat diff: [1.5598086124401913]
Average noise diff: [1.5598086124401913]
Average mAP: [0.9110791068006774]
AUC: 0.9348245971326072
AUC_ind: [0.9804276088600992 0.9400177919339449]
nDCG: [0.9879371330141772]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [551, 136] samples with noise 10.0
Average adj diff: [2.3430127041742286]
Average feat diff: [3.6696914700544463]
Average noise diff: [3.6696914700544463]
Average mAP: [0.8201361059099523]
AUC: 0.906686928225388
AUC_ind: [0.9474051203956951 0.9199196492337074]
nDCG: [0.9731400005024717]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [464, 223] samples with noise 15.0
Average adj diff: [3.7025862068965516]
Average feat diff: [6.219827586206897]
Average noise diff: [6.219827586206897]
Average mAP: [0.7271349677858107]
AUC: 0.874828464126262
AUC_ind: [0.9144569754550559 0.8954294293571874]
nDCG: [0.957797545268632]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [402, 285] samples with noise 20.0
Average adj diff: [4.985074626865671]
Average feat diff: [8.621890547263682]
Average noise diff: [8.621890547263682]
Average mAP: [0.6556018972246335]
AUC: 0.8407312087475112
AUC_ind: [0.8716665741357377 0.8666258913822087]
nDCG: [0.9452798772370571]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [395, 292] samples with noise 25.0
Average adj diff: [6.379746835443038]
Average feat diff: [11.362025316455696]
Average noise diff: [11.362025316455696]
Average mAP: [0.6065380857250452]
AUC: 0.8205714168706901
AUC_ind: [0.8511125709178675 0.840652617096958 ]
nDCG: [0.9325722713948044]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [387, 300] samples with noise 30.0
Average adj diff: [7.656330749354005]
Average feat diff: [13.710594315245478]
Average noise diff: [13.710594315245478]
Average mAP: [0.5522902010733907]
AUC: 0.7859192931409102
AUC_ind: [0.8316925696980716 0.7998138318194528]
nDCG: [0.9202547822014123]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9804276088600992 0.9400177919339449], [627, 60]
0.1, [0.9474051203956951 0.9199196492337074], [551, 136]
0.15, [0.9144569754550559 0.8954294293571874], [464, 223]
0.2, [0.8716665741357377 0.8666258913822087], [402, 285]
0.25, [0.8511125709178675 0.840652617096958 ], [395, 292]
0.3, [0.8316925696980716 0.7998138318194528], [387, 300]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541818
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1957240940361703
 pos diff: [-0.017950994479083597  0.                  ], inv diff: [0.9510571631281657 0.                ], topk inv diff: [0.698303516948761 0.               ]
 Variance: 0.470113555104645
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9585968789201633
AUC_ind: [1. 0.]
nDCG: [0.9999999999999999]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [632, 55] samples with noise 5.0
Average adj diff: [0.8670886075949367]
Average feat diff: [1.5981012658227849]
Average noise diff: [1.5981012658227849]
Average mAP: [0.9042462931670848]
AUC: 0.9348579924091569
AUC_ind: [0.977159933762706  0.9374807747205897]
nDCG: [0.9874425481467364]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [530, 157] samples with noise 10.0
Average adj diff: [2.2132075471698114]
Average feat diff: [3.7433962264150944]
Average noise diff: [3.7433962264150944]
Average mAP: [0.8130940832156962]
AUC: 0.9064146878169246
AUC_ind: [0.9475846592004219 0.9123463141604179]
nDCG: [0.9730026177592184]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [477, 210] samples with noise 15.0
Average adj diff: [3.6121593291404612]
Average feat diff: [6.088050314465409]
Average noise diff: [6.088050314465409]
Average mAP: [0.7378304155122578]
AUC: 0.8757325410349609
AUC_ind: [0.9110368678589391 0.9030777910198055]
nDCG: [0.9589618832022061]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [430, 257] samples with noise 20.0
Average adj diff: [4.841860465116279]
Average feat diff: [8.581395348837209]
Average noise diff: [8.581395348837209]
Average mAP: [0.6654224292111037]
AUC: 0.8460409406554794
AUC_ind: [0.8853204427049541 0.8640515985718484]
nDCG: [0.946395522738476]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [408, 279] samples with noise 25.0
Average adj diff: [6.377450980392157]
Average feat diff: [11.514705882352942]
Average noise diff: [11.514705882352942]
Average mAP: [0.6194815887600588]
AUC: 0.8242515117802394
AUC_ind: [0.8554664925829885 0.8467932676141345]
nDCG: [0.9365318138056182]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [395, 292] samples with noise 30.0
Average adj diff: [7.620253164556962]
Average feat diff: [14.364556962025317]
Average noise diff: [14.364556962025317]
Average mAP: [0.5598442319465872]
AUC: 0.7877294868653464
AUC_ind: [0.8202205186035884 0.8210690729971787]
nDCG: [0.9232627427382459]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.977159933762706  0.9374807747205897], [632, 55]
0.1, [0.9475846592004219 0.9123463141604179], [530, 157]
0.15, [0.9110368678589391 0.9030777910198055], [477, 210]
0.2, [0.8853204427049541 0.8640515985718484], [430, 257]
0.25, [0.8554664925829885 0.8467932676141345], [408, 279]
0.3, [0.8202205186035884 0.8210690729971787], [395, 292]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545522
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522441
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [622, 65] samples with noise 5.0
Average adj diff: [0.8697749196141479]
Average feat diff: [1.5369774919614148]
Average noise diff: [1.5369774919614148]
Average mAP: [0.9090078299883484]
AUC: 0.9329170397156104
AUC_ind: [0.9783163990819405 0.9352978736156867]
nDCG: [0.9883467220591452]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [548, 139] samples with noise 10.0
Average adj diff: [2.2244525547445257]
Average feat diff: [3.7664233576642334]
Average noise diff: [3.7664233576642334]
Average mAP: [0.8079832677991842]
AUC: 0.9036186559783639
AUC_ind: [0.946561563570446  0.9136313225374606]
nDCG: [0.9720566185027706]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [496, 191] samples with noise 15.0
Average adj diff: [3.618951612903226]
Average feat diff: [6.05241935483871]
Average noise diff: [6.05241935483871]
Average mAP: [0.7251935218365076]
AUC: 0.872773436549206
AUC_ind: [0.90961639288576   0.8964077191572604]
nDCG: [0.957231358249646]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [454, 233] samples with noise 20.0
Average adj diff: [4.6982378854625555]
Average feat diff: [8.753303964757709]
Average noise diff: [8.753303964757709]
Average mAP: [0.6574380042030918]
AUC: 0.8459093074060433
AUC_ind: [0.8773677413597311 0.8700137184081627]
nDCG: [0.9466818867255105]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 25.0
Average adj diff: [6.48984771573604]
Average feat diff: [11.67005076142132]
Average noise diff: [11.67005076142132]
Average mAP: [0.5900671973246252]
AUC: 0.8108931632727048
AUC_ind: [0.8416859029981641 0.8346842496564327]
nDCG: [0.9326101226982153]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [389, 298] samples with noise 30.0
Average adj diff: [7.984575835475578]
Average feat diff: [14.287917737789202]
Average noise diff: [14.287917737789202]
Average mAP: [0.5615153027634675]
AUC: 0.792606494484702
AUC_ind: [0.8256606999069571 0.8180910435437291]
nDCG: [0.9244993356575217]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9783163990819405 0.9352978736156867], [622, 65]
0.1, [0.946561563570446  0.9136313225374606], [548, 139]
0.15, [0.90961639288576   0.8964077191572604], [496, 191]
0.2, [0.8773677413597311 0.8700137184081627], [454, 233]
0.25, [0.8416859029981641 0.8346842496564327], [394, 293]
0.3, [0.8256606999069571 0.8180910435437291], [389, 298]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504137
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362937
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [628, 59] samples with noise 5.0
Average adj diff: [0.9585987261146497]
Average feat diff: [1.4171974522292994]
Average noise diff: [1.4171974522292994]
Average mAP: [0.9164926761334141]
AUC: 0.937114874047472
AUC_ind: [0.9803604994417706 0.9379732866426463]
nDCG: [0.9894115079724921]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [536, 151] samples with noise 10.0
Average adj diff: [2.128731343283582]
Average feat diff: [3.705223880597015]
Average noise diff: [3.705223880597015]
Average mAP: [0.8231595797819973]
AUC: 0.9051844446951889
AUC_ind: [0.9449236506685394 0.9279916381789617]
nDCG: [0.9735663034803638]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [472, 215] samples with noise 15.0
Average adj diff: [3.6186440677966103]
Average feat diff: [6.305084745762712]
Average noise diff: [6.305084745762712]
Average mAP: [0.7300931763538454]
AUC: 0.8737255167392491
AUC_ind: [0.9109631736163513 0.8942189731818457]
nDCG: [0.9587971479875158]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [454, 233] samples with noise 20.0
Average adj diff: [4.936123348017621]
Average feat diff: [9.022026431718063]
Average noise diff: [9.022026431718063]
Average mAP: [0.6564273652205711]
AUC: 0.8462702444054805
AUC_ind: [0.8838688458121685 0.8698731547821013]
nDCG: [0.9439233492711698]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [405, 282] samples with noise 25.0
Average adj diff: [6.498765432098765]
Average feat diff: [11.298765432098765]
Average noise diff: [11.298765432098765]
Average mAP: [0.5904622767714229]
AUC: 0.8090444359402652
AUC_ind: [0.8425354295007578 0.8305016196662501]
nDCG: [0.9310565732755118]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [361, 326] samples with noise 30.0
Average adj diff: [7.229916897506925]
Average feat diff: [13.833795013850416]
Average noise diff: [13.833795013850416]
Average mAP: [0.5625130114125955]
AUC: 0.7939008361035615
AUC_ind: [0.8216964448734416 0.8266700787650244]
nDCG: [0.9238845498221665]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9803604994417706 0.9379732866426463], [628, 59]
0.1, [0.9449236506685394 0.9279916381789617], [536, 151]
0.15, [0.9109631736163513 0.8942189731818457], [472, 215]
0.2, [0.8838688458121685 0.8698731547821013], [454, 233]
0.25, [0.8425354295007578 0.8305016196662501], [405, 282]
0.3, [0.8216964448734416 0.8266700787650244], [361, 326]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1956668101010738
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403587
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [635, 52] samples with noise 5.0
Average adj diff: [0.9212598425196851]
Average feat diff: [1.4740157480314962]
Average noise diff: [1.4740157480314962]
Average mAP: [0.918246153608856]
AUC: 0.9357854642930465
AUC_ind: [0.979572464909667  0.9552587100516831]
nDCG: [0.9890167430074678]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [537, 150] samples with noise 10.0
Average adj diff: [2.2942271880819365]
Average feat diff: [3.8808193668528865]
Average noise diff: [3.8808193668528865]
Average mAP: [0.8130211100149821]
AUC: 0.9075358317504933
AUC_ind: [0.9440012559134462 0.930035888826052 ]
nDCG: [0.9728830205174105]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [476, 211] samples with noise 15.0
Average adj diff: [3.6638655462184873]
Average feat diff: [6.239495798319328]
Average noise diff: [6.239495798319328]
Average mAP: [0.7333426565714599]
AUC: 0.8748146883431382
AUC_ind: [0.9136901533558461 0.896869201381416 ]
nDCG: [0.9581513267813143]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [444, 243] samples with noise 20.0
Average adj diff: [4.898648648648648]
Average feat diff: [8.364864864864865]
Average noise diff: [8.364864864864865]
Average mAP: [0.6629520492933024]
AUC: 0.8454721255907225
AUC_ind: [0.8754153052155598 0.863476649503744 ]
nDCG: [0.944423789830039]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [405, 282] samples with noise 25.0
Average adj diff: [6.592592592592593]
Average feat diff: [11.214814814814815]
Average noise diff: [11.214814814814815]
Average mAP: [0.602132874342941]
AUC: 0.8201176562807482
AUC_ind: [0.8562767549859471 0.8400666461995855]
nDCG: [0.9351162681995923]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [405, 282] samples with noise 30.0
Average adj diff: [7.696296296296296]
Average feat diff: [13.693827160493827]
Average noise diff: [13.693827160493827]
Average mAP: [0.565342376870198]
AUC: 0.7976826605312822
AUC_ind: [0.834725553876262  0.8144482735814836]
nDCG: [0.9261240430890586]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.979572464909667  0.9552587100516831], [635, 52]
0.1, [0.9440012559134462 0.930035888826052 ], [537, 150]
0.15, [0.9136901533558461 0.896869201381416 ], [476, 211]
0.2, [0.8754153052155598 0.863476649503744 ], [444, 243]
0.25, [0.8562767549859471 0.8400666461995855], [405, 282]
0.3, [0.834725553876262  0.8144482735814836], [405, 282]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545522
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522439
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [624, 63] samples with noise 5.0
Average adj diff: [0.8942307692307693]
Average feat diff: [1.3846153846153846]
Average noise diff: [1.3846153846153846]
Average mAP: [0.9132022413096785]
AUC: 0.9360990996972489
AUC_ind: [0.9811035692157956 0.9386652871605896]
nDCG: [0.9894708942877889]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [555, 132] samples with noise 10.0
Average adj diff: [2.353153153153153]
Average feat diff: [3.7045045045045044]
Average noise diff: [3.7045045045045044]
Average mAP: [0.8058404703946502]
AUC: 0.9057682474731469
AUC_ind: [0.9445611641223586 0.9130693561429469]
nDCG: [0.9721353012283976]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [489, 198] samples with noise 15.0
Average adj diff: [3.6646216768916156]
Average feat diff: [6.408997955010225]
Average noise diff: [6.408997955010225]
Average mAP: [0.7306807147656278]
AUC: 0.8750516418278331
AUC_ind: [0.9113585720938504 0.8978938069432313]
nDCG: [0.9575246182340017]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [445, 242] samples with noise 20.0
Average adj diff: [4.919101123595506]
Average feat diff: [8.70561797752809]
Average noise diff: [8.70561797752809]
Average mAP: [0.6720205333972565]
