
 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109008
 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: 0.16184265930318015
 pos diff: [0.0339721316333421 0.                ], inv diff: [0.9235811721549805 0.                ], topk inv diff: [0.6724283863103026 0.                ]
 Variance: 0.4588115800955736
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [191.   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: 0.9532936529093794
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: [0.8892405063291139]
Average feat diff: [1.4588607594936709]
Average noise diff: [1.4588607594936709]
Average mAP: [0.9125975753797178]
AUC: 0.9317646859079136
AUC_ind: [0.9795269890256235 0.9432731493695102]
nDCG: [0.9892342839411729]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.301470588235294]
Average feat diff: [3.8455882352941178]
Average noise diff: [3.8455882352941178]
Average mAP: [0.7953392765292036]
AUC: 0.8964586565130096
AUC_ind: [0.9377503107036371 0.9203847584801985]
nDCG: [0.9699179763966503]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.575883575883576]
Average feat diff: [6.486486486486487]
Average noise diff: [6.486486486486487]
Average mAP: [0.7156068010448623]
AUC: 0.8633421115780211
AUC_ind: [0.9084159130096874 0.8859590284481328]
nDCG: [0.9570401235007413]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [447, 240] samples with noise 20.0
Average adj diff: [4.941834451901566]
Average feat diff: [9.0917225950783]
Average noise diff: [9.0917225950783]
Average mAP: [0.6600460930920142]
AUC: 0.8369309967690652
AUC_ind: [0.8795300540966021 0.8685441028913587]
nDCG: [0.9458576553846421]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 25.0
Average adj diff: [6.231503579952268]
Average feat diff: [11.155131264916468]
Average noise diff: [11.155131264916468]
Average mAP: [0.5959605181204207]
AUC: 0.806218398290061
AUC_ind: [0.852679600048771  0.8268877512357031]
nDCG: [0.9301896937581791]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.9503722084367245]
Average feat diff: [14.392059553349876]
Average noise diff: [14.392059553349876]
Average mAP: [0.5429641106705891]
AUC: 0.7788579877672577
AUC_ind: [0.8146198970096327 0.8141843281242387]
nDCG: [0.9201167533536366]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9795269890256235 0.9432731493695102], [632, 55]
0.1, [0.9377503107036371 0.9203847584801985], [544, 143]
0.15, [0.9084159130096874 0.8859590284481328], [481, 206]
0.2, [0.8795300540966021 0.8685441028913587], [447, 240]
0.25, [0.852679600048771  0.8268877512357031], [419, 268]
0.3, [0.8146198970096327 0.8141843281242387], [403, 284]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109008
 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: 0.16184265930318031
 pos diff: [0.0339721316333421 0.                ], inv diff: [0.9235811721549805 0.                ], topk inv diff: [0.6724283863103026 0.                ]
 Variance: 0.45881158009557377
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [191.   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: 0.9532936529093794
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: [0.9202551834130781]
Average feat diff: [1.518341307814992]
Average noise diff: [1.518341307814992]
Average mAP: [0.9021877031857107]
AUC: 0.9282352003778701
AUC_ind: [0.9761680208627895 0.9377243540678167]
nDCG: [0.9871162084590754]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [562, 125] samples with noise 10.0
Average adj diff: [2.185053380782918]
Average feat diff: [3.804270462633452]
Average noise diff: [3.804270462633452]
Average mAP: [0.8001946625295075]
AUC: 0.8957429602730735
AUC_ind: [0.9391623048463402 0.9240465199925698]
nDCG: [0.9711266259580433]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.6498993963782698]
Average feat diff: [6.21327967806841]
Average noise diff: [6.21327967806841]
Average mAP: [0.7187502545815923]
AUC: 0.8659277889362779
AUC_ind: [0.9033174338041406 0.8971187220504094]
nDCG: [0.9559687753682123]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [442, 245] samples with noise 20.0
Average adj diff: [4.993212669683258]
Average feat diff: [8.995475113122172]
Average noise diff: [8.995475113122172]
Average mAP: [0.6474844295617357]
AUC: 0.8307840657893686
AUC_ind: [0.8701374591450368 0.8554846631273088]
nDCG: [0.9410750962354995]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.548235294117647]
Average feat diff: [11.463529411764705]
Average noise diff: [11.463529411764705]
Average mAP: [0.5873214006131333]
AUC: 0.8047317306831101
AUC_ind: [0.8443796765748072 0.8314256512704642]
nDCG: [0.9310206111662597]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [388, 299] samples with noise 30.0
Average adj diff: [7.298969072164948]
Average feat diff: [13.685567010309278]
Average noise diff: [13.685567010309278]
Average mAP: [0.55517757033181]
AUC: 0.7814704711688525
AUC_ind: [0.8281816998413192 0.8097460420018442]
nDCG: [0.9224243887104344]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9761680208627895 0.9377243540678167], [627, 60]
0.1, [0.9391623048463402 0.9240465199925698], [562, 125]
0.15, [0.9033174338041406 0.8971187220504094], [497, 190]
0.2, [0.8701374591450368 0.8554846631273088], [442, 245]
0.25, [0.8443796765748072 0.8314256512704642], [425, 262]
0.3, [0.8281816998413192 0.8097460420018442], [388, 299]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109008
 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: 0.16184265930318
 pos diff: [0.0339721316333421 0.                ], inv diff: [0.9235811721549805 0.                ], topk inv diff: [0.6724283863103026 0.                ]
