
 
 
 Mutagenicity
 method: rcnoiseexplainer
 bloss version: sigmoid
 ckpt dir: ckpt/Mutagenicity
 exp_path: ckpt/Mutagenicity/RCExplainer/rcexp_noldbexplainer_Mutagenicity_pgeboundary_ep_0.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: 0.3277652284199344
 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: 0.5057043562361847
 pos diff: [0.7637301990559008 0.                ], inv diff: [0.7577439691909362 0.                ], topk inv diff: [0.07469885018715171 0.                 ]
 Variance: 0.007298249103240974
 flips: [687.   0.], Inv flips: [687.   0.], topk: 8.0, topk Inv flips: [622.   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: 0.8413823689894857
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: [0.8798076923076923]
Average feat diff: [1.4903846153846154]
Average noise diff: [1.4903846153846154]
Average mAP: [0.8176221971209561]
AUC: 0.787428357112131
AUC_ind: [0.9248184028751177 0.8088018046543015]
nDCG: [0.9689770790132219]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [2.354898336414048]
Average feat diff: [3.722735674676525]
Average noise diff: [3.722735674676525]
Average mAP: [0.6321698675926741]
AUC: 0.717127605794349
AUC_ind: [0.8227102145707307 0.7521922813081579]
nDCG: [0.9323910339574617]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [468, 219] samples with noise 15.0
Average adj diff: [3.6688034188034186]
Average feat diff: [6.3418803418803416]
Average noise diff: [6.3418803418803416]
Average mAP: [0.525342469096775]
AUC: 0.6758088369000699
AUC_ind: [0.7548047338522011 0.7075763517969561]
nDCG: [0.9015824037906998]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [423, 264] samples with noise 20.0
Average adj diff: [5.137115839243499]
Average feat diff: [8.6903073286052]
Average noise diff: [8.6903073286052]
Average mAP: [0.44103915601339827]
AUC: 0.619730422941285
AUC_ind: [0.6911574553164991 0.646294706556688 ]
nDCG: [0.8805086269173582]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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: [6.315136476426799]
Average feat diff: [11.384615384615385]
Average noise diff: [11.384615384615385]
Average mAP: [0.41621886807726355]
AUC: 0.6094310127673213
AUC_ind: [0.6603080538629846 0.657997300241332 ]
nDCG: [0.8694163441805529]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
Fidelity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,

Evaluted [389, 298] samples with noise 30.0
Average adj diff: [7.812339331619537]
Average feat diff: [13.789203084832906]
Average noise diff: [13.789203084832906]
Average mAP: [0.38529766721272235]
AUC: 0.591962502128664
AUC_ind: [0.6375655068949369 0.6295578028542402]
nDCG: [0.8610206547227652]
Reporting statistics
Samples: 0
top 4 acc: 0.0	 top 6 acc: 0.0	 top 8 acc: 0.0
Edge fidelity prediction change: [0. 0. 0. 0. 0. 0. 0. 0. 0.]
 Edge fidelity confidence change: [0. 0. 0. 0. 0. 0. 0. 0. 0.] for sparsity [0. 0. 0. 0. 0. 0. 0. 0. 0.]
Mask density: [0.]
Sparsity, 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,
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, [0.9248184028751177 0.8088018046543015], [624, 63]
0.1, [0.8227102145707307 0.7521922813081579], [541, 146]
0.15, [0.7548047338522011 0.7075763517969561], [468, 219]
0.2, [0.6911574553164991 0.646294706556688 ], [423, 264]
0.25, [0.6603080538629846 0.657997300241332 ], [403, 284]
0.3, [0.6375655068949369 0.6295578028542402], [389, 298]
 
 SUMMARY 