AUC: 0.8485743308388543
AUC_ind: [0.8890789885038078 0.867613656118661 ]
nDCG: [0.9460895737583543]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 25.0
Average adj diff: [6.187817258883249]
Average feat diff: [10.934010152284264]
Average noise diff: [10.934010152284264]
Average mAP: [0.6137124247868567]
AUC: 0.8219859253245145
AUC_ind: [0.8460445855789515 0.858904643360355 ]
nDCG: [0.9357079900634659]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [384, 303] samples with noise 30.0
Average adj diff: [7.700520833333333]
Average feat diff: [13.880208333333334]
Average noise diff: [13.880208333333334]
Average mAP: [0.5591491915308644]
AUC: 0.7970902834363474
AUC_ind: [0.8353776712415236 0.8127470026697781]
nDCG: [0.9235686153587445]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9811035692157956 0.9386652871605896], [624, 63]
0.1, [0.9445611641223586 0.9130693561429469], [555, 132]
0.15, [0.9113585720938504 0.8978938069432313], [489, 198]
0.2, [0.8890789885038078 0.867613656118661 ], [445, 242]
0.25, [0.8460445855789515 0.858904643360355 ], [394, 293]
0.3, [0.8353776712415236 0.8127470026697781], [384, 303]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504173
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629353
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [618, 69] samples with noise 5.0
Average adj diff: [0.8915857605177994]
Average feat diff: [1.3948220064724919]
Average noise diff: [1.3948220064724919]
Average mAP: [0.9165331210646142]
AUC: 0.9370792331279094
AUC_ind: [0.978965076194044  0.9528162127603216]
nDCG: [0.9893138258078027]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [528, 159] samples with noise 10.0
Average adj diff: [2.1704545454545454]
Average feat diff: [3.984848484848485]
Average noise diff: [3.984848484848485]
Average mAP: [0.8018221377112982]
AUC: 0.9020677868066984
AUC_ind: [0.9434451728679593 0.9200381364477568]
nDCG: [0.971413380820844]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [462, 225] samples with noise 15.0
Average adj diff: [3.445887445887446]
Average feat diff: [6.333333333333333]
Average noise diff: [6.333333333333333]
Average mAP: [0.7293201734546177]
AUC: 0.8730507974464625
AUC_ind: [0.9086186396148836 0.8979113740965395]
nDCG: [0.9580782042220544]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [470, 217] samples with noise 20.0
Average adj diff: [5.225531914893617]
Average feat diff: [9.063829787234043]
Average noise diff: [9.063829787234043]
Average mAP: [0.6627952834899786]
AUC: 0.8460457955502226
AUC_ind: [0.880853032614996  0.8756855080535685]
nDCG: [0.945838770027518]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [392, 295] samples with noise 25.0
Average adj diff: [6.186224489795919]
Average feat diff: [11.255102040816327]
Average noise diff: [11.255102040816327]
Average mAP: [0.605705825882339]
AUC: 0.8173218691256545
AUC_ind: [0.8431378443814541 0.8496912965746182]
nDCG: [0.9326308363092364]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [385, 302] samples with noise 30.0
Average adj diff: [7.55064935064935]
Average feat diff: [13.761038961038961]
Average noise diff: [13.761038961038961]
Average mAP: [0.5358200268463778]
AUC: 0.7870422128315226
AUC_ind: [0.8172902238765077 0.8131903519288377]
nDCG: [0.9208189736981534]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.978965076194044  0.9528162127603216], [618, 69]
0.1, [0.9434451728679593 0.9200381364477568], [528, 159]
0.15, [0.9086186396148836 0.8979113740965395], [462, 225]
0.2, [0.880853032614996  0.8756855080535685], [470, 217]
0.25, [0.8431378443814541 0.8496912965746182], [392, 295]
0.3, [0.8172902238765077 0.8131903519288377], [385, 302]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107365
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403526
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [630, 57] samples with noise 5.0
Average adj diff: [0.9047619047619048]
Average feat diff: [1.4412698412698413]
Average noise diff: [1.4412698412698413]
Average mAP: [0.9117620404708271]
AUC: 0.9359462036053485
AUC_ind: [0.9789436625582165 0.9440627062567298]
nDCG: [0.9891197634741492]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [536, 151] samples with noise 10.0
Average adj diff: [2.3824626865671643]
Average feat diff: [3.9029850746268657]
Average noise diff: [3.9029850746268657]
Average mAP: [0.8027717735470845]
AUC: 0.9047344137306843
AUC_ind: [0.938879213602458  0.9221866927268387]
nDCG: [0.9695996340102542]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [481, 206] samples with noise 15.0
Average adj diff: [3.577962577962578]
Average feat diff: [6.137214137214137]
Average noise diff: [6.137214137214137]
Average mAP: [0.7375276729488792]
AUC: 0.8779556430157645
AUC_ind: [0.9134772724898214 0.8976218387066991]
nDCG: [0.9576348237347881]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [437, 250] samples with noise 20.0
Average adj diff: [4.997711670480549]
Average feat diff: [8.837528604118994]
Average noise diff: [8.837528604118994]
Average mAP: [0.6643301943892603]
AUC: 0.8471291771449665
AUC_ind: [0.884605304536738  0.8678141914625368]
nDCG: [0.9449494129585314]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [395, 292] samples with noise 25.0
Average adj diff: [6.189873417721519]
Average feat diff: [11.331645569620253]
Average noise diff: [11.331645569620253]
Average mAP: [0.6139120681184183]
AUC: 0.8239935094320594
AUC_ind: [0.8551668477490714 0.8476587500028794]
nDCG: [0.9368491882576712]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [396, 291] samples with noise 30.0
Average adj diff: [7.5227272727272725]
Average feat diff: [14.696969696969697]
Average noise diff: [14.696969696969697]
Average mAP: [0.5488278792008416]
AUC: 0.7899405047725436
AUC_ind: [0.8223651430246108 0.8184037004203247]
nDCG: [0.9218716839955067]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9789436625582165 0.9440627062567298], [630, 57]
0.1, [0.938879213602458  0.9221866927268387], [536, 151]
0.15, [0.9134772724898214 0.8976218387066991], [481, 206]
0.2, [0.884605304536738  0.8678141914625368], [437, 250]
0.25, [0.8551668477490714 0.8476587500028794], [395, 292]
0.3, [0.8223651430246108 0.8184037004203247], [396, 291]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545497
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522436
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [632, 55] samples with noise 5.0
Average adj diff: [0.9003164556962026]
Average feat diff: [1.3544303797468353]
Average noise diff: [1.3544303797468353]
Average mAP: [0.9218549617130402]
AUC: 0.9379220609150518
AUC_ind: [0.9804917877056637 0.9497717326268493]
nDCG: [0.9895279062735951]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [517, 170] samples with noise 10.0
Average adj diff: [2.2321083172147]
Average feat diff: [3.872340425531915]
Average noise diff: [3.872340425531915]
Average mAP: [0.8069003427087149]
AUC: 0.9026452018199218
AUC_ind: [0.9438524198477279 0.917618866347115 ]
nDCG: [0.972011006015935]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [479, 208] samples with noise 15.0
Average adj diff: [3.4676409185803756]
Average feat diff: [6.050104384133611]
Average noise diff: [6.050104384133611]
Average mAP: [0.7405200037446023]
AUC: 0.877732440097901
AUC_ind: [0.9158222108284227 0.9003829888233563]
nDCG: [0.9586478910750779]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [427, 260] samples with noise 20.0
Average adj diff: [4.960187353629976]
Average feat diff: [9.091334894613583]
Average noise diff: [9.091334894613583]
Average mAP: [0.6594083409844345]
AUC: 0.8488240344216003
AUC_ind: [0.8823634216793954 0.8708748109698066]
nDCG: [0.9460676890878342]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [425, 262] samples with noise 25.0
Average adj diff: [6.437647058823529]
Average feat diff: [11.52]
Average noise diff: [11.52]
Average mAP: [0.6016876048050233]
AUC: 0.817934461029205
AUC_ind: [0.8543498155392447 0.8376880204589798]
nDCG: [0.9327753680934877]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 30.0
Average adj diff: [8.104060913705585]
Average feat diff: [14.395939086294415]
Average noise diff: [14.395939086294415]
Average mAP: [0.5539925062881517]
AUC: 0.7901760105165072
AUC_ind: [0.8294475346571106 0.8117096848541884]
nDCG: [0.922346060250339]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9804917877056637 0.9497717326268493], [632, 55]
0.1, [0.9438524198477279 0.917618866347115 ], [517, 170]
0.15, [0.9158222108284227 0.9003829888233563], [479, 208]
0.2, [0.8823634216793954 0.8708748109698066], [427, 260]
0.25, [0.8543498155392447 0.8376880204589798], [425, 262]
0.3, [0.8294475346571106 0.8117096848541884], [394, 293]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504148
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629353
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [625, 62] samples with noise 5.0
Average adj diff: [0.872]
Average feat diff: [1.5136]
Average noise diff: [1.5136]
Average mAP: [0.9090019273956569]
AUC: 0.9351435971292636
AUC_ind: [0.9771890950147526 0.9379894838556802]
nDCG: [0.9879108046298306]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [546, 141] samples with noise 10.0
Average adj diff: [2.4615384615384617]
Average feat diff: [3.6373626373626373]
Average noise diff: [3.6373626373626373]
Average mAP: [0.8111862680235402]
AUC: 0.9060862796490923
AUC_ind: [0.944840756474632  0.9232234662566386]
nDCG: [0.9734220452347435]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [457, 230] samples with noise 15.0
Average adj diff: [3.5076586433260393]
Average feat diff: [6.179431072210066]
Average noise diff: [6.179431072210066]
Average mAP: [0.7285958466448758]
AUC: 0.8741192962249895
AUC_ind: [0.9163624792172652 0.8861300036734447]
nDCG: [0.9594514573372399]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [423, 264] samples with noise 20.0
Average adj diff: [4.862884160756501]
Average feat diff: [8.898345153664303]
Average noise diff: [8.898345153664303]
Average mAP: [0.6588236114444604]
AUC: 0.8453875811884151
AUC_ind: [0.8822439050647908 0.8656168052451361]
nDCG: [0.9439359030177005]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [396, 291] samples with noise 25.0
Average adj diff: [6.1313131313131315]
Average feat diff: [11.297979797979798]
Average noise diff: [11.297979797979798]
Average mAP: [0.5863869548298228]
AUC: 0.8084846339198508
AUC_ind: [0.8470875470436625 0.8256553481244486]
nDCG: [0.9310252180768477]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [381, 306] samples with noise 30.0
Average adj diff: [7.698162729658793]
Average feat diff: [14.225721784776903]
Average noise diff: [14.225721784776903]
Average mAP: [0.5490660466738116]
AUC: 0.7886277704613548
AUC_ind: [0.8242113385431871 0.813288017024985 ]
nDCG: [0.9256042529295349]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9771890950147526 0.9379894838556802], [625, 62]
0.1, [0.944840756474632  0.9232234662566386], [546, 141]
0.15, [0.9163624792172652 0.8861300036734447], [457, 230]
0.2, [0.8822439050647908 0.8656168052451361], [423, 264]
0.25, [0.8470875470436625 0.8256553481244486], [396, 291]
0.3, [0.8242113385431871 0.813288017024985 ], [381, 306]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107373
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403476
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [614, 73] samples with noise 5.0
Average adj diff: [0.8371335504885994]
Average feat diff: [1.498371335504886]
Average noise diff: [1.498371335504886]
Average mAP: [0.9195668354883756]
AUC: 0.9362745684807012
AUC_ind: [0.9801851869549235 0.9578454568841556]
nDCG: [0.9887712539564497]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [552, 135] samples with noise 10.0
Average adj diff: [2.346014492753623]
Average feat diff: [3.829710144927536]
Average noise diff: [3.829710144927536]
Average mAP: [0.8204941173914938]
AUC: 0.9090898093510682
AUC_ind: [0.9486608716488331 0.9192025549521521]
nDCG: [0.9731416395307916]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [469, 218] samples with noise 15.0
Average adj diff: [3.7078891257995736]
Average feat diff: [6.460554371002132]
Average noise diff: [6.460554371002132]
Average mAP: [0.7237896753965133]
AUC: 0.8732164692732769
AUC_ind: [0.9060821965133589 0.901216506335796 ]
nDCG: [0.9573892274834841]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [432, 255] samples with noise 20.0
Average adj diff: [4.847222222222222]
Average feat diff: [8.472222222222221]
Average noise diff: [8.472222222222221]
Average mAP: [0.6652370572832002]
AUC: 0.8504744316475309
AUC_ind: [0.8879301787948821 0.8581011962173231]
nDCG: [0.9449885338273347]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [426, 261] samples with noise 25.0
Average adj diff: [6.633802816901408]
Average feat diff: [11.671361502347418]
Average noise diff: [11.671361502347418]
Average mAP: [0.6027583962491276]
AUC: 0.8197351953629936
AUC_ind: [0.8535315313189888 0.8438451978676181]
nDCG: [0.9337243336441058]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [404, 283] samples with noise 30.0
Average adj diff: [7.76980198019802]
Average feat diff: [13.386138613861386]
Average noise diff: [13.386138613861386]
Average mAP: [0.5570449625960037]
AUC: 0.794023921837058
AUC_ind: [0.8329981827552473 0.8075967494743532]
nDCG: [0.923054238212825]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9801851869549235 0.9578454568841556], [614, 73]
0.1, [0.9486608716488331 0.9192025549521521], [552, 135]
0.15, [0.9060821965133589 0.901216506335796 ], [469, 218]
0.2, [0.8879301787948821 0.8581011962173231], [432, 255]
0.25, [0.8535315313189888 0.8438451978676181], [426, 261]
0.3, [0.8329981827552473 0.8075967494743532], [404, 283]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545517
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522449
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [631, 56] samples with noise 5.0
Average adj diff: [0.8256735340729001]
Average feat diff: [1.6006339144215531]
Average noise diff: [1.6006339144215531]
Average mAP: [0.9131050749232321]
AUC: 0.9361450763984067