 Variance: 0.458811580095574
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [191.   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: 0.9532936529093794
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: [0.9022435897435898]
Average feat diff: [1.4967948717948718]
Average noise diff: [1.4967948717948718]
Average mAP: [0.9093448539475093]
AUC: 0.9277733799921108
AUC_ind: [0.9772757430496276 0.9466356607007153]
nDCG: [0.9874291649643709]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2877959927140257]
Average feat diff: [3.948998178506375]
Average noise diff: [3.948998178506375]
Average mAP: [0.7964090679508224]
AUC: 0.8912737789357953
AUC_ind: [0.9378722644607093 0.9062166626317858]
nDCG: [0.9712138736215754]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.6160337552742616]
Average feat diff: [6.105485232067511]
Average noise diff: [6.105485232067511]
Average mAP: [0.7195227195741903]
AUC: 0.8682469856526052
AUC_ind: [0.910521282295629  0.8941459120950876]
nDCG: [0.9561464848499465]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.905092592592593]
Average feat diff: [8.351851851851851]
Average noise diff: [8.351851851851851]
Average mAP: [0.6481226574668015]
AUC: 0.8327311407573647
AUC_ind: [0.8694038221515618 0.8634383269000785]
nDCG: [0.9424296871744163]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [357, 330] samples with noise 25.0
Average adj diff: [6.330532212885154]
Average feat diff: [11.72549019607843]
Average noise diff: [11.72549019607843]
Average mAP: [0.5789487668176293]
AUC: 0.7945647433422574
AUC_ind: [0.8346161786464911 0.8289584079225721]
nDCG: [0.9259733132153094]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.6368286445012785]
Average feat diff: [13.989769820971867]
Average noise diff: [13.989769820971867]
Average mAP: [0.549987360032717]
AUC: 0.7858947270871466
AUC_ind: [0.8221675623732903 0.8184851956057602]
nDCG: [0.9221909878342004]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9772757430496276 0.9466356607007153], [624, 63]
0.1, [0.9378722644607093 0.9062166626317858], [549, 138]
0.15, [0.910521282295629  0.8941459120950876], [474, 213]
0.2, [0.8694038221515618 0.8634383269000785], [432, 255]
0.25, [0.8346161786464911 0.8289584079225721], [357, 330]
0.3, [0.8221675623732903 0.8184851956057602], [391, 296]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109008
 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: 0.16184265930318023
 pos diff: [0.0339721316333421 0.                ], inv diff: [0.9235811721549805 0.                ], topk inv diff: [0.6724283863103026 0.                ]
 Variance: 0.4588115800955736
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [191.   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: 0.9532936529093794
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: [0.9357945425361156]
Average feat diff: [1.4028892455858748]
Average noise diff: [1.4028892455858748]
Average mAP: [0.9060135551697615]
AUC: 0.9297389461240282
AUC_ind: [0.9794244692687651 0.9312344044408002]
nDCG: [0.988273955159307]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2076190476190476]
Average feat diff: [3.7142857142857144]
Average noise diff: [3.7142857142857144]
Average mAP: [0.8030260938778732]
AUC: 0.894271978325059
AUC_ind: [0.9421094246410991 0.9216946495606944]
nDCG: [0.9717688446031315]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.6103092783505155]
Average feat diff: [6.334020618556701]
Average noise diff: [6.334020618556701]
Average mAP: [0.7141810085036121]
AUC: 0.8591912948794972
AUC_ind: [0.9049400926888415 0.8724885503354914]
nDCG: [0.9553808213556939]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.911214953271028]
Average feat diff: [8.658878504672897]
Average noise diff: [8.658878504672897]
Average mAP: [0.6527556683228686]
AUC: 0.8331860103013498
AUC_ind: [0.8734945249660218 0.8575039947537962]
nDCG: [0.9428069494497096]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.387654320987655]
Average feat diff: [11.585185185185185]
Average noise diff: [11.585185185185185]
Average mAP: [0.5961410918960185]
AUC: 0.8062458788588953
AUC_ind: [0.8383780500428697 0.8416390942903457]
nDCG: [0.932659615744723]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.544554455445544]
Average feat diff: [13.485148514851485]
Average noise diff: [13.485148514851485]
Average mAP: [0.5536287777447834]
AUC: 0.7763766659425506
AUC_ind: [0.8162817407749234 0.8133187324430136]
nDCG: [0.9221138385135335]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9794244692687651 0.9312344044408002], [623, 64]
0.1, [0.9421094246410991 0.9216946495606944], [525, 162]
0.15, [0.9049400926888415 0.8724885503354914], [485, 202]
0.2, [0.8734945249660218 0.8575039947537962], [428, 259]
0.25, [0.8383780500428697 0.8416390942903457], [405, 282]
0.3, [0.8162817407749234 0.8133187324430136], [404, 283]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109008
 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: 0.16184265930318023
 pos diff: [0.0339721316333421 0.                ], inv diff: [0.9235811721549805 0.                ], topk inv diff: [0.6724283863103026 0.                ]
 Variance: 0.45881158009557343
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [191.   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: 0.9532936529093794
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: [0.8791732909379968]
Average feat diff: [1.4562798092209857]
Average noise diff: [1.4562798092209857]
Average mAP: [0.9078105084764104]
AUC: 0.9286316407331747
AUC_ind: [0.9762265501228689 0.9489277126956602]