AUC_ind: [0.9771961298270976 0.9630253312518683]
nDCG: [0.988147370463784]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [533, 154] samples with noise 10.0
Average adj diff: [2.397748592870544]
Average feat diff: [3.9362101313320825]
Average noise diff: [3.9362101313320825]
Average mAP: [0.8003925762289902]
AUC: 0.8998952630023913
AUC_ind: [0.9404713242169702 0.9180258521761724]
nDCG: [0.9721802255025458]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [497, 190] samples with noise 15.0
Average adj diff: [3.5593561368209254]
Average feat diff: [6.193158953722334]
Average noise diff: [6.193158953722334]
Average mAP: [0.7350840535005605]
AUC: 0.8771310516610245
AUC_ind: [0.917751125380487  0.8918403194703546]
nDCG: [0.9594063347953431]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [423, 264] samples with noise 20.0
Average adj diff: [4.947990543735225]
Average feat diff: [8.76595744680851]
Average noise diff: [8.76595744680851]
Average mAP: [0.6608387333542087]
AUC: 0.8465861773228153
AUC_ind: [0.8817824898892813 0.8726462466998955]
nDCG: [0.9470818644059064]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [403, 284] samples with noise 25.0
Average adj diff: [6.312655086848635]
Average feat diff: [11.682382133995038]
Average noise diff: [11.682382133995038]
Average mAP: [0.597120569529415]
AUC: 0.8144017125927919
AUC_ind: [0.852214327236226  0.8323309337409007]
nDCG: [0.9310051546316942]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [420, 267] samples with noise 30.0
Average adj diff: [7.435714285714286]
Average feat diff: [13.628571428571428]
Average noise diff: [13.628571428571428]
Average mAP: [0.5671479128653675]
AUC: 0.7937059170857806
AUC_ind: [0.8292304956063352 0.8217802424037098]
nDCG: [0.9259121188100922]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9771961298270976 0.9630253312518683], [631, 56]
0.1, [0.9404713242169702 0.9180258521761724], [533, 154]
0.15, [0.917751125380487  0.8918403194703546], [497, 190]
0.2, [0.8817824898892813 0.8726462466998955], [423, 264]
0.25, [0.852214327236226  0.8323309337409007], [403, 284]
0.3, [0.8292304956063352 0.8217802424037098], [420, 267]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504154
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362936
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [619, 68] samples with noise 5.0
Average adj diff: [0.9159935379644588]
Average feat diff: [1.3731825525040389]
Average noise diff: [1.3731825525040389]
Average mAP: [0.909978707398856]
AUC: 0.93645591383653
AUC_ind: [0.9789090721427982 0.9409987650140748]
nDCG: [0.9881737304938033]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [529, 158] samples with noise 10.0
Average adj diff: [2.264650283553875]
Average feat diff: [3.931947069943289]
Average noise diff: [3.931947069943289]
Average mAP: [0.804274637588773]
AUC: 0.899979235813567
AUC_ind: [0.9389261824897662 0.9230906173515424]
nDCG: [0.9710477354634024]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [481, 206] samples with noise 15.0
Average adj diff: [3.638253638253638]
Average feat diff: [6.241164241164241]
Average noise diff: [6.241164241164241]
Average mAP: [0.7287228711285071]
AUC: 0.8724390059176999
AUC_ind: [0.9100305113987464 0.899681661193818 ]
nDCG: [0.9593943079555713]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [412, 275] samples with noise 20.0
Average adj diff: [4.842233009708738]
Average feat diff: [8.563106796116505]
Average noise diff: [8.563106796116505]
Average mAP: [0.6564359854539824]
AUC: 0.8421750046499851
AUC_ind: [0.880972277304511  0.8579657546117159]
nDCG: [0.9447728713834888]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [396, 291] samples with noise 25.0
Average adj diff: [6.492424242424242]
Average feat diff: [11.318181818181818]
Average noise diff: [11.318181818181818]
Average mAP: [0.6054130079839314]
AUC: 0.8151004364217735
AUC_ind: [0.8508986942912659 0.8401482223902629]
nDCG: [0.9326568667132982]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [385, 302] samples with noise 30.0
Average adj diff: [7.5064935064935066]
Average feat diff: [13.932467532467532]
Average noise diff: [13.932467532467532]
Average mAP: [0.556983150226068]
AUC: 0.7920371750543548
AUC_ind: [0.8242127256954914 0.8097797100770288]
nDCG: [0.923885935236056]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9789090721427982 0.9409987650140748], [619, 68]
0.1, [0.9389261824897662 0.9230906173515424], [529, 158]
0.15, [0.9100305113987464 0.899681661193818 ], [481, 206]
0.2, [0.880972277304511  0.8579657546117159], [412, 275]
0.25, [0.8508986942912659 0.8401482223902629], [396, 291]
0.3, [0.8242127256954914 0.8097797100770288], [385, 302]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1956668101010737
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403554
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [635, 52] samples with noise 5.0
Average adj diff: [0.9070866141732283]
Average feat diff: [1.5590551181102361]
Average noise diff: [1.5590551181102361]
Average mAP: [0.9197335112438229]
AUC: 0.9378447497657041
AUC_ind: [0.9797596003537083 0.9551731225652773]
nDCG: [0.9884319128274013]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [512, 175] samples with noise 10.0
Average adj diff: [2.22265625]
Average feat diff: [3.67578125]
Average noise diff: [3.67578125]
Average mAP: [0.8113287547981864]
AUC: 0.9064655943136604
AUC_ind: [0.9468720033418946 0.921088295963204 ]
nDCG: [0.9721887163224211]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [488, 199] samples with noise 15.0
Average adj diff: [3.5204918032786887]
Average feat diff: [6.233606557377049]
Average noise diff: [6.233606557377049]
Average mAP: [0.7455683183946548]
AUC: 0.880468672756585
AUC_ind: [0.9173905632447474 0.8977181951735824]
nDCG: [0.9594274840908843]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [431, 256] samples with noise 20.0
Average adj diff: [5.0116009280742455]
Average feat diff: [8.77030162412993]
Average noise diff: [8.77030162412993]
Average mAP: [0.6572960166690841]
AUC: 0.8433873902281743
AUC_ind: [0.8749291773877471 0.8610346502931077]
nDCG: [0.9429209422185627]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [438, 249] samples with noise 25.0
Average adj diff: [6.3812785388127855]
Average feat diff: [11.401826484018265]
Average noise diff: [11.401826484018265]
Average mAP: [0.6090579100787107]
AUC: 0.8210870197827849
AUC_ind: [0.8537549037161367 0.839062447308739 ]
nDCG: [0.9344356214310081]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [404, 283] samples with noise 30.0
Average adj diff: [7.670792079207921]
Average feat diff: [13.871287128712872]
Average noise diff: [13.871287128712872]
Average mAP: [0.5564591474079932]
AUC: 0.7939424567651616
AUC_ind: [0.8251714753969073 0.8195244054034285]
nDCG: [0.9240928438759825]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9797596003537083 0.9551731225652773], [635, 52]
0.1, [0.9468720033418946 0.921088295963204 ], [512, 175]
0.15, [0.9173905632447474 0.8977181951735824], [488, 199]
0.2, [0.8749291773877471 0.8610346502931077], [431, 256]
0.25, [0.8537549037161367 0.839062447308739 ], [438, 249]
0.3, [0.8251714753969073 0.8195244054034285], [404, 283]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1919186524154553
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.47200400975224394
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [622, 65] samples with noise 5.0
Average adj diff: [0.8504823151125402]
Average feat diff: [1.3215434083601285]
Average noise diff: [1.3215434083601285]
Average mAP: [0.9148781067510119]
AUC: 0.9364266889720011
AUC_ind: [0.979330607735422  0.9516132576298589]
nDCG: [0.9886799970751906]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [525, 162] samples with noise 10.0
Average adj diff: [2.1485714285714286]
Average feat diff: [3.5923809523809522]
Average noise diff: [3.5923809523809522]
Average mAP: [0.8162056614121651]
AUC: 0.9050873598685876
AUC_ind: [0.9476580379302323 0.9160253322786651]
nDCG: [0.9735750563324478]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [488, 199] samples with noise 15.0
Average adj diff: [3.778688524590164]
Average feat diff: [6.159836065573771]
Average noise diff: [6.159836065573771]
Average mAP: [0.7413079259738112]
AUC: 0.8718481422464828
AUC_ind: [0.9170414591983853 0.8913503123339833]
nDCG: [0.9587656924290134]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [450, 237] samples with noise 20.0
Average adj diff: [4.977777777777778]
Average feat diff: [8.795555555555556]
Average noise diff: [8.795555555555556]
Average mAP: [0.664411456196202]
AUC: 0.8500187581803651
AUC_ind: [0.8868086098730359 0.8649169958010556]
nDCG: [0.9452782373850968]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [407, 280] samples with noise 25.0
Average adj diff: [6.486486486486487]
Average feat diff: [11.626535626535626]
Average noise diff: [11.626535626535626]
Average mAP: [0.6006076636356333]
AUC: 0.8151722849933393
AUC_ind: [0.8458076510638726 0.8367956265227016]
nDCG: [0.9344052806512984]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [391, 296] samples with noise 30.0
Average adj diff: [7.549872122762149]
Average feat diff: [13.9846547314578]
Average noise diff: [13.9846547314578]
Average mAP: [0.5449102437561396]
AUC: 0.7878543172435536
AUC_ind: [0.8163047581028218 0.8176737161594341]
nDCG: [0.92282656524123]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.979330607735422  0.9516132576298589], [622, 65]
0.1, [0.9476580379302323 0.9160253322786651], [525, 162]
0.15, [0.9170414591983853 0.8913503123339833], [488, 199]
0.2, [0.8868086098730359 0.8649169958010556], [450, 237]
0.25, [0.8458076510638726 0.8367956265227016], [407, 280]
0.3, [0.8163047581028218 0.8176737161594341], [391, 296]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1957240986750413
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629386
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [634, 53] samples with noise 5.0
Average adj diff: [0.8958990536277602]
Average feat diff: [1.5331230283911672]
Average noise diff: [1.5331230283911672]
Average mAP: [0.9139832341109543]
AUC: 0.9364369179438595
AUC_ind: [0.9790354725274358 0.9507299674710248]
nDCG: [0.988324072416379]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [532, 155] samples with noise 10.0
Average adj diff: [2.319548872180451]
Average feat diff: [3.601503759398496]
Average noise diff: [3.601503759398496]
Average mAP: [0.8108632003114279]
AUC: 0.902425564743049
AUC_ind: [0.944371280170786  0.9150628909706668]
nDCG: [0.9723920694678796]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [467, 220] samples with noise 15.0
Average adj diff: [3.605995717344754]
Average feat diff: [6.154175588865097]
Average noise diff: [6.154175588865097]
Average mAP: [0.7335321115953478]
AUC: 0.8772942464649123
AUC_ind: [0.9174307478869185 0.8954798489719654]
nDCG: [0.960639128030964]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [435, 252] samples with noise 20.0
Average adj diff: [4.933333333333334]
Average feat diff: [8.72183908045977]
Average noise diff: [8.72183908045977]
Average mAP: [0.6639315771715298]
AUC: 0.8429604935880639
AUC_ind: [0.8790045891434423 0.8663887925341148]
nDCG: [0.9460480258742449]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [398, 289] samples with noise 25.0
Average adj diff: [6.201005025125628]
Average feat diff: [10.959798994974875]
Average noise diff: [10.959798994974875]
Average mAP: [0.6009716467134008]
AUC: 0.8188855690446188
AUC_ind: [0.851915189130481  0.8452502286532422]
nDCG: [0.9337738253934275]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [403, 284] samples with noise 30.0
Average adj diff: [7.535980148883374]
Average feat diff: [14.148883374689825]
Average noise diff: [14.148883374689825]
Average mAP: [0.5643121257334962]
AUC: 0.7961732134569324
AUC_ind: [0.8235644645899872 0.8238480384982373]
nDCG: [0.9246196129319585]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9790354725274358 0.9507299674710248], [634, 53]
0.1, [0.944371280170786  0.9150628909706668], [532, 155]
0.15, [0.9174307478869185 0.8954798489719654], [467, 220]
0.2, [0.8790045891434423 0.8663887925341148], [435, 252]
0.25, [0.851915189130481  0.8452502286532422], [398, 289]
0.3, [0.8235644645899872 0.8238480384982373], [403, 284]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107373
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403576
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [622, 65] samples with noise 5.0
Average adj diff: [0.8922829581993569]
Average feat diff: [1.3536977491961415]
Average noise diff: [1.3536977491961415]
Average mAP: [0.9216753920313374]
AUC: 0.9384248145070707
AUC_ind: [0.9813974341425891 0.9543645432482708]
nDCG: [0.9900743239449604]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [551, 136] samples with noise 10.0
Average adj diff: [2.3430127041742286]
Average feat diff: [3.6406533575317606]
Average noise diff: [3.6406533575317606]
Average mAP: [0.8147808018882764]
AUC: 0.9078057080395009
AUC_ind: [0.9470343233302406 0.9207933061516445]
nDCG: [0.9744596020934589]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [490, 197] samples with noise 15.0
Average adj diff: [3.557142857142857]
Average feat diff: [6.289795918367347]
Average noise diff: [6.289795918367347]
Average mAP: [0.7384055104532107]
AUC: 0.8769870086965785
AUC_ind: [0.9085277602652889 0.8991252917255419]
nDCG: [0.9590503536653722]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [431, 256] samples with noise 20.0
Average adj diff: [5.067285382830627]
Average feat diff: [9.31322505800464]
Average noise diff: [9.31322505800464]
Average mAP: [0.6613392474862919]
AUC: 0.8463824557486808
AUC_ind: [0.8823055386977225 0.8663970994197784]
nDCG: [0.9435000641165308]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [415, 272] samples with noise 25.0
Average adj diff: [6.63132530120482]
Average feat diff: [11.257831325301204]
Average noise diff: [11.257831325301204]
Average mAP: [0.6074534809892844]
AUC: 0.8215188823011695
AUC_ind: [0.8487097569077399 0.8498470298672967]