nDCG: [0.9877944836117147]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.3830570902394106]
Average feat diff: [3.9447513812154695]
Average noise diff: [3.9447513812154695]
Average mAP: [0.8100546393480128]
AUC: 0.8975365706715139
AUC_ind: [0.9423470598305999 0.9227606920633359]
nDCG: [0.971993342644165]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [505, 182] samples with noise 15.0
Average adj diff: [3.398019801980198]
Average feat diff: [6.344554455445545]
Average noise diff: [6.344554455445545]
Average mAP: [0.7119014182967306]
AUC: 0.8625687770403543
AUC_ind: [0.902949214415119  0.8860610527529317]
nDCG: [0.9554888340432556]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.821100917431193]
Average feat diff: [8.729357798165138]
Average noise diff: [8.729357798165138]
Average mAP: [0.6516874581199595]
AUC: 0.8383797104708032
AUC_ind: [0.8723951591338993 0.8758787508376681]
nDCG: [0.9432636941189944]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 25.0
Average adj diff: [6.247002398081535]
Average feat diff: [11.333333333333334]
Average noise diff: [11.333333333333334]
Average mAP: [0.5972246107637013]
AUC: 0.8059975383513371
AUC_ind: [0.8417186437680538 0.8465475886916619]
nDCG: [0.9322198195718401]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.69521410579345]
Average feat diff: [13.702770780856422]
Average noise diff: [13.702770780856422]
Average mAP: [0.5486472332919191]
AUC: 0.7807869750525673
AUC_ind: [0.8219606337394003 0.8100507382784122]
nDCG: [0.923620044492065]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9762265501228689 0.9489277126956602], [629, 58]
0.1, [0.9423470598305999 0.9227606920633359], [543, 144]
0.15, [0.902949214415119  0.8860610527529317], [505, 182]
0.2, [0.8723951591338993 0.8758787508376681], [436, 251]
0.25, [0.8417186437680538 0.8465475886916619], [417, 270]
0.3, [0.8219606337394003 0.8100507382784122], [397, 290]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817665
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206529
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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 [633, 54] samples with noise 5.0
Average adj diff: [0.8546603475513428]
Average feat diff: [1.5229067930489733]
Average noise diff: [1.5229067930489733]
Average mAP: [0.9070744424656974]
AUC: 0.9297518209629047
AUC_ind: [0.9752144125147536 0.9563621166075607]
nDCG: [0.9876755674354796]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.0720887245841033]
Average feat diff: [3.7597042513863217]
Average noise diff: [3.7597042513863217]
Average mAP: [0.794340585488168]
AUC: 0.8939955887202023
AUC_ind: [0.9385265437770258 0.9138184640518693]
nDCG: [0.9703191806369759]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.4968152866242037]
Average feat diff: [6.2123142250530785]
Average noise diff: [6.2123142250530785]
Average mAP: [0.7180882348227328]
AUC: 0.8603603013544796
AUC_ind: [0.9052826542668521 0.8870992818553161]
nDCG: [0.9569377599462354]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [5.033492822966507]
Average feat diff: [8.698564593301436]
Average noise diff: [8.698564593301436]
Average mAP: [0.651200927979586]
AUC: 0.8408603912624559
AUC_ind: [0.8853865458945558 0.854251196680133 ]
nDCG: [0.9440629432735237]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.258620689655173]
Average feat diff: [10.960591133004925]
Average noise diff: [10.960591133004925]
Average mAP: [0.6039422874414196]
AUC: 0.8084436500595026
AUC_ind: [0.839143888086088 0.845967356890805]
nDCG: [0.9327250523187817]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.644670050761421]
Average feat diff: [14.34010152284264]
Average noise diff: [14.34010152284264]
Average mAP: [0.5451414811078856]
AUC: 0.7809914082285135
AUC_ind: [0.8138034323616886 0.8163320245273495]
nDCG: [0.92239480740557]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9752144125147536 0.9563621166075607], [633, 54]
0.1, [0.9385265437770258 0.9138184640518693], [541, 146]
0.15, [0.9052826542668521 0.8870992818553161], [471, 216]
0.2, [0.8853865458945558 0.854251196680133 ], [418, 269]
0.25, [0.839143888086088 0.845967356890805], [406, 281]
0.3, [0.8138034323616886 0.8163320245273495], [394, 293]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.1618426600281768
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206524
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.873217115689382]
Average feat diff: [1.445324881141046]
Average noise diff: [1.445324881141046]
Average mAP: [0.907471324304372]
AUC: 0.9287733744304015
AUC_ind: [0.9769997724378701 0.952130207688409 ]
nDCG: [0.9889204332055535]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.299445471349353]
Average feat diff: [3.866913123844732]
Average noise diff: [3.866913123844732]
Average mAP: [0.7912557767415649]
AUC: 0.8929697203434253
AUC_ind: [0.9371038513483506 0.9083187629458856]
nDCG: [0.9703942681921952]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.573529411764706]
Average feat diff: [5.920168067226891]
Average noise diff: [5.920168067226891]
Average mAP: [0.717103834894991]
AUC: 0.8664097425653288
AUC_ind: [0.907242182888133  0.8884451889750218]
nDCG: [0.9564979474830276]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [446, 241] samples with noise 20.0
Average adj diff: [4.9708520179372195]
Average feat diff: [9.026905829596412]
Average noise diff: [9.026905829596412]
Average mAP: [0.6398420734852737]
AUC: 0.8326139549523379
AUC_ind: [0.8699500574272249 0.8555808407173686]
nDCG: [0.941114614176647]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.365]
Average feat diff: [11.525]
Average noise diff: [11.525]
Average mAP: [0.5868511167184682]
AUC: 0.8018566856385164
AUC_ind: [0.8438359098590966 0.8270422702030525]
nDCG: [0.9304427947028201]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.545691906005222]
Average feat diff: [13.93733681462141]
Average noise diff: [13.93733681462141]
Average mAP: [0.5465318053233014]
AUC: 0.7855020821988743
AUC_ind: [0.8264386482392956 0.8115366904213996]
nDCG: [0.9207753041476002]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9769997724378701 0.952130207688409 ], [631, 56]
0.1, [0.9371038513483506 0.9083187629458856], [541, 146]
0.15, [0.907242182888133  0.8884451889750218], [476, 211]
0.2, [0.8699500574272249 0.8555808407173686], [446, 241]
0.25, [0.8438359098590966 0.8270422702030525], [400, 287]
0.3, [0.8264386482392956 0.8115366904213996], [383, 304]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817677
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206527
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.931637519872814]
Average feat diff: [1.4054054054054055]
Average noise diff: [1.4054054054054055]
Average mAP: [0.9073709040120325]
AUC: 0.9301453429144259
AUC_ind: [0.9782111181410792 0.9315924234160458]
nDCG: [0.9886744206683596]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2288288288288287]
Average feat diff: [3.700900900900901]
Average noise diff: [3.700900900900901]
Average mAP: [0.8045786687791023]
AUC: 0.8987541120701793
AUC_ind: [0.9413509729125471 0.9237031126694276]
nDCG: [0.9722673541048875]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.422037422037422]
Average feat diff: [6.299376299376299]
Average noise diff: [6.299376299376299]
Average mAP: [0.7179401145202682]
AUC: 0.868151969360095
AUC_ind: [0.9064859456133597 0.8920081027560034]
nDCG: [0.9571001977156162]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.979118329466357]
Average feat diff: [8.747099767981439]
Average noise diff: [8.747099767981439]
Average mAP: [0.643081902946782]
AUC: 0.8350840879656404
AUC_ind: [0.874703637609166 0.854740387535902]
nDCG: [0.9431913970202969]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.54911838790932]
Average feat diff: [11.57682619647355]
Average noise diff: [11.57682619647355]
Average mAP: [0.5890601309524836]
AUC: 0.8013549502170609
AUC_ind: [0.8467741794202973 0.8242427482231541]
nDCG: [0.9297616618301743]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [388, 299] samples with noise 30.0
Average adj diff: [7.536082474226804]
Average feat diff: [13.974226804123711]
Average noise diff: [13.974226804123711]
Average mAP: [0.5544725267570637]
AUC: 0.7879246360864027
AUC_ind: [0.8203080243640244 0.8211403989818592]
nDCG: [0.9218115845604631]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9782111181410792 0.9315924234160458], [629, 58]
0.1, [0.9413509729125471 0.9237031126694276], [555, 132]
0.15, [0.9064859456133597 0.8920081027560034], [481, 206]
0.2, [0.874703637609166 0.854740387535902], [431, 256]
0.25, [0.8467741794202973 0.8242427482231541], [397, 290]
0.3, [0.8203080243640244 0.8211403989818592], [388, 299]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817677
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206522
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.8200636942675159]
Average feat diff: [1.3885350318471337]
Average noise diff: [1.3885350318471337]
Average mAP: [0.9078270119488812]
AUC: 0.9298878581992713
AUC_ind: [0.9757362073288474 0.9508746342055655]
nDCG: [0.988527323906144]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.340782122905028]
Average feat diff: [3.750465549348231]
Average noise diff: [3.750465549348231]
Average mAP: [0.7954093715077526]
AUC: 0.8930099731440089
AUC_ind: [0.9363193753644763 0.9128937873564403]
nDCG: [0.9690284314314452]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.6012526096033404]
Average feat diff: [6.279749478079332]
Average noise diff: [6.279749478079332]
Average mAP: [0.7114090971187885]
AUC: 0.8608202238771647
AUC_ind: [0.9015344212540619 0.8880660650501999]
nDCG: [0.9556363695340505]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 20.0
Average adj diff: [5.167023554603855]
Average feat diff: [8.8779443254818]
Average noise diff: [8.8779443254818]
Average mAP: [0.645824197603868]
AUC: 0.8342461558493697
AUC_ind: [0.8684391049572231 0.8642433730864834]
nDCG: [0.9429913712480682]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [5.860294117647059]
Average feat diff: [11.446078431372548]
Average noise diff: [11.446078431372548]
Average mAP: [0.5975290254329623]
AUC: 0.8116335421403504
AUC_ind: [0.8478333822062162 0.8349391963836701]
nDCG: [0.9344863453497002]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.707920792079208]
Average feat diff: [14.138613861386139]
Average noise diff: [14.138613861386139]
Average mAP: [0.5456806379599884]
AUC: 0.7823865500689526
AUC_ind: [0.8137817023330126 0.8138195015388033]
nDCG: [0.9207617782523254]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9757362073288474 0.9508746342055655], [628, 59]
0.1, [0.9363193753644763 0.9128937873564403], [537, 150]
0.15, [0.9015344212540619 0.8880660650501999], [479, 208]
0.2, [0.8684391049572231 0.8642433730864834], [467, 220]
0.25, [0.8478333822062162 0.8349391963836701], [408, 279]
0.3, [0.8137817023330126 0.8138195015388033], [404, 283]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817693
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206522
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.8910256410256411]
Average feat diff: [1.3878205128205128]
Average noise diff: [1.3878205128205128]
Average mAP: [0.9057252955023934]