nDCG: [0.9339332195327148]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [384, 303] samples with noise 30.0
Average adj diff: [7.7265625]
Average feat diff: [13.567708333333334]
Average noise diff: [13.567708333333334]
Average mAP: [0.5648435440405553]
AUC: 0.7985082147609939
AUC_ind: [0.8343637125368667 0.8204430189812311]
nDCG: [0.9257397143054369]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9813974341425891 0.9543645432482708], [622, 65]
0.1, [0.9470343233302406 0.9207933061516445], [551, 136]
0.15, [0.9085277602652889 0.8991252917255419], [490, 197]
0.2, [0.8823055386977225 0.8663970994197784], [431, 256]
0.25, [0.8487097569077399 0.8498470298672967], [415, 272]
0.3, [0.8343637125368667 0.8204430189812311], [384, 303]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545508
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522437
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [620, 67] samples with noise 5.0
Average adj diff: [0.853225806451613]
Average feat diff: [1.4967741935483871]
Average noise diff: [1.4967741935483871]
Average mAP: [0.909976888934828]
AUC: 0.9339826856520241
AUC_ind: [0.97937244129216   0.9412397359586204]
nDCG: [0.9879731298343204]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [549, 138] samples with noise 10.0
Average adj diff: [2.1602914389799635]
Average feat diff: [3.737704918032787]
Average noise diff: [3.737704918032787]
Average mAP: [0.8129958810344847]
AUC: 0.9044946623850922
AUC_ind: [0.9422639459017924 0.9295881274540997]
nDCG: [0.9729812601355141]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [479, 208] samples with noise 15.0
Average adj diff: [3.6805845511482254]
Average feat diff: [6.267223382045929]
Average noise diff: [6.267223382045929]
Average mAP: [0.7269730189897492]
AUC: 0.8777564218211759
AUC_ind: [0.9136461541358433 0.8926791234161334]
nDCG: [0.9595117474268949]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [440, 247] samples with noise 20.0
Average adj diff: [5.040909090909091]
Average feat diff: [8.709090909090909]
Average noise diff: [8.709090909090909]
Average mAP: [0.6622404555400302]
AUC: 0.8447907457886276
AUC_ind: [0.8812498851613415 0.8672332704793169]
nDCG: [0.9460116780388682]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [396, 291] samples with noise 25.0
Average adj diff: [6.540404040404041]
Average feat diff: [11.166666666666666]
Average noise diff: [11.166666666666666]
Average mAP: [0.6031029932617004]
AUC: 0.8154098177872885
AUC_ind: [0.8500969010737299 0.8370640667530381]
nDCG: [0.9328086925996151]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [373, 314] samples with noise 30.0
Average adj diff: [7.498659517426273]
Average feat diff: [14.193029490616622]
Average noise diff: [14.193029490616622]
Average mAP: [0.5523629798493634]
AUC: 0.7865873810786489
AUC_ind: [0.8252515151844362 0.8092802591550835]
nDCG: [0.9232025364081174]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.97937244129216   0.9412397359586204], [620, 67]
0.1, [0.9422639459017924 0.9295881274540997], [549, 138]
0.15, [0.9136461541358433 0.8926791234161334], [479, 208]
0.2, [0.8812498851613415 0.8672332704793169], [440, 247]
0.25, [0.8500969010737299 0.8370640667530381], [396, 291]
0.3, [0.8252515151844362 0.8092802591550835], [373, 314]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504129
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362939
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [625, 62] samples with noise 5.0
Average adj diff: [0.8352]
Average feat diff: [1.3888]
Average noise diff: [1.3888]
Average mAP: [0.9154588613206447]
AUC: 0.9379624933923543
AUC_ind: [0.9805838534666358 0.9486539167349988]
nDCG: [0.9891975537898553]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [547, 140] samples with noise 10.0
Average adj diff: [2.340036563071298]
Average feat diff: [4.043875685557587]
Average noise diff: [4.043875685557587]
Average mAP: [0.8067967586354996]
AUC: 0.9022325908474927
AUC_ind: [0.9417026876229547 0.9219934118431675]
nDCG: [0.9728268489482017]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [496, 191] samples with noise 15.0
Average adj diff: [3.475806451612903]
Average feat diff: [6.641129032258065]
Average noise diff: [6.641129032258065]
Average mAP: [0.7221440429253891]
AUC: 0.8722008610272012
AUC_ind: [0.9054047889506094 0.8933775261926026]
nDCG: [0.9567922336229545]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [429, 258] samples with noise 20.0
Average adj diff: [4.589743589743589]
Average feat diff: [8.564102564102564]
Average noise diff: [8.564102564102564]
Average mAP: [0.6734797162145221]
AUC: 0.8503616445500404
AUC_ind: [0.8870818864240512 0.8769295276521158]
nDCG: [0.9470604848626101]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [400, 287] samples with noise 25.0
Average adj diff: [6.31]
Average feat diff: [11.435]
Average noise diff: [11.435]
Average mAP: [0.5969528368650702]
AUC: 0.813353166779378
AUC_ind: [0.8488316906389626 0.8398225179479951]
nDCG: [0.9327676909456805]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [383, 304] samples with noise 30.0
Average adj diff: [7.516971279373368]
Average feat diff: [13.921671018276763]
Average noise diff: [13.921671018276763]
Average mAP: [0.5563269383302442]
AUC: 0.7903017299200271
AUC_ind: [0.8249639626416011 0.8156907376560545]
nDCG: [0.9243909172756167]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9805838534666358 0.9486539167349988], [625, 62]
0.1, [0.9417026876229547 0.9219934118431675], [547, 140]
0.15, [0.9054047889506094 0.8933775261926026], [496, 191]
0.2, [0.8870818864240512 0.8769295276521158], [429, 258]
0.25, [0.8488316906389626 0.8398225179479951], [400, 287]
0.3, [0.8249639626416011 0.8156907376560545], [383, 304]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107387
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403576
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [0.9999999999999999]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [628, 59] samples with noise 5.0
Average adj diff: [0.8423566878980892]
Average feat diff: [1.4681528662420382]
Average noise diff: [1.4681528662420382]
Average mAP: [0.9257690905988369]
AUC: 0.93863775110593
AUC_ind: [0.9807010540847984 0.9599951396555337]
nDCG: [0.9904784464940678]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [527, 160] samples with noise 10.0
Average adj diff: [2.282732447817837]
Average feat diff: [3.8026565464895636]
Average noise diff: [3.8026565464895636]
Average mAP: [0.8128714189194806]
AUC: 0.9068216123748378
AUC_ind: [0.9457068936613489 0.9189491835370311]
nDCG: [0.9717028128168568]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [463, 224] samples with noise 15.0
Average adj diff: [3.5680345572354213]
Average feat diff: [6.198704103671706]
Average noise diff: [6.198704103671706]
Average mAP: [0.7312839503370334]
AUC: 0.8760457755286989
AUC_ind: [0.912645709981221  0.8997949420781344]
nDCG: [0.9605779835958088]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [431, 256] samples with noise 20.0
Average adj diff: [5.208816705336427]
Average feat diff: [8.830626450116009]
Average noise diff: [8.830626450116009]
Average mAP: [0.6612503181257786]
AUC: 0.8444523089997815
AUC_ind: [0.8835380754149478 0.8614002437256851]
nDCG: [0.9459148051155528]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [414, 273] samples with noise 25.0
Average adj diff: [6.084541062801932]
Average feat diff: [11.44927536231884]
Average noise diff: [11.44927536231884]
Average mAP: [0.601323916613025]
AUC: 0.8121399865847317
AUC_ind: [0.84690131478884   0.8325906240939288]
nDCG: [0.9330995313350976]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [399, 288] samples with noise 30.0
Average adj diff: [7.518796992481203]
Average feat diff: [13.709273182957393]
Average noise diff: [13.709273182957393]
Average mAP: [0.5698436433506318]
AUC: 0.7952531054795413
AUC_ind: [0.8329192003048468 0.8136950984508444]
nDCG: [0.9266878647049013]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9807010540847984 0.9599951396555337], [628, 59]
0.1, [0.9457068936613489 0.9189491835370311], [527, 160]
0.15, [0.912645709981221  0.8997949420781344], [463, 224]
0.2, [0.8835380754149478 0.8614002437256851], [431, 256]
0.25, [0.84690131478884   0.8325906240939288], [414, 273]
0.3, [0.8329192003048468 0.8136950984508444], [399, 288]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1919186524154555
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522436
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [620, 67] samples with noise 5.0
Average adj diff: [0.9]
Average feat diff: [1.3838709677419354]
Average noise diff: [1.3838709677419354]
Average mAP: [0.9148340189993228]
AUC: 0.9376065614248554
AUC_ind: [0.9803129610934546 0.9498511717359086]
nDCG: [0.9887413083761173]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [525, 162] samples with noise 10.0
Average adj diff: [2.222857142857143]
Average feat diff: [3.862857142857143]
Average noise diff: [3.862857142857143]
Average mAP: [0.8077571402246378]
AUC: 0.903230049544645
AUC_ind: [0.9447892229926785 0.9308741512910922]
nDCG: [0.9728012012974958]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [485, 202] samples with noise 15.0
Average adj diff: [3.814432989690722]
Average feat diff: [6.268041237113402]
Average noise diff: [6.268041237113402]
Average mAP: [0.7262126742097544]
AUC: 0.8743352722564304
AUC_ind: [0.9118934512456757 0.8977402144620608]
nDCG: [0.9581726660275326]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [443, 244] samples with noise 20.0
Average adj diff: [4.837471783295711]
Average feat diff: [8.609480812641083]
Average noise diff: [8.609480812641083]
Average mAP: [0.658623168068503]
AUC: 0.844527987975587
AUC_ind: [0.8786410717398505 0.8722942062291715]
nDCG: [0.9463785355134767]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [416, 271] samples with noise 25.0
Average adj diff: [6.550480769230769]
Average feat diff: [11.615384615384615]
Average noise diff: [11.615384615384615]
Average mAP: [0.6041424871321479]
AUC: 0.8162841462366704
AUC_ind: [0.8520578340379349 0.8402880068557409]
nDCG: [0.9331783354956049]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [386, 301] samples with noise 30.0
Average adj diff: [7.66321243523316]
Average feat diff: [14.134715025906736]
Average noise diff: [14.134715025906736]
Average mAP: [0.556865183966943]
AUC: 0.7914287231887236
AUC_ind: [0.827147307067205  0.8208037918251677]
nDCG: [0.9222493946319535]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9803129610934546 0.9498511717359086], [620, 67]
0.1, [0.9447892229926785 0.9308741512910922], [525, 162]
0.15, [0.9118934512456757 0.8977402144620608], [485, 202]
0.2, [0.8786410717398505 0.8722942062291715], [443, 244]
0.25, [0.8520578340379349 0.8402880068557409], [416, 271]
0.3, [0.827147307067205  0.8208037918251677], [386, 301]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504145
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629386
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [632, 55] samples with noise 5.0
Average adj diff: [0.8370253164556962]
Average feat diff: [1.4746835443037976]
Average noise diff: [1.4746835443037976]
Average mAP: [0.9204840263698653]
AUC: 0.937893729987979
AUC_ind: [0.9816232974447068 0.9420532936262747]
nDCG: [0.988934813970396]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [536, 151] samples with noise 10.0
Average adj diff: [2.2406716417910446]
Average feat diff: [3.7238805970149254]
Average noise diff: [3.7238805970149254]
Average mAP: [0.8174195478760216]
AUC: 0.904982209071761
AUC_ind: [0.9466670781405477 0.9244684564585157]
nDCG: [0.9734556894248839]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [481, 206] samples with noise 15.0
Average adj diff: [3.525987525987526]
Average feat diff: [6.087318087318088]
Average noise diff: [6.087318087318088]
Average mAP: [0.7262648020592286]
AUC: 0.8807797881083711
AUC_ind: [0.9182115813373811 0.8959247791731327]
nDCG: [0.9586804570608203]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [418, 269] samples with noise 20.0
Average adj diff: [5.07177033492823]
Average feat diff: [8.923444976076555]
Average noise diff: [8.923444976076555]
Average mAP: [0.658028441041989]
AUC: 0.8431850474638101
AUC_ind: [0.8826755360277653 0.8587615155233911]
nDCG: [0.9441510094190966]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [406, 281] samples with noise 25.0
Average adj diff: [6.586206896551724]
Average feat diff: [11.492610837438423]
Average noise diff: [11.492610837438423]
Average mAP: [0.5960937518249473]
AUC: 0.8130169223172036
AUC_ind: [0.8482617680881778 0.834895615354392 ]
nDCG: [0.930734254034488]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [397, 290] samples with noise 30.0
Average adj diff: [7.448362720403023]
Average feat diff: [13.591939546599496]
Average noise diff: [13.591939546599496]
Average mAP: [0.5490791934281796]
AUC: 0.7879987979402789
AUC_ind: [0.8172023339168726 0.8168544936202005]
nDCG: [0.9222309619357861]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9816232974447068 0.9420532936262747], [632, 55]
0.1, [0.9466670781405477 0.9244684564585157], [536, 151]
0.15, [0.9182115813373811 0.8959247791731327], [481, 206]
0.2, [0.8826755360277653 0.8587615155233911], [418, 269]
0.25, [0.8482617680881778 0.834895615354392 ], [406, 281]
0.3, [0.8172023339168726 0.8168544936202005], [397, 290]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107365
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403554
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [614, 73] samples with noise 5.0
Average adj diff: [0.9348534201954397]
Average feat diff: [1.485342019543974]
Average noise diff: [1.485342019543974]
Average mAP: [0.9211162266893869]
AUC: 0.9363440799441549
AUC_ind: [0.9812408968884362 0.9447595659849878]
nDCG: [0.9893931991548639]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [556, 131] samples with noise 10.0
Average adj diff: [2.318345323741007]
Average feat diff: [3.971223021582734]
Average noise diff: [3.971223021582734]
Average mAP: [0.8119820524717412]
AUC: 0.9001315501067454
AUC_ind: [0.9383534413177717 0.9312357469131991]
nDCG: [0.9708076637503015]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [475, 212] samples with noise 15.0
Average adj diff: [3.423157894736842]
Average feat diff: [6.185263157894737]
Average noise diff: [6.185263157894737]
Average mAP: [0.7389931227047933]
AUC: 0.8772264786035902
AUC_ind: [0.9177269623023381 0.8904936920339134]
nDCG: [0.9561914891536593]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [449, 238] samples with noise 20.0
Average adj diff: [5.120267260579064]
Average feat diff: [8.806236080178174]
Average noise diff: [8.806236080178174]
Average mAP: [0.6590548862865339]
AUC: 0.8446452955548056
AUC_ind: [0.8781390845829875 0.8639894057501504]
nDCG: [0.9453453753211932]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [404, 283] samples with noise 25.0
Average adj diff: [6.514851485148514]
Average feat diff: [11.663366336633663]
Average noise diff: [11.663366336633663]
Average mAP: [0.602027422151741]
AUC: 0.8176533370488442
AUC_ind: [0.8516363672940687 0.8426040500705324]
nDCG: [0.9344231726287964]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [392, 295] samples with noise 30.0
Average adj diff: [7.5688775510204085]
Average feat diff: [14.107142857142858]
Average noise diff: [14.107142857142858]
Average mAP: [0.564584315962113]
AUC: 0.7968097711674159
AUC_ind: [0.8312146042968757 0.8114525231249291]
nDCG: [0.922389092659072]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9812408968884362 0.9447595659849878], [614, 73]
0.1, [0.9383534413177717 0.9312357469131991], [556, 131]
0.15, [0.9177269623023381 0.8904936920339134], [475, 212]
0.2, [0.8781390845829875 0.8639894057501504], [449, 238]
0.25, [0.8516363672940687 0.8426040500705324], [404, 283]
0.3, [0.8312146042968757 0.8114525231249291], [392, 295]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545508
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522439
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [629, 58] samples with noise 5.0
Average adj diff: [0.8982511923688394]
Average feat diff: [1.4721780604133545]
Average noise diff: [1.4721780604133545]
Average mAP: [0.9113421340748604]
AUC: 0.9357790967561582
AUC_ind: [0.9780678924666987 0.9505878070689167]
nDCG: [0.9880885932246722]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [523, 164] samples with noise 10.0
Average adj diff: [2.2695984703632885]
Average feat diff: [3.5564053537284894]
Average noise diff: [3.5564053537284894]
Average mAP: [0.8095334618016352]
AUC: 0.9044787173255708
AUC_ind: [0.9447763105280986 0.9215028741309655]
nDCG: [0.9723546954801019]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [481, 206] samples with noise 15.0
Average adj diff: [3.4615384615384617]
Average feat diff: [6.316008316008316]
Average noise diff: [6.316008316008316]
Average mAP: [0.7283174243239396]
AUC: 0.8757732930856957
AUC_ind: [0.9132615168220801 0.8930804587245605]
nDCG: [0.9585253549513657]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [432, 255] samples with noise 20.0
Average adj diff: [5.046296296296297]
Average feat diff: [9.027777777777779]
Average noise diff: [9.027777777777779]
Average mAP: [0.666180941367233]
AUC: 0.8437297244561137
AUC_ind: [0.8734712058141881 0.8783728538913843]
nDCG: [0.9454107843309335]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [407, 280] samples with noise 25.0
Average adj diff: [6.022113022113022]
Average feat diff: [10.668304668304668]
Average noise diff: [10.668304668304668]
Average mAP: [0.6164806999008986]
AUC: 0.8256257773616766
AUC_ind: [0.8624937019838933 0.84767614506129  ]
nDCG: [0.9348699446759559]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [373, 314] samples with noise 30.0
Average adj diff: [7.3083109919571045]
Average feat diff: [12.981233243967829]
Average noise diff: [12.981233243967829]
Average mAP: [0.5555748426183506]
AUC: 0.7899169539397711
AUC_ind: [0.8243335309082677 0.8111285809818414]
nDCG: [0.9261579685471469]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9780678924666987 0.9505878070689167], [629, 58]
0.1, [0.9447763105280986 0.9215028741309655], [523, 164]
0.15, [0.9132615168220801 0.8930804587245605], [481, 206]
0.2, [0.8734712058141881 0.8783728538913843], [432, 255]
0.25, [0.8624937019838933 0.84767614506129  ], [407, 280]
0.3, [0.8243335309082677 0.8111285809818414], [373, 314]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504145
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629336
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [623, 64] samples with noise 5.0
Average adj diff: [0.841091492776886]
Average feat diff: [1.4991974317817014]
Average noise diff: [1.4991974317817014]
Average mAP: [0.9155110483712339]
AUC: 0.9369679309204246
AUC_ind: [0.9795622448463686 0.9527711967281869]
nDCG: [0.9885748858475826]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [544, 143] samples with noise 10.0
Average adj diff: [2.329044117647059]
Average feat diff: [3.827205882352941]
Average noise diff: [3.827205882352941]
Average mAP: [0.8103221743387856]
AUC: 0.9032467973458285
AUC_ind: [0.9439486854314092 0.9241569459501229]
nDCG: [0.9737989373328003]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [480, 207] samples with noise 15.0
Average adj diff: [3.4520833333333334]
Average feat diff: [6.229166666666667]
Average noise diff: [6.229166666666667]
Average mAP: [0.7312136800259388]
AUC: 0.875360983106638
AUC_ind: [0.9104341715779135 0.9028651254359701]
nDCG: [0.9584657130303006]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [421, 266] samples with noise 20.0
Average adj diff: [4.885985748218527]
Average feat diff: [8.660332541567696]
Average noise diff: [8.660332541567696]
Average mAP: [0.6572320865702465]
AUC: 0.8438046224726152
AUC_ind: [0.8738527629056013 0.8714576780326846]
nDCG: [0.9422440024106259]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [404, 283] samples with noise 25.0
Average adj diff: [6.356435643564357]
Average feat diff: [11.351485148514852]
Average noise diff: [11.351485148514852]
Average mAP: [0.6069678339092482]
AUC: 0.815091460299526
AUC_ind: [0.8472882175681011 0.8416959889439631]
nDCG: [0.9319597459037113]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [375, 312] samples with noise 30.0
Average adj diff: [8.074666666666667]
Average feat diff: [14.026666666666667]
Average noise diff: [14.026666666666667]
Average mAP: [0.5527747479846014]
AUC: 0.7855315048851649
AUC_ind: [0.8181578302670144 0.8057572830543689]
nDCG: [0.9233363533910088]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9795622448463686 0.9527711967281869], [623, 64]
0.1, [0.9439486854314092 0.9241569459501229], [544, 143]
0.15, [0.9104341715779135 0.9028651254359701], [480, 207]
0.2, [0.8738527629056013 0.8714576780326846], [421, 266]
0.25, [0.8472882175681011 0.8416959889439631], [404, 283]
0.3, [0.8181578302670144 0.8057572830543689], [375, 312]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107365
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.4716781013140357
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [627, 60] samples with noise 5.0
Average adj diff: [0.8086124401913876]
Average feat diff: [1.4066985645933014]
Average noise diff: [1.4066985645933014]
Average mAP: [0.9195286017078014]
AUC: 0.9397227817610351
AUC_ind: [0.9816399964386984 0.950959231335144 ]
nDCG: [0.9889682980556586]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [567, 120] samples with noise 10.0
Average adj diff: [2.271604938271605]
Average feat diff: [3.932980599647266]
Average noise diff: [3.932980599647266]
Average mAP: [0.8193492944256174]
AUC: 0.9054371323505159
AUC_ind: [0.9441836431616013 0.9228103029262009]
nDCG: [0.9727463779867875]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [495, 192] samples with noise 15.0
Average adj diff: [3.6343434343434344]
Average feat diff: [6.488888888888889]
Average noise diff: [6.488888888888889]
Average mAP: [0.7338846732227662]
AUC: 0.8757459335876153
AUC_ind: [0.9127004163331628 0.8898307362699113]
nDCG: [0.9583000149570606]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [436, 251] samples with noise 20.0
Average adj diff: [5.0389908256880735]
Average feat diff: [9.243119266055047]
Average noise diff: [9.243119266055047]
Average mAP: [0.6601115242183839]
AUC: 0.8394673739921781
AUC_ind: [0.874572165099914  0.8708343102819261]
nDCG: [0.9448771081284484]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [426, 261] samples with noise 25.0
Average adj diff: [6.213615023474178]
Average feat diff: [11.488262910798122]
Average noise diff: [11.488262910798122]
Average mAP: [0.5987365345465373]
AUC: 0.8162872124981461
AUC_ind: [0.8488347241624653 0.8394718886236144]
nDCG: [0.9342934077184517]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [391, 296] samples with noise 30.0
Average adj diff: [7.667519181585678]
Average feat diff: [13.989769820971867]
Average noise diff: [13.989769820971867]
Average mAP: [0.5491794333692614]
AUC: 0.7833438029700301
AUC_ind: [0.8130995779774728 0.8193879026144767]
nDCG: [0.9232915328422984]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9816399964386984 0.950959231335144 ], [627, 60]
0.1, [0.9441836431616013 0.9228103029262009], [567, 120]
0.15, [0.9127004163331628 0.8898307362699113], [495, 192]
0.2, [0.874572165099914  0.8708343102819261], [436, 251]
0.25, [0.8488347241624653 0.8394718886236144], [426, 261]
0.3, [0.8130995779774728 0.8193879026144767], [391, 296]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545522
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.47200400975224394
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [625, 62] samples with noise 5.0
Average adj diff: [0.9136]
Average feat diff: [1.5328]
Average noise diff: [1.5328]
Average mAP: [0.9137996015432196]
AUC: 0.9353974103379468
AUC_ind: [0.9793431538614917 0.9509466396868905]
nDCG: [0.9877151474649032]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [507, 180] samples with noise 10.0
Average adj diff: [2.250493096646943]
Average feat diff: [3.727810650887574]
Average noise diff: [3.727810650887574]
Average mAP: [0.812807375983676]
AUC: 0.9044125747729113
AUC_ind: [0.9469487032249104 0.9219037115796941]
nDCG: [0.9734875433119128]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [468, 219] samples with noise 15.0
Average adj diff: [3.547008547008547]
Average feat diff: [6.055555555555555]
Average noise diff: [6.055555555555555]
Average mAP: [0.7346396810287714]
AUC: 0.8766331997505059
AUC_ind: [0.9162075813102205 0.8972965793105696]
nDCG: [0.9599671044616404]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [419, 268] samples with noise 20.0
Average adj diff: [4.873508353221957]
Average feat diff: [8.505966587112171]
Average noise diff: [8.505966587112171]
Average mAP: [0.6575758885248831]
AUC: 0.8435664672245592
AUC_ind: [0.8809973694228175 0.860359069176973 ]
nDCG: [0.9456870507001878]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [416, 271] samples with noise 25.0
Average adj diff: [6.259615384615385]
Average feat diff: [11.625]
Average noise diff: [11.625]
Average mAP: [0.5919762472962363]
AUC: 0.8158703558519994
AUC_ind: [0.8405225928883198 0.8386041268148767]
nDCG: [0.9337977983964318]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [384, 303] samples with noise 30.0
Average adj diff: [8.075520833333334]
Average feat diff: [14.145833333333334]
Average noise diff: [14.145833333333334]
Average mAP: [0.5660995270163239]
AUC: 0.7981832642300508
AUC_ind: [0.8293277507388769 0.8212800602922345]
nDCG: [0.9239762198767781]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9793431538614917 0.9509466396868905], [625, 62]
0.1, [0.9469487032249104 0.9219037115796941], [507, 180]
0.15, [0.9162075813102205 0.8972965793105696], [468, 219]
0.2, [0.8809973694228175 0.860359069176973 ], [419, 268]
0.25, [0.8405225928883198 0.8386041268148767], [416, 271]
0.3, [0.8293277507388769 0.8212800602922345], [384, 303]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504137
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629425
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [612, 75] samples with noise 5.0
Average adj diff: [0.9444444444444444]
Average feat diff: [1.4640522875816993]
Average noise diff: [1.4640522875816993]
Average mAP: [0.910760204959585]
AUC: 0.9339874970569801
AUC_ind: [0.9792627629692429 0.9359746035468026]
nDCG: [0.988306802430403]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [549, 138] samples with noise 10.0
Average adj diff: [2.242258652094718]
Average feat diff: [3.602914389799636]
Average noise diff: [3.602914389799636]
Average mAP: [0.8114354713256411]
AUC: 0.9062329665124411
AUC_ind: [0.94238278778115   0.9304350864807661]
nDCG: [0.9729367672313916]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [480, 207] samples with noise 15.0
Average adj diff: [3.5479166666666666]
Average feat diff: [6.166666666666667]
Average noise diff: [6.166666666666667]
Average mAP: [0.7279989593339746]
AUC: 0.8748006748123738
AUC_ind: [0.9150297672490555 0.8879942885405114]
nDCG: [0.9591448685155812]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [436, 251] samples with noise 20.0
Average adj diff: [4.841743119266055]
Average feat diff: [8.642201834862385]
Average noise diff: [8.642201834862385]
Average mAP: [0.6506258650021217]
AUC: 0.846779813993152
AUC_ind: [0.8812686323772777 0.8636747548508835]
nDCG: [0.946293548002257]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [413, 274] samples with noise 25.0
Average adj diff: [6.2058111380145276]
Average feat diff: [11.564164648910412]
Average noise diff: [11.564164648910412]
Average mAP: [0.597663199201564]
AUC: 0.8124328006701502
AUC_ind: [0.8467964563335546 0.8339251311058364]
nDCG: [0.931671928718306]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [395, 292] samples with noise 30.0
Average adj diff: [7.50379746835443]
Average feat diff: [13.51392405063291]
Average noise diff: [13.51392405063291]
Average mAP: [0.5616722435761253]
AUC: 0.7969677362755228
AUC_ind: [0.8317058465893282 0.8107230338789254]