AUC: 0.9287289385360562
AUC_ind: [0.9760041993363641 0.9470173302117043]
nDCG: [0.9881001448084671]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.184357541899441]
Average feat diff: [3.973929236499069]
Average noise diff: [3.973929236499069]
Average mAP: [0.8042840997766312]
AUC: 0.8950823181746306
AUC_ind: [0.9402573186146822 0.919602214198762 ]
nDCG: [0.9707394676056831]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [483, 204] samples with noise 15.0
Average adj diff: [3.536231884057971]
Average feat diff: [6.045548654244306]
Average noise diff: [6.045548654244306]
Average mAP: [0.7043033575112991]
AUC: 0.859830618543665
AUC_ind: [0.9036733510403651 0.8765599959593985]
nDCG: [0.9546561685056575]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [456, 231] samples with noise 20.0
Average adj diff: [4.923245614035087]
Average feat diff: [8.780701754385966]
Average noise diff: [8.780701754385966]
Average mAP: [0.6375858907081514]
AUC: 0.8325013442517553
AUC_ind: [0.8714336489410175 0.8507637164776917]
nDCG: [0.9423143822383879]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 25.0
Average adj diff: [6.435323383084577]
Average feat diff: [11.2636815920398]
Average noise diff: [11.2636815920398]
Average mAP: [0.6058731291155949]
AUC: 0.8089475993303233
AUC_ind: [0.8439685970187476 0.8443268167064873]
nDCG: [0.9333510659847767]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.774193548387097]
Average feat diff: [13.71712158808933]
Average noise diff: [13.71712158808933]
Average mAP: [0.56828790606284]
AUC: 0.7969816889310156
AUC_ind: [0.8338241303811222 0.8226816328669898]
nDCG: [0.9257070199509001]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9760041993363641 0.9470173302117043], [624, 63]
0.1, [0.9402573186146822 0.919602214198762 ], [537, 150]
0.15, [0.9036733510403651 0.8765599959593985], [483, 204]
0.2, [0.8714336489410175 0.8507637164776917], [456, 231]
0.25, [0.8439685970187476 0.8443268167064873], [402, 285]
0.3, [0.8338241303811222 0.8226816328669898], [403, 284]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.1618426600281768
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.45881158082065276
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.8969889064976229]
Average feat diff: [1.508716323296355]
Average noise diff: [1.508716323296355]
Average mAP: [0.898521827589412]
AUC: 0.9286731955774864
AUC_ind: [0.9729521316938111 0.9431499297651328]
nDCG: [0.987466468625041]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2257462686567164]
Average feat diff: [3.832089552238806]
Average noise diff: [3.832089552238806]
Average mAP: [0.8070334941464227]
AUC: 0.897571436324021
AUC_ind: [0.9452500317979105 0.92042007535341  ]
nDCG: [0.9725377831368235]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.475]
Average feat diff: [5.954166666666667]
Average noise diff: [5.954166666666667]
Average mAP: [0.7204758552460071]
AUC: 0.8649500592090634
AUC_ind: [0.9063048534091841 0.8908521102759166]
nDCG: [0.9565566470541403]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [5.067415730337078]
Average feat diff: [9.03820224719101]
Average noise diff: [9.03820224719101]
Average mAP: [0.6427641295497125]
AUC: 0.8317898410391315
AUC_ind: [0.8691756076965044 0.870390123588859 ]
nDCG: [0.9421755636376303]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.313397129186603]
Average feat diff: [11.181818181818182]
Average noise diff: [11.181818181818182]
Average mAP: [0.6067794252635424]
AUC: 0.812457347623737
AUC_ind: [0.8510626334890322 0.8342585027972528]
nDCG: [0.9335124569804012]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.602666666666667]
Average feat diff: [13.221333333333334]
Average noise diff: [13.221333333333334]
Average mAP: [0.5478337210120087]
AUC: 0.7815906150579213
AUC_ind: [0.832265455521462  0.7964338153525534]
nDCG: [0.9214809981696714]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9729521316938111 0.9431499297651328], [631, 56]
0.1, [0.9452500317979105 0.92042007535341  ], [536, 151]
0.15, [0.9063048534091841 0.8908521102759166], [480, 207]
0.2, [0.8691756076965044 0.870390123588859 ], [445, 242]
0.25, [0.8510626334890322 0.8342585027972528], [418, 269]
0.3, [0.832265455521462  0.7964338153525534], [375, 312]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817696
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206521
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.8096]
Average feat diff: [1.552]
Average noise diff: [1.552]
Average mAP: [0.9043914493213997]
AUC: 0.9277200763502066
AUC_ind: [0.9752342047450182 0.9393168637395428]
nDCG: [0.9869393889115678]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2892249527410207]
Average feat diff: [4.147448015122873]
Average noise diff: [4.147448015122873]
Average mAP: [0.7831809276296866]
AUC: 0.8907680040514407
AUC_ind: [0.9344218868585887 0.916723031351898 ]
nDCG: [0.9684275424996538]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [487, 200] samples with noise 15.0
Average adj diff: [3.5790554414784395]
Average feat diff: [6.123203285420945]
Average noise diff: [6.123203285420945]
Average mAP: [0.7178505928359766]
AUC: 0.8668161599572344
AUC_ind: [0.9082992613351278 0.8886330048487717]
nDCG: [0.9554901881682295]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [5.022321428571429]
Average feat diff: [8.767857142857142]
Average noise diff: [8.767857142857142]
Average mAP: [0.6504890184272856]
AUC: 0.8339849059414982
AUC_ind: [0.8740529885435759 0.8616602297958893]
nDCG: [0.9416819711535708]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.63855421686747]
Average feat diff: [11.706024096385542]