nDCG: [0.9254498068297197]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9792627629692429 0.9359746035468026], [612, 75]
0.1, [0.94238278778115   0.9304350864807661], [549, 138]
0.15, [0.9150297672490555 0.8879942885405114], [480, 207]
0.2, [0.8812686323772777 0.8636747548508835], [436, 251]
0.25, [0.8467964563335546 0.8339251311058364], [413, 274]
0.3, [0.8317058465893282 0.8107230338789254], [395, 292]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107378
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403526
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [629, 58] samples with noise 5.0
Average adj diff: [0.8950715421303657]
Average feat diff: [1.4880763116057234]
Average noise diff: [1.4880763116057234]
Average mAP: [0.925197887586393]
AUC: 0.9381401593357971
AUC_ind: [0.9811376351867468 0.9377098536712761]
nDCG: [0.9902362675785438]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [541, 146] samples with noise 10.0
Average adj diff: [2.2181146025878005]
Average feat diff: [3.6968576709796674]
Average noise diff: [3.6968576709796674]
Average mAP: [0.818505725113975]
AUC: 0.9046576168125147
AUC_ind: [0.9468115930403759 0.9162363171341062]
nDCG: [0.9732245952744722]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [479, 208] samples with noise 15.0
Average adj diff: [3.578288100208768]
Average feat diff: [6.38830897703549]
Average noise diff: [6.38830897703549]
Average mAP: [0.7321299575845622]
AUC: 0.8729139367341263
AUC_ind: [0.9109965885831325 0.8889093995864622]
nDCG: [0.9585222057524531]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [453, 234] samples with noise 20.0
Average adj diff: [5.216335540838852]
Average feat diff: [8.975717439293598]
Average noise diff: [8.975717439293598]
Average mAP: [0.6724161399779959]
AUC: 0.8481568649642027
AUC_ind: [0.8792355603581284 0.8778640287176771]
nDCG: [0.9461221091144543]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [403, 284] samples with noise 25.0
Average adj diff: [6.290322580645161]
Average feat diff: [10.764267990074442]
Average noise diff: [10.764267990074442]
Average mAP: [0.6164996625447926]
AUC: 0.8217880676879896
AUC_ind: [0.8552779674552401 0.8461609236049212]
nDCG: [0.936292747122982]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [401, 286] samples with noise 30.0
Average adj diff: [7.882793017456359]
Average feat diff: [14.443890274314214]
Average noise diff: [14.443890274314214]
Average mAP: [0.5603755228837549]
AUC: 0.7950115711059084
AUC_ind: [0.828294011522629  0.8077886488886441]
nDCG: [0.9228552022333603]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9811376351867468 0.9377098536712761], [629, 58]
0.1, [0.9468115930403759 0.9162363171341062], [541, 146]
0.15, [0.9109965885831325 0.8889093995864622], [479, 208]
0.2, [0.8792355603581284 0.8778640287176771], [453, 234]
0.25, [0.8552779674552401 0.8461609236049212], [403, 284]
0.3, [0.828294011522629  0.8077886488886441], [401, 286]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545505
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522439
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [627, 60] samples with noise 5.0
Average adj diff: [0.8596491228070176]
Average feat diff: [1.496012759170654]
Average noise diff: [1.496012759170654]
Average mAP: [0.9153470334753671]
AUC: 0.936294035608797
AUC_ind: [0.979036083217929  0.9560335280240376]
nDCG: [0.9886052515058366]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [543, 144] samples with noise 10.0
Average adj diff: [2.2154696132596685]
Average feat diff: [3.7569060773480665]
Average noise diff: [3.7569060773480665]
Average mAP: [0.8166554093374974]
AUC: 0.9066103008713018
AUC_ind: [0.9447744750617658 0.9348678271968027]
nDCG: [0.9728734637440294]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [463, 224] samples with noise 15.0
Average adj diff: [3.537796976241901]
Average feat diff: [6.27645788336933]
Average noise diff: [6.27645788336933]
Average mAP: [0.7291367021932702]
AUC: 0.8713176754045727
AUC_ind: [0.9141367991569047 0.8944022751551597]
nDCG: [0.9598811616243075]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [439, 248] samples with noise 20.0
Average adj diff: [4.952164009111617]
Average feat diff: [8.701594533029613]
Average noise diff: [8.701594533029613]
Average mAP: [0.6669833208247166]
AUC: 0.846729045813527
AUC_ind: [0.8886174079536747 0.8623849343559502]
nDCG: [0.9481871038701103]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [399, 288] samples with noise 25.0
Average adj diff: [6.040100250626566]
Average feat diff: [11.002506265664161]
Average noise diff: [11.002506265664161]
Average mAP: [0.6054227737503409]
AUC: 0.8173309988243687
AUC_ind: [0.8569578874684604 0.8355515989167048]
nDCG: [0.9329376814513622]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [391, 296] samples with noise 30.0
Average adj diff: [7.427109974424552]
Average feat diff: [13.514066496163682]
Average noise diff: [13.514066496163682]
Average mAP: [0.553766143371182]
AUC: 0.7960451218591282
AUC_ind: [0.8286109286432372 0.8168745087356681]
nDCG: [0.9244792846745065]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.979036083217929  0.9560335280240376], [627, 60]
0.1, [0.9447744750617658 0.9348678271968027], [543, 144]
0.15, [0.9141367991569047 0.8944022751551597], [463, 224]
0.2, [0.8886174079536747 0.8623849343559502], [439, 248]
0.25, [0.8569578874684604 0.8355515989167048], [399, 288]
0.3, [0.8286109286432372 0.8168745087356681], [391, 296]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504154
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362939
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [629, 58] samples with noise 5.0
Average adj diff: [0.904610492845787]
Average feat diff: [1.411764705882353]
Average noise diff: [1.411764705882353]
Average mAP: [0.915329912831154]
AUC: 0.9368906905089835
AUC_ind: [0.9795247885306391 0.9432550242312094]
nDCG: [0.9886290874274308]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [548, 139] samples with noise 10.0
Average adj diff: [2.366788321167883]
Average feat diff: [3.510948905109489]
Average noise diff: [3.510948905109489]
Average mAP: [0.810800641799768]
AUC: 0.9061482341417763
AUC_ind: [0.9466764810907838 0.9183340740938114]
nDCG: [0.9737070115879848]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [472, 215] samples with noise 15.0
Average adj diff: [3.559322033898305]
Average feat diff: [6.2076271186440675]
Average noise diff: [6.2076271186440675]
Average mAP: [0.7271206099010998]
AUC: 0.8728660380403033
AUC_ind: [0.9119869149849832 0.887624677815586 ]
nDCG: [0.9590235237878774]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [461, 226] samples with noise 20.0
Average adj diff: [5.080260303687636]
Average feat diff: [9.227765726681127]
Average noise diff: [9.227765726681127]
Average mAP: [0.6476997009815723]
AUC: 0.8388458303313828
AUC_ind: [0.8755976157581178 0.8586633086953466]
nDCG: [0.9442871699554697]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [426, 261] samples with noise 25.0
Average adj diff: [6.302816901408451]
Average feat diff: [10.967136150234742]
Average noise diff: [10.967136150234742]
Average mAP: [0.5931231676410856]
AUC: 0.8137368939731889
AUC_ind: [0.8517691848955887 0.8217463393996757]
nDCG: [0.9326472908118328]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [376, 311] samples with noise 30.0
Average adj diff: [7.3138297872340425]
Average feat diff: [13.303191489361701]
Average noise diff: [13.303191489361701]
Average mAP: [0.5663419150886523]
AUC: 0.7990495764468367
AUC_ind: [0.8216134377754939 0.8294236975724669]
nDCG: [0.9257325544495878]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9795247885306391 0.9432550242312094], [629, 58]
0.1, [0.9466764810907838 0.9183340740938114], [548, 139]
0.15, [0.9119869149849832 0.887624677815586 ], [472, 215]
0.2, [0.8755976157581178 0.8586633086953466], [461, 226]
0.25, [0.8517691848955887 0.8217463393996757], [426, 261]
0.3, [0.8216134377754939 0.8294236975724669], [376, 311]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107387
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403537
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [629, 58] samples with noise 5.0
Average adj diff: [0.8728139904610492]
Average feat diff: [1.507154213036566]
Average noise diff: [1.507154213036566]
Average mAP: [0.9198224722915437]
AUC: 0.9367716703674173
AUC_ind: [0.9800602887192896 0.9457145646457015]
nDCG: [0.9888091662639488]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [532, 155] samples with noise 10.0
Average adj diff: [2.325187969924812]
Average feat diff: [4.067669172932331]
Average noise diff: [4.067669172932331]
Average mAP: [0.8065785400282796]
AUC: 0.9028868030844943
AUC_ind: [0.9399003771117369 0.9208843847460507]
nDCG: [0.9719377861815741]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [474, 213] samples with noise 15.0
Average adj diff: [3.5]
Average feat diff: [6.033755274261603]
Average noise diff: [6.033755274261603]
Average mAP: [0.7409826478155533]
AUC: 0.8784778098401027
AUC_ind: [0.917246373632465  0.9074571026564356]
nDCG: [0.9595693853855978]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [420, 267] samples with noise 20.0
Average adj diff: [5.035714285714286]
Average feat diff: [8.952380952380953]
Average noise diff: [8.952380952380953]
Average mAP: [0.6655216586992116]
AUC: 0.8466904317250885
AUC_ind: [0.8810152245101749 0.8798232612691869]
nDCG: [0.9483503446988829]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [415, 272] samples with noise 25.0
Average adj diff: [6.2]
Average feat diff: [11.489156626506023]
Average noise diff: [11.489156626506023]
Average mAP: [0.606180203313325]
AUC: 0.8102768890242925
AUC_ind: [0.846676296292467  0.8375998909467897]
nDCG: [0.9365238388625272]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [393, 294] samples with noise 30.0
Average adj diff: [7.755725190839694]
Average feat diff: [14.127226463104325]
Average noise diff: [14.127226463104325]
Average mAP: [0.5683422951130382]
AUC: 0.8004994277695678
AUC_ind: [0.8336301623293393 0.818600238468689 ]
nDCG: [0.9259916968932399]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9800602887192896 0.9457145646457015], [629, 58]
0.1, [0.9399003771117369 0.9208843847460507], [532, 155]
0.15, [0.917246373632465  0.9074571026564356], [474, 213]
0.2, [0.8810152245101749 0.8798232612691869], [420, 267]
0.25, [0.846676296292467  0.8375998909467897], [415, 272]
0.3, [0.8336301623293393 0.818600238468689 ], [393, 294]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545514
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522444
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [625, 62] samples with noise 5.0
Average adj diff: [0.9136]
Average feat diff: [1.5392]
Average noise diff: [1.5392]
Average mAP: [0.9132284060004338]
AUC: 0.9364215271994236
AUC_ind: [0.9790181943747999 0.9593650462022254]
nDCG: [0.9890225716349815]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [548, 139] samples with noise 10.0
Average adj diff: [2.427007299270073]
Average feat diff: [3.7846715328467155]
Average noise diff: [3.7846715328467155]
Average mAP: [0.8073837104779762]
AUC: 0.9014091023753731
AUC_ind: [0.9442573250238203 0.9198736902534524]
nDCG: [0.9723143968781467]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [476, 211] samples with noise 15.0
Average adj diff: [3.607142857142857]
Average feat diff: [6.063025210084033]
Average noise diff: [6.063025210084033]
Average mAP: [0.7259934626424752]
AUC: 0.8737306161834568
AUC_ind: [0.9161374466023918 0.8940601090473249]
nDCG: [0.9575913319923813]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [441, 246] samples with noise 20.0
Average adj diff: [5.09750566893424]
Average feat diff: [8.988662131519275]
Average noise diff: [8.988662131519275]
Average mAP: [0.6558204088885852]
AUC: 0.8423860309723696
AUC_ind: [0.86493963636429   0.8778665907030279]
nDCG: [0.945314048659601]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [391, 296] samples with noise 25.0
Average adj diff: [6.3478260869565215]
Average feat diff: [11.375959079283888]
Average noise diff: [11.375959079283888]
Average mAP: [0.5952148526401838]
AUC: 0.813325000719329
AUC_ind: [0.8540738481828601 0.8272050979417782]
nDCG: [0.9319643457327912]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [387, 300] samples with noise 30.0
Average adj diff: [7.669250645994832]
Average feat diff: [14.087855297157622]
Average noise diff: [14.087855297157622]
Average mAP: [0.5526818457240921]
AUC: 0.7911323936358319
AUC_ind: [0.8289633314087159 0.8097785224886649]
nDCG: [0.9236315821039552]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9790181943747999 0.9593650462022254], [625, 62]
0.1, [0.9442573250238203 0.9198736902534524], [548, 139]
0.15, [0.9161374466023918 0.8940601090473249], [476, 211]
0.2, [0.86493963636429   0.8778665907030279], [441, 246]
0.25, [0.8540738481828601 0.8272050979417782], [391, 296]
0.3, [0.8289633314087159 0.8097785224886649], [387, 300]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504165
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362937
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [622, 65] samples with noise 5.0
Average adj diff: [0.8681672025723473]
Average feat diff: [1.5369774919614148]
Average noise diff: [1.5369774919614148]
Average mAP: [0.9090787827363724]
AUC: 0.9343415353210679
AUC_ind: [0.9770740798982562 0.9518087982011295]
nDCG: [0.9887025681112597]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [544, 143] samples with noise 10.0
Average adj diff: [2.3125]
Average feat diff: [3.875]
Average noise diff: [3.875]
Average mAP: [0.8057119603840457]
AUC: 0.9025681309212492
AUC_ind: [0.9414081387361849 0.9223348362099003]
nDCG: [0.9710168933998966]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [482, 205] samples with noise 15.0
Average adj diff: [3.5]
Average feat diff: [6.6929460580912865]
Average noise diff: [6.6929460580912865]
Average mAP: [0.7262718903918061]
AUC: 0.8741151799206235
AUC_ind: [0.910948665008278  0.8931203503399401]
nDCG: [0.9563165250321499]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [441, 246] samples with noise 20.0
Average adj diff: [5.090702947845805]
Average feat diff: [9.369614512471655]
Average noise diff: [9.369614512471655]
Average mAP: [0.6527142818953149]
AUC: 0.8397875815019663
AUC_ind: [0.8767539983400969 0.8610537864774793]
nDCG: [0.9442769871003611]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [406, 281] samples with noise 25.0
Average adj diff: [6.480295566502463]
Average feat diff: [11.566502463054187]
Average noise diff: [11.566502463054187]
Average mAP: [0.605805995101781]
AUC: 0.8182413953315965
AUC_ind: [0.8497706809657553 0.8401594624585167]
nDCG: [0.933887254788729]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [392, 295] samples with noise 30.0
Average adj diff: [7.821428571428571]
Average feat diff: [13.892857142857142]
Average noise diff: [13.892857142857142]
Average mAP: [0.5528939493074417]
AUC: 0.7867641343444173
AUC_ind: [0.8177185719203641 0.8149261699341158]
nDCG: [0.9220879706719494]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9770740798982562 0.9518087982011295], [622, 65]
0.1, [0.9414081387361849 0.9223348362099003], [544, 143]
0.15, [0.910948665008278  0.8931203503399401], [482, 205]
0.2, [0.8767539983400969 0.8610537864774793], [441, 246]
0.25, [0.8497706809657553 0.8401594624585167], [406, 281]
0.3, [0.8177185719203641 0.8149261699341158], [392, 295]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107378
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.4716781013140351
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [609, 78] samples with noise 5.0
Average adj diff: [0.8456486042692939]
Average feat diff: [1.5599343185550083]
Average noise diff: [1.5599343185550083]
Average mAP: [0.9087272716703619]
AUC: 0.9338339948155028
AUC_ind: [0.9778896969581736 0.9425543269054338]
nDCG: [0.9876956022593312]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [540, 147] samples with noise 10.0
Average adj diff: [2.259259259259259]
Average feat diff: [3.6148148148148147]
Average noise diff: [3.6148148148148147]
Average mAP: [0.8268902683711026]
AUC: 0.9091421001716966
AUC_ind: [0.948129152746417  0.9273578098942955]
nDCG: [0.974169033100362]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [452, 235] samples with noise 15.0
Average adj diff: [3.575221238938053]
Average feat diff: [5.831858407079646]
Average noise diff: [5.831858407079646]
Average mAP: [0.7495546848119272]
AUC: 0.8804989191291528
AUC_ind: [0.9159226996881734 0.9049318593092226]
nDCG: [0.9601884072654462]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [436, 251] samples with noise 20.0
Average adj diff: [4.922018348623853]
Average feat diff: [8.68348623853211]
Average noise diff: [8.68348623853211]
Average mAP: [0.6748071243529907]
AUC: 0.8487854624044525
AUC_ind: [0.8877066371939002 0.8639765520802896]
nDCG: [0.9461761738271952]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 25.0
Average adj diff: [6.281725888324873]
Average feat diff: [11.32994923857868]
Average noise diff: [11.32994923857868]
Average mAP: [0.6139500162357816]
AUC: 0.8194501040674115
AUC_ind: [0.8573629101155396 0.8408201180800344]
nDCG: [0.9368682571040591]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [415, 272] samples with noise 30.0
Average adj diff: [7.756626506024096]
Average feat diff: [13.643373493975904]
Average noise diff: [13.643373493975904]
Average mAP: [0.56522321853785]
AUC: 0.7985146212207861
AUC_ind: [0.8305540416033198 0.8249132027942615]
nDCG: [0.9279452753034011]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9778896969581736 0.9425543269054338], [609, 78]
0.1, [0.948129152746417  0.9273578098942955], [540, 147]
0.15, [0.9159226996881734 0.9049318593092226], [452, 235]
0.2, [0.8877066371939002 0.8639765520802896], [436, 251]
0.25, [0.8573629101155396 0.8408201180800344], [394, 293]
0.3, [0.8305540416033198 0.8249132027942615], [415, 272]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545533
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522441
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [627, 60] samples with noise 5.0
Average adj diff: [0.9043062200956937]
Average feat diff: [1.4896331738437]
Average noise diff: [1.4896331738437]
Average mAP: [0.9097998954299005]
AUC: 0.9363384934859544
AUC_ind: [0.9791146150580731 0.9494311981269773]
nDCG: [0.9887091135006041]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [512, 175] samples with noise 10.0
Average adj diff: [2.30859375]
Average feat diff: [3.78515625]
Average noise diff: [3.78515625]
Average mAP: [0.8043368042951877]
AUC: 0.9029097092943311
AUC_ind: [0.9427848100109856 0.919359236281356 ]
nDCG: [0.9707031014594862]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [485, 202] samples with noise 15.0
Average adj diff: [3.5525773195876287]
Average feat diff: [6.25979381443299]
Average noise diff: [6.25979381443299]
Average mAP: [0.7418088375054339]
AUC: 0.8764183717436628
AUC_ind: [0.9155417732933474 0.8967736020402811]
nDCG: [0.9601034156422596]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [409, 278] samples with noise 20.0
Average adj diff: [5.056234718826406]
Average feat diff: [8.904645476772616]
Average noise diff: [8.904645476772616]
Average mAP: [0.6430134830411635]
AUC: 0.8366786963621431
AUC_ind: [0.8707964894405794 0.8571482177340141]
nDCG: [0.9404856445293149]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [398, 289] samples with noise 25.0
Average adj diff: [6.324120603015075]
Average feat diff: [11.251256281407036]
Average noise diff: [11.251256281407036]
Average mAP: [0.6041723457079996]
AUC: 0.8199469120098104
AUC_ind: [0.8590467914363388 0.8368089983603745]
nDCG: [0.9329171977927156]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [403, 284] samples with noise 30.0
Average adj diff: [7.560794044665013]
Average feat diff: [13.498759305210918]
Average noise diff: [13.498759305210918]
Average mAP: [0.5537884106520475]
AUC: 0.7928998908078817
AUC_ind: [0.8312736474403483 0.8100906797406536]
nDCG: [0.9209164611220114]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9791146150580731 0.9494311981269773], [627, 60]
0.1, [0.9427848100109856 0.919359236281356 ], [512, 175]
0.15, [0.9155417732933474 0.8967736020402811], [485, 202]
0.2, [0.8707964894405794 0.8571482177340141], [409, 278]
0.25, [0.8590467914363388 0.8368089983603745], [398, 289]
0.3, [0.8312736474403483 0.8100906797406536], [403, 284]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1957240986750414
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362934
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [619, 68] samples with noise 5.0
Average adj diff: [0.9111470113085622]
Average feat diff: [1.321486268174475]
Average noise diff: [1.321486268174475]
Average mAP: [0.9112991308186271]
AUC: 0.9370473799103357
AUC_ind: [0.9810011732110351 0.9371950301274324]
nDCG: [0.9897997742953214]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [533, 154] samples with noise 10.0
Average adj diff: [2.275797373358349]
Average feat diff: [3.7898686679174483]
Average noise diff: [3.7898686679174483]
Average mAP: [0.8016622517183558]
AUC: 0.904543436932181
AUC_ind: [0.9418341420219388 0.9213356303526659]
nDCG: [0.9726489331511528]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [478, 209] samples with noise 15.0
Average adj diff: [3.696652719665272]
Average feat diff: [6.510460251046025]
Average noise diff: [6.510460251046025]
Average mAP: [0.7236642904849308]
AUC: 0.873384638262948
AUC_ind: [0.9067502748032666 0.8994681510430502]
nDCG: [0.9558905552158157]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [459, 228] samples with noise 20.0
Average adj diff: [5.093681917211329]
Average feat diff: [9.032679738562091]
Average noise diff: [9.032679738562091]
Average mAP: [0.6592980962162955]
AUC: 0.8481326541866847
AUC_ind: [0.8798763617362042 0.8677090482659878]
nDCG: [0.9456343381877582]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [422, 265] samples with noise 25.0
Average adj diff: [6.580568720379147]
Average feat diff: [11.872037914691942]
Average noise diff: [11.872037914691942]
Average mAP: [0.5892826164316594]
AUC: 0.8115332474906345
AUC_ind: [0.8447007949550456 0.835898504764217 ]
nDCG: [0.9293960953221074]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [401, 286] samples with noise 30.0
Average adj diff: [7.605985037406484]
Average feat diff: [13.820448877805486]
Average noise diff: [13.820448877805486]
Average mAP: [0.5585173863251964]
AUC: 0.794630312946184
AUC_ind: [0.8199255608504455 0.8271032098490514]
nDCG: [0.9260294023005333]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9810011732110351 0.9371950301274324], [619, 68]
0.1, [0.9418341420219388 0.9213356303526659], [533, 154]
0.15, [0.9067502748032666 0.8994681510430502], [478, 209]
0.2, [0.8798763617362042 0.8677090482659878], [459, 228]
0.25, [0.8447007949550456 0.835898504764217 ], [422, 265]
0.3, [0.8199255608504455 0.8271032098490514], [401, 286]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107387
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403554
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [619, 68] samples with noise 5.0
Average adj diff: [0.8109854604200323]
Average feat diff: [1.4184168012924072]
Average noise diff: [1.4184168012924072]
Average mAP: [0.917882700361108]
AUC: 0.9401259981921317
AUC_ind: [0.9823271041122303 0.9465866514393086]
nDCG: [0.9895317121751134]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [550, 137] samples with noise 10.0
Average adj diff: [2.321818181818182]
Average feat diff: [3.8327272727272725]
Average noise diff: [3.8327272727272725]
Average mAP: [0.8117727391808659]
AUC: 0.9049011572652131
AUC_ind: [0.9422621164549494 0.9280241422633607]
nDCG: [0.972450249854396]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [471, 216] samples with noise 15.0
Average adj diff: [3.5796178343949046]
Average feat diff: [6.24203821656051]
Average noise diff: [6.24203821656051]
Average mAP: [0.7492562565932563]
AUC: 0.8787507102089773
AUC_ind: [0.9192193061253346 0.8987107855152141]
nDCG: [0.9598767870481548]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [424, 263] samples with noise 20.0
Average adj diff: [4.849056603773585]
Average feat diff: [8.49056603773585]
Average noise diff: [8.49056603773585]
Average mAP: [0.6708910309836303]
AUC: 0.8458675871541912
AUC_ind: [0.8806347545125958 0.8707850408283069]
nDCG: [0.9455004858893564]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [410, 277] samples with noise 25.0
Average adj diff: [6.363414634146341]
Average feat diff: [11.039024390243902]
Average noise diff: [11.039024390243902]
Average mAP: [0.6133465798181176]
AUC: 0.8207227077260917
AUC_ind: [0.851207141291672  0.8515185709514957]
nDCG: [0.9332257173993557]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [412, 275] samples with noise 30.0
Average adj diff: [7.468446601941747]
Average feat diff: [14.009708737864077]
Average noise diff: [14.009708737864077]
Average mAP: [0.5564754502954695]
AUC: 0.7906108797429993
AUC_ind: [0.82673225770162   0.8173301105755391]
nDCG: [0.9211269346980291]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9823271041122303 0.9465866514393086], [619, 68]
0.1, [0.9422621164549494 0.9280241422633607], [550, 137]
0.15, [0.9192193061253346 0.8987107855152141], [471, 216]
0.2, [0.8806347545125958 0.8707850408283069], [424, 263]
0.25, [0.851207141291672  0.8515185709514957], [410, 277]
0.3, [0.82673225770162   0.8173301105755391], [412, 275]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545533
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.4720040097522441
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [639, 48] samples with noise 5.0
Average adj diff: [0.8857589984350548]
Average feat diff: [1.517996870109546]
Average noise diff: [1.517996870109546]
Average mAP: [0.9120617271229156]
AUC: 0.9378111765233953
AUC_ind: [0.9799020252001217 0.9468156226248029]
nDCG: [0.9887010834433848]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [545, 142] samples with noise 10.0
Average adj diff: [2.304587155963303]
Average feat diff: [3.8275229357798164]
Average noise diff: [3.8275229357798164]
Average mAP: [0.8158455694709829]
AUC: 0.9100439379607087
AUC_ind: [0.9478735056186043 0.9272268924622226]
nDCG: [0.9752265263770955]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [471, 216] samples with noise 15.0
Average adj diff: [3.583864118895966]
Average feat diff: [5.936305732484076]
Average noise diff: [5.936305732484076]
Average mAP: [0.7339128474598386]
AUC: 0.8784068012973915
AUC_ind: [0.9118430845550559 0.8941067310734342]
nDCG: [0.9590750420438701]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [445, 242] samples with noise 20.0
Average adj diff: [4.9460674157303375]
Average feat diff: [8.993258426966293]
Average noise diff: [8.993258426966293]
Average mAP: [0.6550172700416123]
AUC: 0.8433008036422313
AUC_ind: [0.8738357240877038 0.8712241610708317]
nDCG: [0.9441808306855286]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [393, 294] samples with noise 25.0
Average adj diff: [6.374045801526718]
Average feat diff: [11.577608142493638]
Average noise diff: [11.577608142493638]
Average mAP: [0.5969126245882543]
AUC: 0.8161910063832234
AUC_ind: [0.8471287069961235 0.8447731470653216]
nDCG: [0.9328155459788103]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [400, 287] samples with noise 30.0
Average adj diff: [7.54]
Average feat diff: [13.545]
Average noise diff: [13.545]
Average mAP: [0.5586932131463896]
AUC: 0.7920976039130134
AUC_ind: [0.8277418627456322 0.8142883814900196]
nDCG: [0.92528057617924]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9799020252001217 0.9468156226248029], [639, 48]
0.1, [0.9478735056186043 0.9272268924622226], [545, 142]
0.15, [0.9118430845550559 0.8941067310734342], [471, 216]
0.2, [0.8738357240877038 0.8712241610708317], [445, 242]
0.25, [0.8471287069961235 0.8447731470653216], [393, 294]
0.3, [0.8277418627456322 0.8142883814900196], [400, 287]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.1957240986750413
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.47011354753629353