Average noise diff: [11.706024096385542]
Average mAP: [0.5955545699907613]
AUC: 0.8099288610498945
AUC_ind: [0.845925647254569  0.8425122225366604]
nDCG: [0.9321669076851835]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.485564304461942]
Average feat diff: [13.727034120734908]
Average noise diff: [13.727034120734908]
Average mAP: [0.5355296132079234]
AUC: 0.7776525726610173
AUC_ind: [0.8219756700726714 0.8034802988567751]
nDCG: [0.9197529075228958]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9752342047450182 0.9393168637395428], [625, 62]
0.1, [0.9344218868585887 0.916723031351898 ], [529, 158]
0.15, [0.9082992613351278 0.8886330048487717], [487, 200]
0.2, [0.8740529885435759 0.8616602297958893], [448, 239]
0.25, [0.845925647254569  0.8425122225366604], [415, 272]
0.3, [0.8219756700726714 0.8034802988567751], [381, 306]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.1618426600281767
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.45881158082065204
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.9125596184419714]
Average feat diff: [1.5103338632750398]
Average noise diff: [1.5103338632750398]
Average mAP: [0.9056430778716886]
AUC: 0.9284392560844028
AUC_ind: [0.9756261105544455 0.9451120811875671]
nDCG: [0.9873915433864929]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.1978221415607986]
Average feat diff: [4.1560798548094375]
Average noise diff: [4.1560798548094375]
Average mAP: [0.792423448880878]
AUC: 0.8922352976415221
AUC_ind: [0.9378851304742818 0.9098901051742218]
nDCG: [0.9696229925787073]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [484, 203] samples with noise 15.0
Average adj diff: [3.7231404958677685]
Average feat diff: [5.921487603305785]
Average noise diff: [5.921487603305785]
Average mAP: [0.7132578518536301]
AUC: 0.8635226220473388
AUC_ind: [0.9049585430514202 0.8875406770087982]
nDCG: [0.9571910158591596]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.966292134831461]
Average feat diff: [8.939325842696629]
Average noise diff: [8.939325842696629]
Average mAP: [0.6495017132902424]
AUC: 0.8352365329964946
AUC_ind: [0.8778461053489329 0.8535707124902546]
nDCG: [0.9451258923707943]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.476744186046512]
Average feat diff: [11.548837209302325]
Average noise diff: [11.548837209302325]
Average mAP: [0.5887238357821653]
AUC: 0.8057496462446778
AUC_ind: [0.8435450153228425 0.8390267584448409]
nDCG: [0.931114674388779]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [379, 308] samples with noise 30.0
Average adj diff: [7.203166226912929]
Average feat diff: [13.408970976253299]
Average noise diff: [13.408970976253299]
Average mAP: [0.551215918158316]
AUC: 0.7827107524455863
AUC_ind: [0.8210105882218475 0.8101184387699212]
nDCG: [0.9222279423855516]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9756261105544455 0.9451120811875671], [629, 58]
0.1, [0.9378851304742818 0.9098901051742218], [551, 136]
0.15, [0.9049585430514202 0.8875406770087982], [484, 203]
0.2, [0.8778461053489329 0.8535707124902546], [445, 242]
0.25, [0.8435450153228425 0.8390267584448409], [430, 257]
0.3, [0.8210105882218475 0.8101184387699212], [379, 308]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer_noldb.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.34855542770472536
 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: 0.5569200773410105
 pos diff: [0.07738561856694617 0.                 ], inv diff: [0.3393993732537071 0.                ], topk inv diff: [0.5293933751367933 0.                ]
 Variance: 0.4189016268050718
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [287.   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: 0.9174519745245697
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 [621, 66] samples with noise 5.0
Average adj diff: [0.7971014492753623]
Average feat diff: [1.5233494363929148]
Average noise diff: [1.5233494363929148]
Average mAP: [0.8829905935689435]
AUC: 0.8873773599955078
AUC_ind: [0.9657018233581716 0.8924097980831814]
nDCG: [0.9851232910237793]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [554, 133] samples with noise 10.0
Average adj diff: [2.283393501805054]
Average feat diff: [3.862815884476534]
Average noise diff: [3.862815884476534]
Average mAP: [0.7553159791952454]
AUC: 0.8520900382045662
AUC_ind: [0.9158056955652979 0.8831561007400331]
nDCG: [0.9668450213852451]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [491, 196] samples with noise 15.0
Average adj diff: [3.645621181262729]
Average feat diff: [6.130346232179226]
Average noise diff: [6.130346232179226]
Average mAP: [0.6779724539636391]
AUC: 0.8247116189733432
AUC_ind: [0.8814590912490139 0.8553388142428061]
nDCG: [0.9528099690477417]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.980997624703088]
Average feat diff: [8.712589073634204]
Average noise diff: [8.712589073634204]
Average mAP: [0.6137334127715092]
AUC: 0.7942981336830521
AUC_ind: [0.8467879705845159 0.8292757955253062]
nDCG: [0.9403789907832708]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.346055979643766]
Average feat diff: [11.414758269720101]
Average noise diff: [11.414758269720101]
Average mAP: [0.5529115378231664]
AUC: 0.7634545918624688
AUC_ind: [0.8165749237920369 0.7970088111385308]
nDCG: [0.9281948408295396]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.65974025974026]
Average feat diff: [13.449350649350649]
Average noise diff: [13.449350649350649]
Average mAP: [0.5305230641582588]
AUC: 0.7468999540223282
AUC_ind: [0.7936337043911126 0.788143912277176 ]
nDCG: [0.9233843116559862]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9657018233581716 0.8924097980831814], [621, 66]
0.1, [0.9158056955652979 0.8831561007400331], [554, 133]
0.15, [0.8814590912490139 0.8553388142428061], [491, 196]
0.2, [0.8467879705845159 0.8292757955253062], [421, 266]
0.25, [0.8165749237920369 0.7970088111385308], [393, 294]
0.3, [0.7936337043911126 0.788143912277176 ], [385, 302]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer_noldb.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.34855542770472536
 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: 0.5569200773410099
 pos diff: [0.07738561856694617 0.                 ], inv diff: [0.3393993732537071 0.                ], topk inv diff: [0.5293933751367933 0.                ]
 Variance: 0.41890162680507154
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [287.   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: 0.9174519745245697
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: [0.8338607594936709]
Average feat diff: [1.4335443037974684]
Average noise diff: [1.4335443037974684]
Average mAP: [0.8898696278791857]
AUC: 0.8927943096382667
AUC_ind: [0.9700979983951165 0.9142397131680253]
nDCG: [0.9869781900058909]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.302367941712204]
Average feat diff: [3.7413479052823315]
Average noise diff: [3.7413479052823315]
Average mAP: [0.7520293254797835]
AUC: 0.8513404449565082
AUC_ind: [0.9139687337072971 0.8874752565502119]
nDCG: [0.964465425803791]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [473, 214] samples with noise 15.0
Average adj diff: [3.6448202959830867]
Average feat diff: [6.101479915433404]
Average noise diff: [6.101479915433404]
Average mAP: [0.6800473983912714]
AUC: 0.8234943362152616
AUC_ind: [0.8835132239811629 0.8553137774037332]
nDCG: [0.954152257428982]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.924444444444444]
Average feat diff: [8.79111111111111]
Average noise diff: [8.79111111111111]
Average mAP: [0.6129384222055174]
AUC: 0.7954054693902133
AUC_ind: [0.8544301778672483 0.8204345798845835]
nDCG: [0.942375566126085]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 25.0
Average adj diff: [6.432304038004751]
Average feat diff: [11.496437054631828]
Average noise diff: [11.496437054631828]
Average mAP: [0.5665528638035305]
AUC: 0.7665604201235526
AUC_ind: [0.813550966678068  0.8056529680841261]
nDCG: [0.9291122994392469]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.613941018766756]
Average feat diff: [13.656836461126005]
Average noise diff: [13.656836461126005]
Average mAP: [0.5258970219332227]
AUC: 0.7511769105339923
AUC_ind: [0.8094055321768678 0.7804343514037169]
nDCG: [0.9236687460479577]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9700979983951165 0.9142397131680253], [632, 55]
0.1, [0.9139687337072971 0.8874752565502119], [549, 138]
0.15, [0.8835132239811629 0.8553137774037332], [473, 214]
0.2, [0.8544301778672483 0.8204345798845835], [450, 237]
0.25, [0.813550966678068  0.8056529680841261], [421, 266]
0.3, [0.8094055321768678 0.7804343514037169], [373, 314]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer_noldb.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.34855542770472536
 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: 0.55692007734101
 pos diff: [0.07738561856694617 0.                 ], inv diff: [0.3393993732537071 0.                ], topk inv diff: [0.5293933751367933 0.                ]
 Variance: 0.41890162680507204
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [287.   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: 0.9174519745245697
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: [0.9181102362204724]
Average feat diff: [1.5748031496062993]
Average noise diff: [1.5748031496062993]
Average mAP: [0.885821276391238]
AUC: 0.890733923408438
AUC_ind: [0.9636283966403545 0.9241108084819504]
nDCG: [0.9856994192907741]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [531, 156] samples with noise 10.0
Average adj diff: [2.3069679849340865]
Average feat diff: [3.792843691148776]
Average noise diff: [3.792843691148776]
Average mAP: [0.7510987112519096]
AUC: 0.8476585779173059
AUC_ind: [0.912730607214307  0.8833489865054909]
nDCG: [0.965852972785006]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.7979381443298967]
Average feat diff: [6.482474226804124]
Average noise diff: [6.482474226804124]
Average mAP: [0.678865871453178]
AUC: 0.8179316465991127
AUC_ind: [0.8753853546329851 0.8564225746129421]
nDCG: [0.9530020757439888]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [4.870283018867925]
Average feat diff: [8.424528301886792]
Average noise diff: [8.424528301886792]
Average mAP: [0.6222474957018541]
AUC: 0.7972061154875743
AUC_ind: [0.8517239967233574 0.8337825791240797]
nDCG: [0.9419614118863763]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 25.0
Average adj diff: [6.449877750611247]
Average feat diff: [11.58435207823961]
Average noise diff: [11.58435207823961]
Average mAP: [0.5585752266341844]
AUC: 0.7663342408352851
AUC_ind: [0.8092030737449127 0.8040046190829849]
nDCG: [0.9265234502181451]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.4847715736040605]
Average feat diff: [13.654822335025381]
Average noise diff: [13.654822335025381]
Average mAP: [0.5385362013365563]
AUC: 0.7492702228999901
AUC_ind: [0.7950270608642832 0.7980872791722171]
nDCG: [0.9244938890934975]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9636283966403545 0.9241108084819504], [635, 52]
0.1, [0.912730607214307  0.8833489865054909], [531, 156]
0.15, [0.8753853546329851 0.8564225746129421], [485, 202]