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [627, 60] samples with noise 5.0
Average adj diff: [0.8947368421052632]
Average feat diff: [1.4736842105263157]
Average noise diff: [1.4736842105263157]
Average mAP: [0.9169640578375338]
AUC: 0.9373414146454724
AUC_ind: [0.9802260232224137 0.9458001110679437]
nDCG: [0.9890599539621622]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [537, 150] samples with noise 10.0
Average adj diff: [2.363128491620112]
Average feat diff: [3.7951582867783986]
Average noise diff: [3.7951582867783986]
Average mAP: [0.7990700633701132]
AUC: 0.901117976504341
AUC_ind: [0.9422942390638889 0.9140755478733604]
nDCG: [0.9711154097932195]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [471, 216] samples with noise 15.0
Average adj diff: [3.5286624203821657]
Average feat diff: [5.974522292993631]
Average noise diff: [5.974522292993631]
Average mAP: [0.7311030809591907]
AUC: 0.877797523027247
AUC_ind: [0.912117918159888  0.8998633574482956]
nDCG: [0.9587646089901983]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [427, 260] samples with noise 20.0
Average adj diff: [5.107728337236534]
Average feat diff: [9.053864168618267]
Average noise diff: [9.053864168618267]
Average mAP: [0.6613908421428273]
AUC: 0.8469576450902971
AUC_ind: [0.8793430364146404 0.8739991711706138]
nDCG: [0.9441540585534594]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [413, 274] samples with noise 25.0
Average adj diff: [6.348668280871671]
Average feat diff: [11.113801452784504]
Average noise diff: [11.113801452784504]
Average mAP: [0.6039702785529746]
AUC: 0.8173924324332664
AUC_ind: [0.849678172533058  0.8415388417437456]
nDCG: [0.9325791349801763]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [394, 293] samples with noise 30.0
Average adj diff: [7.817258883248731]
Average feat diff: [13.857868020304569]
Average noise diff: [13.857868020304569]
Average mAP: [0.5572546352702926]
AUC: 0.7931662908291518
AUC_ind: [0.8259546859702823 0.8238721461846514]
nDCG: [0.9248350349049452]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9802260232224137 0.9458001110679437], [627, 60]
0.1, [0.9422942390638889 0.9140755478733604], [537, 150]
0.15, [0.912117918159888  0.8998633574482956], [471, 216]
0.2, [0.8793430364146404 0.8739991711706138], [427, 260]
0.25, [0.849678172533058  0.8415388417437456], [413, 274]
0.3, [0.8259546859702823 0.8238721461846514], [394, 293]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107365
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.47167810131403476
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [637, 50] samples with noise 5.0
Average adj diff: [0.8712715855572999]
Average feat diff: [1.4913657770800628]
Average noise diff: [1.4913657770800628]
Average mAP: [0.920683080281633]
AUC: 0.9378153885642875
AUC_ind: [0.9801695669633972 0.9506554062064491]
nDCG: [0.989216879105267]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [542, 145] samples with noise 10.0
Average adj diff: [2.282287822878229]
Average feat diff: [3.782287822878229]
Average noise diff: [3.782287822878229]
Average mAP: [0.8167295232904693]
AUC: 0.9102992311041224
AUC_ind: [0.9480857946459099 0.9260263029383372]
nDCG: [0.9729498899091809]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [478, 209] samples with noise 15.0
Average adj diff: [3.7447698744769875]
Average feat diff: [6.2719665271966525]
Average noise diff: [6.2719665271966525]
Average mAP: [0.7218582694083039]
AUC: 0.87244050224178
AUC_ind: [0.9077950539625027 0.9022434339601458]
nDCG: [0.9567997255507725]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [440, 247] samples with noise 20.0
Average adj diff: [5.047727272727273]
Average feat diff: [8.795454545454545]
Average noise diff: [8.795454545454545]
Average mAP: [0.6628146687266941]
AUC: 0.84924795377831
AUC_ind: [0.8885493837771467 0.8638535405386011]
nDCG: [0.944578447466234]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [412, 275] samples with noise 25.0
Average adj diff: [6.160194174757281]
Average feat diff: [10.820388349514563]
Average noise diff: [10.820388349514563]
Average mAP: [0.6124297620864686]
AUC: 0.8234749880219293
AUC_ind: [0.855458333031328  0.8425454937645772]
nDCG: [0.9363364689476347]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [398, 289] samples with noise 30.0
Average adj diff: [7.527638190954774]
Average feat diff: [13.743718592964823]
Average noise diff: [13.743718592964823]
Average mAP: [0.5668544579518185]
AUC: 0.7957286435074047
AUC_ind: [0.8312718892369112 0.8186345352145767]
nDCG: [0.9250954167630397]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9801695669633972 0.9506554062064491], [637, 50]
0.1, [0.9480857946459099 0.9260263029383372], [542, 145]
0.15, [0.9077950539625027 0.9022434339601458], [478, 209]
0.2, [0.8885493837771467 0.8638535405386011], [440, 247]
0.25, [0.855458333031328  0.8425454937645772], [412, 275]
0.3, [0.8312718892369112 0.8186345352145767], [398, 289]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed1.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5555221492345561
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19191865241545525
 pos diff: [-0.017970555994708464  0.                  ], inv diff: [0.9513489557579268 0.                ], topk inv diff: [0.695921509373483 0.               ]
 Variance: 0.47200400975224427
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [180.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9581203505167759
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [628, 59] samples with noise 5.0
Average adj diff: [0.856687898089172]
Average feat diff: [1.4904458598726114]
Average noise diff: [1.4904458598726114]
Average mAP: [0.9203524506930125]
AUC: 0.9369348229057926
AUC_ind: [0.9796998829127019 0.9603206009742227]
nDCG: [0.9891913716276165]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [522, 165] samples with noise 10.0
Average adj diff: [2.1436781609195403]
Average feat diff: [3.4827586206896552]
Average noise diff: [3.4827586206896552]
Average mAP: [0.807068982648866]
AUC: 0.9050823104638949
AUC_ind: [0.9487835232151375 0.9120502026157469]
nDCG: [0.9725228454342902]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [495, 192] samples with noise 15.0
Average adj diff: [3.6363636363636362]
Average feat diff: [6.157575757575757]
Average noise diff: [6.157575757575757]
Average mAP: [0.7296344006019474]
AUC: 0.872351021972149
AUC_ind: [0.909743491645611  0.8954623916125429]
nDCG: [0.9575570601749798]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [422, 265] samples with noise 20.0
Average adj diff: [5.187203791469194]
Average feat diff: [8.938388625592417]
Average noise diff: [8.938388625592417]
Average mAP: [0.6613710137383181]
AUC: 0.845172555824808
AUC_ind: [0.8716930156189183 0.8785602752300808]
nDCG: [0.9453566154594688]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [386, 301] samples with noise 25.0
Average adj diff: [6.106217616580311]
Average feat diff: [11.015544041450777]
Average noise diff: [11.015544041450777]
Average mAP: [0.6080205933019805]
AUC: 0.8188353736669993
AUC_ind: [0.8537420141986711 0.8448854424640567]
nDCG: [0.9360329333243103]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [390, 297] samples with noise 30.0
Average adj diff: [7.687179487179487]
Average feat diff: [13.835897435897436]
Average noise diff: [13.835897435897436]
Average mAP: [0.563529727901289]
AUC: 0.7992437158795708
AUC_ind: [0.8281773951039628 0.8252710172389078]
nDCG: [0.9246290642235676]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.9796998829127019 0.9603206009742227], [628, 59]
0.1, [0.9487835232151375 0.9120502026157469], [522, 165]
0.15, [0.909743491645611  0.8954623916125429], [495, 192]
0.2, [0.8716930156189183 0.8785602752300808], [422, 265]
0.25, [0.8537420141986711 0.8448854424640567], [386, 301]
0.3, [0.8281773951039628 0.8252710172389078], [390, 297]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed0.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5644912611541819
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19572409867504156
 pos diff: [-0.01795099630105964  0.                 ], inv diff: [0.9510571645163379 0.                ], topk inv diff: [0.6983035279239976 0.                ]
 Variance: 0.4701135475362936
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [175.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9586994294228898
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [625, 62] samples with noise 5.0
Average adj diff: [0.808]
Average feat diff: [1.4176]
Average noise diff: [1.4176]
Average mAP: [0.9190061331626013]
AUC: 0.9378966709274813
AUC_ind: [0.980603400647631  0.9474954537040926]
nDCG: [0.9887586987999991]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [530, 157] samples with noise 10.0
Average adj diff: [2.2]
Average feat diff: [3.5849056603773586]
Average noise diff: [3.5849056603773586]
Average mAP: [0.8180406498223346]
AUC: 0.9072925058273298
AUC_ind: [0.94648471785244   0.9290242283901847]
nDCG: [0.9719379979993875]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [490, 197] samples with noise 15.0
Average adj diff: [3.477551020408163]
Average feat diff: [6.583673469387755]
Average noise diff: [6.583673469387755]
Average mAP: [0.730082011837706]
AUC: 0.8743902172799958
AUC_ind: [0.9138340673503578 0.8885666450564766]
nDCG: [0.9581932076924823]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [424, 263] samples with noise 20.0
Average adj diff: [5.03066037735849]
Average feat diff: [9.070754716981131]
Average noise diff: [9.070754716981131]
Average mAP: [0.6644949168045925]
AUC: 0.8467460209981472
AUC_ind: [0.8772818749354021 0.8807662333221331]
nDCG: [0.9461508114577117]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [430, 257] samples with noise 25.0
Average adj diff: [6.113953488372093]
Average feat diff: [11.525581395348837]
Average noise diff: [11.525581395348837]
Average mAP: [0.5987357240178839]
AUC: 0.8104817571513363
AUC_ind: [0.8424242383892343 0.8412419296934998]
nDCG: [0.9327347278127721]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [377, 310] samples with noise 30.0
Average adj diff: [7.7161803713527854]
Average feat diff: [13.687002652519894]
Average noise diff: [13.687002652519894]
Average mAP: [0.5538904334458441]
AUC: 0.7939325061301015
AUC_ind: [0.8133725552738176 0.8281235240735342]
nDCG: [0.9221275883847753]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.980603400647631  0.9474954537040926], [625, 62]
0.1, [0.94648471785244   0.9290242283901847], [530, 157]
0.15, [0.9138340673503578 0.8885666450564766], [490, 197]
0.2, [0.8772818749354021 0.8807662333221331], [424, 263]
0.25, [0.8424242383892343 0.8412419296934998], [430, 257]
0.3, [0.8133725552738176 0.8281235240735342], [377, 310]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: trained_rcexplainers_ours/explainer_seed2.pth
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5512138409210925
 noise percent: 10.0, inverse noise: False
 avg removed edges: 0.0
 pred removed edges: 0.0
 avg added edges: 0.0
 avg noise diff: 0.0
 avg pred diff: 0.0
 skipped iters: 0.0
 Average mask density: 0.19566681010107387
 pos diff: [-0.018529521325790204  0.                  ], inv diff: [0.9512605913948631 0.                ], topk inv diff: [0.7092024176401863 0.                ]
 Variance: 0.4716781013140351
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [166.   0.], Incorrect preds: 0.0, Total: [687.   1.]
 

 

Evaluted [687, 0] samples with noise 0
Average adj diff: [0.]
Average feat diff: [0.]
Average noise diff: [0.]
Average mAP: [1.]
AUC: 0.9587514897233974
AUC_ind: [1. 0.]
nDCG: [1.]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [637, 50] samples with noise 5.0
Average adj diff: [0.869701726844584]
Average feat diff: [1.554160125588697]
Average noise diff: [1.554160125588697]
Average mAP: [0.9142885746744591]
AUC: 0.9356581483519184
AUC_ind: [0.978721417285776  0.9410257062888973]
nDCG: [0.9885228023140197]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [546, 141] samples with noise 10.0
Average adj diff: [2.188644688644689]
Average feat diff: [3.7472527472527473]
Average noise diff: [3.7472527472527473]
Average mAP: [0.822407871129953]
AUC: 0.90753911290227
AUC_ind: [0.9479439430193287 0.9239071589214622]
nDCG: [0.9734968524987934]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [480, 207] samples with noise 15.0
Average adj diff: [3.26875]
Average feat diff: [6.1625]
Average noise diff: [6.1625]
Average mAP: [0.7356684716301664]
AUC: 0.8740978462762062
AUC_ind: [0.9135679573960969 0.8914476276117113]
nDCG: [0.9581427514105766]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [428, 259] samples with noise 20.0
Average adj diff: [4.988317757009346]
Average feat diff: [8.411214953271028]
Average noise diff: [8.411214953271028]
Average mAP: [0.654760466613854]
AUC: 0.8480395887124692
AUC_ind: [0.8827711160007192 0.8611452950989882]
nDCG: [0.9450506230838792]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [418, 269] samples with noise 25.0
Average adj diff: [6.2009569377990434]
Average feat diff: [11.157894736842104]
Average noise diff: [11.157894736842104]
Average mAP: [0.6154318487049095]
AUC: 0.8243510396995223
AUC_ind: [0.8596489783592428 0.8397571645268291]
nDCG: [0.9355578501031528]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [393, 294] samples with noise 30.0
Average adj diff: [7.56234096692112]
Average feat diff: [14.183206106870228]
Average noise diff: [14.183206106870228]
Average mAP: [0.5588554691380803]
AUC: 0.7937891820206668
AUC_ind: [0.8221022993172536 0.8184341592623336]
nDCG: [0.9229583438261711]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,


 NOISE SUMMARY 

0, [1. 0.], [687, 0]
0.05, [0.978721417285776  0.9410257062888973], [637, 50]
0.1, [0.9479439430193287 0.9239071589214622], [546, 141]
0.15, [0.9135679573960969 0.8914476276117113], [480, 207]
0.2, [0.8827711160007192 0.8611452950989882], [428, 259]
0.25, [0.8596489783592428 0.8397571645268291], [418, 269]
0.3, [0.8221022993172536 0.8184341592623336], [393, 294]
 
 SUMMARY 