0.2, [0.8517239967233574 0.8337825791240797], [424, 263]
0.25, [0.8092030737449127 0.8040046190829849], [409, 278]
0.3, [0.7950270608642832 0.7980872791722171], [394, 293]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.5660230472109007
 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: 0.16184266002817702
 pos diff: [0.033972132067145916 0.                  ], inv diff: [0.9235811690749733 0.                ], topk inv diff: [0.6720131257822767 0.                ]
 Variance: 0.4588115808206522
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [192.   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: 0.9534406987363403
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: [0.8857142857142857]
Average feat diff: [1.4285714285714286]
Average noise diff: [1.4285714285714286]
Average mAP: [0.9128964595939385]
AUC: 0.9298340087430865
AUC_ind: [0.9775925025230132 0.9440576417009579]
nDCG: [0.9884693891784999]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2304900181488203]
Average feat diff: [3.5245009074410163]
Average noise diff: [3.5245009074410163]
Average mAP: [0.8056479822672341]
AUC: 0.8968106021372109
AUC_ind: [0.9433635903798628 0.9096670725031718]
nDCG: [0.9722205646261135]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [493, 194] samples with noise 15.0
Average adj diff: [3.383367139959432]
Average feat diff: [6.210953346855984]
Average noise diff: [6.210953346855984]
Average mAP: [0.7081398022298394]
AUC: 0.8656855933669088
AUC_ind: [0.9063218038274585 0.8841711187375131]
nDCG: [0.9547155246678889]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [5.183908045977011]
Average feat diff: [9.144827586206896]
Average noise diff: [9.144827586206896]
Average mAP: [0.6405286051235415]
AUC: 0.8304741238574376
AUC_ind: [0.8730924488479495 0.8593719440413212]
nDCG: [0.9415903825887451]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.379652605459057]
Average feat diff: [11.394540942928039]
Average noise diff: [11.394540942928039]
Average mAP: [0.5924959494580846]
AUC: 0.8072745116636848
AUC_ind: [0.8471482804553235 0.8356820065585236]
nDCG: [0.9334272975088603]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.3813131313131315]
Average feat diff: [13.45959595959596]
Average noise diff: [13.45959595959596]
Average mAP: [0.5491181072637502]
AUC: 0.7846359219916056
AUC_ind: [0.8237677428505633 0.8079335298750253]
nDCG: [0.9211320327207798]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9775925025230132 0.9440576417009579], [630, 57]
0.1, [0.9433635903798628 0.9096670725031718], [551, 136]
0.15, [0.9063218038274585 0.8841711187375131], [493, 194]
0.2, [0.8730924488479495 0.8593719440413212], [435, 252]
0.25, [0.8471482804553235 0.8356820065585236], [403, 284]
0.3, [0.8237677428505633 0.8079335298750253], [396, 291]
 
 SUMMARY 

 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ./ckpt/Mutagenicity/RCExplainer/rcexplainer_noldb.pth.tar
 explainer params sum: 0.0, model params sum: 0.0
 use comb: True,  size cf: -1.0, ent cf -1.0
 ROC AUC score: 0.34855542770472536
 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: 0.5569200773410101
 pos diff: [0.07738561856694617 0.                 ], inv diff: [0.3393993732537071 0.                ], topk inv diff: [0.5293933751367933 0.                ]
 Variance: 0.41890162680507187
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [287.   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: 0.9174519745245697
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: [0.8820678513731826]
Average feat diff: [1.4991922455573505]
Average noise diff: [1.4991922455573505]
Average mAP: [0.8840039745993403]
AUC: 0.8894829843412487
AUC_ind: [0.9658156167364091 0.9173113490044337]
nDCG: [0.9856330770184037]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.2775735294117645]
Average feat diff: [3.8419117647058822]
Average noise diff: [3.8419117647058822]
Average mAP: [0.760081100186502]
AUC: 0.8546860369761169
AUC_ind: [0.9180486858790479 0.8892783002691228]
nDCG: [0.9671525641055692]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [3.740899357601713]
Average feat diff: [6.04710920770878]
Average noise diff: [6.04710920770878]
Average mAP: [0.6682081964994956]
AUC: 0.8193981923471032
AUC_ind: [0.8885471987393592 0.8284133603140588]
nDCG: [0.952542425293391]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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 20.0
Average adj diff: [5.0120481927710845]
Average feat diff: [8.64578313253012]
Average noise diff: [8.64578313253012]
Average mAP: [0.6283522702745886]
AUC: 0.7974973539847854
AUC_ind: [0.8524417308817067 0.8408567999489872]
nDCG: [0.9411334771208273]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.633971291866029]
Average feat diff: [11.55023923444976]
Average noise diff: [11.55023923444976]
Average mAP: [0.5657209164895947]
AUC: 0.7703810502615934
AUC_ind: [0.8181619737702628 0.8073019980186175]
nDCG: [0.9310363584375325]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [7.344913151364764]
Average feat diff: [13.712158808933003]
Average noise diff: [13.712158808933003]
Average mAP: [0.5133358012808139]
AUC: 0.7402974799762798
AUC_ind: [0.7896452954239799 0.7798220984702193]
nDCG: [0.9176583911601163]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9658156167364091 0.9173113490044337], [619, 68]
0.1, [0.9180486858790479 0.8892783002691228], [544, 143]
0.15, [0.8885471987393592 0.8284133603140588], [467, 220]
0.2, [0.8524417308817067 0.8408567999489872], [415, 272]
0.25, [0.8181619737702628 0.8073019980186175], [418, 269]
0.3, [0.7896452954239799 0.7798220984702193], [403, 284]
 
 SUMMARY 
