From 2d27abc35026456de765c929dbcf0503a72ac572 Mon Sep 17 00:00:00 2001 From: willtanoe Date: Sun, 27 Sep 2026 15:12:58 +0700 Subject: [PATCH] experiment: complete xiiotid three-alpha beta study --- ...lpha0.75__multiseed__beta0_equivalence.csv | 4 + ...5__multiseed__client_validation_deltas.csv | 151 + ...5__multiseed__client_validation_detail.csv | 151 + ...__multiseed__client_validation_summary.csv | 16 + ...75__multiseed__prototype_effect_pooled.csv | 46 + ...xiiotid__alpha0.75__multiseed__runtime.csv | 19 + ...pha0.75__multiseed__selection_summary.json | 111 + ...alpha0.75__multiseed__test_descriptive.csv | 19 + ...ha0.75__multiseed__validation_per_seed.csv | 16 + ...lpha0.75__multiseed__validation_pooled.csv | 6 + ...t_weighted__beta0__prototype_snapshot.json | 11444 +++++++++++++++ ...__prototype_snapshot__prototype_effect.csv | 46 + ..._prototype_snapshot__prototype_weights.csv | 436 + ...t_weighted__beta0__prototype_snapshot.json | 11706 +++++++++++++++ ...__prototype_snapshot__prototype_effect.csv | 46 + ..._prototype_snapshot__prototype_weights.csv | 446 + ...t_weighted__beta0__prototype_snapshot.json | 11837 ++++++++++++++++ ...__prototype_snapshot__prototype_effect.csv | 46 + ..._prototype_snapshot__prototype_weights.csv | 451 + ...d__cross_alpha__cross_alpha_beta_table.csv | 16 + ...ross_alpha__cross_alpha_client_summary.csv | 46 + ..._cross_alpha__cross_alpha_displacement.csv | 136 + ...tid__cross_alpha__cross_alpha_summary.json | 78 + ..._cross_alpha__partition_heterogeneity.json | 299 + ...pha__partition_heterogeneity_per_class.csv | 82 + ...lpha__partition_heterogeneity_per_seed.csv | 10 + ...alpha__partition_heterogeneity_summary.csv | 4 + docs/handoff/SESSION_HANDOFF.md | 463 +- docs/research/ALPHA075_BETA_VALIDATION.md | 422 + docs/research/SESSION8_AUDIT.md | 268 + docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md | 223 + scripts/analyze_cross_alpha.py | 230 + scripts/analyze_partition_heterogeneity.py | 213 + scripts/verify_beta0_equivalence.py | 209 + 34 files changed, 39475 insertions(+), 221 deletions(-) create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__beta0_equivalence.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__client_validation_deltas.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__client_validation_detail.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__client_validation_summary.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__prototype_effect_pooled.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__runtime.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__selection_summary.json create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__test_descriptive.csv create mode 100644 artifacts/analysis/xiiotid__alpha0.75__multiseed__validation_per_seed.csv create mode 100644 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+6,Reconnaissance,0.25,10,458,23824,8916.1,5.660090963965236,5.4315535155841825,0.5376024764747891,0.09897766356021405 +6,Reconnaissance,0.5,10,458,23824,8916.1,5.875435929367915,5.4315535155841825,0.9554020858885306,0.17589849444496797 +6,Reconnaissance,0.75,10,458,23824,8916.1,6.050569988897634,5.4315535155841825,1.2477469868993738,0.22972193559712617 +6,Reconnaissance,1.0,10,458,23824,8916.1,6.182111898039249,5.4315535155841825,1.4403595556867412,0.2651837179830908 +7,Tampering,0.0,10,31,1227,358.6,4.706517869218987,4.706517869218987,0.0,0.0 +7,Tampering,0.25,10,31,1227,358.6,4.732695239198885,4.706517869218987,0.4954736962043322,0.10527394349116802 +7,Tampering,0.5,10,31,1227,358.6,4.800394814968305,4.706517869218987,0.9972201076239648,0.21188065897844904 +7,Tampering,0.75,10,31,1227,358.6,4.894369256116969,4.706517869218987,1.4405774092634833,0.3060813640345398 +7,Tampering,1.0,10,31,1227,358.6,4.988010172720971,4.706517869218987,1.7857948642169272,0.37943016766093085 +8,Weaponization,0.0,10,285,12915,4509.4,6.188427323891787,6.188427323891787,0.0,0.0 +8,Weaponization,0.25,10,285,12915,4509.4,6.406125706681027,6.188427323891787,0.47266743210924217,0.07637924910007514 +8,Weaponization,0.5,10,285,12915,4509.4,6.600258090509457,6.188427323891787,0.8725595523209223,0.14099859409388454 +8,Weaponization,0.75,10,285,12915,4509.4,6.763071657325928,6.188427323891787,1.1938030191978883,0.1929089503223135 +8,Weaponization,1.0,10,285,12915,4509.4,6.892762983819524,6.188427323891787,1.4401830467817407,0.2327219778798398 diff --git a/artifacts/analysis/xiiotid__alpha0.75__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv b/artifacts/analysis/xiiotid__alpha0.75__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv new file mode 100644 index 0000000..3ccb977 --- /dev/null +++ b/artifacts/analysis/xiiotid__alpha0.75__seed0__support_weighted__beta0__prototype_snapshot__prototype_weights.csv @@ -0,0 +1,436 @@ +class_id,class_name,beta,client_id,N_ij,weight +0,C&C,0.0,0,196.0,0.125 +0,C&C,0.0,1,387.0,0.125 +0,C&C,0.0,3,229.0,0.125 +0,C&C,0.0,5,760.0,0.125 +0,C&C,0.0,6,179.0,0.125 +0,C&C,0.0,7,215.0,0.125 +0,C&C,0.0,8,5.0,0.125 +0,C&C,0.0,9,33.0,0.125 +0,C&C,0.25,0,196.0,0.1303862851887575 +0,C&C,0.25,1,387.0,0.15455947558098102 +0,C&C,0.25,3,229.0,0.13555850320497126 +0,C&C,0.25,5,760.0,0.182966378613902 +0,C&C,0.25,6,179.0,0.12746212423991282 +0,C&C,0.25,7,215.0,0.13343738095108978 +0,C&C,0.25,8,5.0,0.052108718808452095 +0,C&C,0.25,9,33.0,0.08352113341193354 +0,C&C,0.5,0,196.0,0.12455986315218183 +0,C&C,0.5,1,387.0,0.17502720968912192 +0,C&C,0.5,3,229.0,0.13463805462151734 +0,C&C,0.5,5,760.0,0.2452770323204801 +0,C&C,0.5,6,179.0,0.11903552788163703 +0,C&C,0.5,7,215.0,0.13045757959232882 +0,C&C,0.5,8,5.0,0.019894594376882128 +0,C&C,0.5,9,33.0,0.05111013836585088 +0,C&C,0.75,0,196.0,0.11254353491012732 +0,C&C,0.75,1,387.0,0.1874613421805779 +0,C&C,0.75,3,229.0,0.12647512291249882 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@@ +dataset,alpha,beta,n_seeds,pooled_val_mean,pooled_val_std_sample,pooled_val_std_pop,pooled_val_min,pooled_val_max,pooled_val_recall,selected,cells_sj_lt_M_total,cells_total,missing_client_class_cells_total,client_size_ratio_mean,support_cv_mean,union_unseen_mean,mean_displacement_beta1 +xiiotid,0.25,0.0,3,0.5917038146634539,0.08952883653922933,0.07309998892871802,0.4924247362191584,0.666306546980769,0.7594821244948339,True,19,27,38,14.745270966890343,1.4423832412721973,5.666666666666667,3.4221253316893105 +xiiotid,0.25,0.25,3,0.5886906256417319,0.06643525635398827,0.05424415966608841,0.5142371025483052,0.641922010303426,0.7807713039840465,False,19,27,38,14.745270966890343,1.4423832412721973,5.666666666666667,3.4221253316893105 +xiiotid,0.25,0.5,3,0.5858514801619258,0.044857849968223555,0.03662628112682344,0.5370398599938551,0.625267153876176,0.7725355473131942,False,19,27,38,14.745270966890343,1.4423832412721973,5.666666666666667,3.4221253316893105 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new file mode 100644 index 0000000..b16a1dd --- /dev/null +++ b/artifacts/analysis/xiiotid__cross_alpha__cross_alpha_client_summary.csv @@ -0,0 +1,46 @@ +dataset,alpha,seed,beta,n_eligible,n_improve,n_degrade,n_unchanged,mean_delta,min_delta,max_delta +xiiotid,0.25,0,0.0,10,0,0,10,0.0,0.0,0.0 +xiiotid,0.25,0,0.25,10,2,5,3,-0.0064673967169698865,-0.04293419597212661,0.01876993547229222 +xiiotid,0.25,0,0.5,10,2,7,1,-0.021132734174687993,-0.07692307692307698,0.05741836974395109 +xiiotid,0.25,0,0.75,10,5,4,1,0.037652802750874605,-0.11166156370194247,0.3758987633016968 +xiiotid,0.25,0,1.0,10,4,5,1,-0.037995631629473454,-0.25698055252395446,0.14104421200350115 +xiiotid,0.25,1,0.0,10,0,0,10,0.0,0.0,0.0 +xiiotid,0.25,1,0.25,10,7,1,2,0.02181236632914685,-0.006350012700025401,0.07175976877899254 +xiiotid,0.25,1,0.5,10,7,2,1,0.04461512377469677,-0.015820270172650433,0.23562664832014674 +xiiotid,0.25,1,0.75,10,5,4,1,0.04324763034707273,-0.018266942973367145,0.316706904876871 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a/artifacts/analysis/xiiotid__cross_alpha__cross_alpha_displacement.csv b/artifacts/analysis/xiiotid__cross_alpha__cross_alpha_displacement.csv new file mode 100644 index 0000000..6dba3dc --- /dev/null +++ b/artifacts/analysis/xiiotid__cross_alpha__cross_alpha_displacement.csv @@ -0,0 +1,136 @@ +dataset,alpha,class_id,class_name,beta,n_seeds,mean_displacement,std_sample_displacement,mean_relative_displacement +xiiotid,0.25,0,C&C,0.0,3,0.0,0.0,0.0 +xiiotid,0.25,0,C&C,0.25,3,1.27837054967781,0.41822833159420114,0.27003430350349095 +xiiotid,0.25,0,C&C,0.5,3,2.3990142795607627,0.581298481867443,0.5059337917781374 +xiiotid,0.25,0,C&C,0.75,3,3.306991238919704,0.508900823524494,0.6970245995294005 +xiiotid,0.25,0,C&C,1.0,3,4.01952572799183,0.29030488023104745,0.8472326753379348 +xiiotid,0.25,1,Exfiltration,0.0,3,0.0,0.0,0.0 +xiiotid,0.25,1,Exfiltration,0.25,3,2.1505748980950266,0.3101835560753807,0.33309243067039757 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+xiiotid,0.25,0,8,Weaponization,45094,8,2,43,23381,5636.75,8611.380141272362,1.527720786139595,543.7441860465116 +xiiotid,0.25,1,0,C&C,2004,9,1,1,986,222.66666666666666,347.19286987046394,1.559249415585916,986.0 +xiiotid,0.25,1,1,Exfiltration,15494,10,0,1,6204,1549.4,1918.3846955186025,1.238146828139023,6204.0 +xiiotid,0.25,1,2,Exploitation,793,8,2,1,321,99.125,128.38558865776173,1.2951887884767892,321.0 +xiiotid,0.25,1,3,Lateral_movement,22117,10,0,1,10094,2211.7,3172.3787305427454,1.4343621334461027,10094.0 +xiiotid,0.25,1,4,Normal,292777,8,2,198,129678,36597.125,42579.4751242823,1.16346502967876,654.939393939394 +xiiotid,0.25,1,5,RDOS,98883,9,1,168,53482,10987.0,16131.251346377318,1.4682125554179775,318.3452380952381 +xiiotid,0.25,1,6,Reconnaissance,89161,8,2,138,65101,11145.125,20636.12875176386,1.8515834278901186,471.7463768115942 +xiiotid,0.25,1,7,Tampering,3586,9,1,2,1804,398.44444444444446,536.9212876537887,1.3475436667273,902.0 +xiiotid,0.25,1,8,Weaponization,45094,9,1,1,12024,5010.444444444444,4173.732332127554,0.8330064085942251,12024.0 +xiiotid,0.25,2,0,C&C,2004,6,4,2,1265,334.0,439.2504980076858,1.3151212515200172,632.5 +xiiotid,0.25,2,1,Exfiltration,15494,8,2,4,8269,1936.75,2589.4560118874388,1.3370109781269852,2067.25 +xiiotid,0.25,2,2,Exploitation,793,6,4,1,485,132.16666666666666,175.5395650242101,1.3281682095148306,485.0 +xiiotid,0.25,2,3,Lateral_movement,22117,7,3,1,12496,3159.5714285714284,4660.178716902324,1.4749401373747013,12496.0 +xiiotid,0.25,2,4,Normal,292777,9,1,452,118937,32530.777777777777,35898.97737255285,1.1035388584245882,263.13495575221236 +xiiotid,0.25,2,5,RDOS,98883,10,0,2,48839,9888.3,14049.173043634988,1.4207875007468411,24419.5 +xiiotid,0.25,2,6,Reconnaissance,89161,10,0,1,46541,8916.1,13537.102034408988,1.5182761559884914,46541.0 +xiiotid,0.25,2,7,Tampering,3586,10,0,1,2584,358.6,765.2960472915041,2.1341217158156836,2584.0 +xiiotid,0.25,2,8,Weaponization,45094,9,1,4,22853,5010.444444444444,7371.102105310547,1.4711473576927068,5713.25 diff --git a/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_per_seed.csv b/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_per_seed.csv new file mode 100644 index 0000000..20d36ab --- /dev/null +++ b/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_per_seed.csv @@ -0,0 +1,10 @@ +dataset,alpha,seed,num_clients,num_classes,num_train,nij_sum,n_class_condition_cells,n_cells_sj_lt_M,n_missing_client_class_cells,client_size_min,client_size_max,client_size_ratio,union_rare_count,union_unseen_count,max_unseen_on_client,n_clients_2_rare,support_cv_mean,support_cv_max,support_max_min_ratio_max,manifest_content_hash,partition_hash,split_hash,rng_seed +xiiotid,0.75,0,10,9,569909,569909,9,2,3,24236,100573,4.149735930021456,4,2,1,10,0.9158166599996324,1.1761913516072886,240.0,759f7e504a7d442101238726334d121a746ea4a8ff366428c1c174e47f358723,473b1c4aaaa9e55e5cc2d108a261345e6630fe481630987df60a5ac30f793048,d0acc0c14e3ecbeba02dfa9e6227c983da12dec2d6111df0b714002e6e03c29d,957653162 +xiiotid,0.75,1,10,9,569909,569909,9,1,1,21623,109690,5.07283910650696,5,1,1,10,0.8079457241747787,0.9968878931715411,127.05597014925372,4a50ba219de20af14f6ad73976111659fba2d5b25cd5eeb1a2f21b76de0d9303,a0ec9e9c0902c64b12bbfdb2c2ec1ee814a83699b2d48c88b7edd6255aa90022,880a9f82686bbf3af83880238aeef6f1462881ba87c5ae667b7415eb10d79892,762050403 +xiiotid,0.75,2,10,9,569909,569909,9,0,0,24224,115896,4.784346103038309,6,0,0,10,0.9182087571099508,1.3221152695060867,1903.0,922b7bb15169302be7dd4d4eb1644d077537872e1e91fe59b7525b7e3343cc1c,00345255053affa05ebf348cee3efd6dc85ede6c4e500de881b9d96605d99b0e,8e0db0be53055317b4df53398fad500c1a3f09491967539bf5459ba5600cee09,843543396 +xiiotid,0.5,0,10,9,569909,569909,9,3,4,18521,94564,5.105771826575239,6,3,2,10,1.248838795111587,2.0951833233286665,1800.5,e81a6538ecefcda202aa7602310ff8d8c9529031ab9dcbec4ebbabc0e63b636c,2465485539a3e923548dcc0c73d8acc5076d456536b78682d512897ea586e779,d0acc0c14e3ecbeba02dfa9e6227c983da12dec2d6111df0b714002e6e03c29d,2009062324 +xiiotid,0.5,1,10,9,569909,569909,9,3,4,16884,121896,7.219616204690832,7,3,2,10,1.1049783022262067,1.6430117091261995,7879.333333333333,54ea1df16a2e7998cc21c3cee86318dc02a2fd8396894371ebaab7f87bad7e98,4061c5a15ff5c981cb0ae90beb892ad2ef730fb460a995d80fcef77bbcba52a3,880a9f82686bbf3af83880238aeef6f1462881ba87c5ae667b7415eb10d79892,621679668 +xiiotid,0.5,2,10,9,569909,569909,9,1,1,10091,99807,9.890694678426321,7,1,1,10,1.1961862479265188,1.9477820757600992,12215.0,be881ac63bc0057eca7c82d762d082344e1f4fbd1dff56a475898c6de977712a,1118e7fa4e7d852e38b1b8a6e2789c5c6e357579698de7497a4436d02869e214,8e0db0be53055317b4df53398fad500c1a3f09491967539bf5459ba5600cee09,1438483982 +xiiotid,0.25,0,10,9,569909,569909,9,6,13,9075,141358,15.576639118457301,8,6,3,10,1.5167196772431413,1.8552062040219504,122800.0,839ba18e2d87245d198be8f5f89d4a24f13cdb6fc64c004d406c6d4f8246f3da,60faf299c0220bb6dd013fd4621babe8f6ad342e3012b983bce11445252a2b21,d0acc0c14e3ecbeba02dfa9e6227c983da12dec2d6111df0b714002e6e03c29d,1617371214 +xiiotid,0.25,1,10,9,569909,569909,9,7,10,9278,193326,20.837033843500755,7,6,4,10,1.3545286948840236,1.8515834278901186,12024.0,84c20b4bd6cd20010fa35d139a0416cb06e79c82fbd8e1d57441cafdcdfbf15d,6ffb10aec991b9d440bad675b72f07d0807c73fa334f86b284eeb90df31687aa,880a9f82686bbf3af83880238aeef6f1462881ba87c5ae667b7415eb10d79892,1670706564 +xiiotid,0.25,2,10,9,569909,569909,9,6,15,15664,122526,7.822139938712972,8,5,3,10,1.4559013516894272,2.1341217158156836,46541.0,d6e68ee13cb11f98cfb4aea668f3e905d3ebc94884632352c0045fc5f86df28c,c8e1ad316272b858d61b083cd65a174ed674f37157c24d06efb713960b710996,8e0db0be53055317b4df53398fad500c1a3f09491967539bf5459ba5600cee09,795318573 diff --git a/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_summary.csv b/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_summary.csv new file mode 100644 index 0000000..570c2fd --- /dev/null +++ b/artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_summary.csv @@ -0,0 +1,4 @@ +dataset,alpha,n_seeds,cells_sj_lt_M_total,cells_total,missing_client_class_cells_total,client_size_ratio_mean,client_size_ratio_max,union_unseen_mean,max_unseen_on_client,union_rare_mean,support_cv_mean,support_max_min_ratio_max +xiiotid,0.75,3,3,27,4,4.668973713188908,5.07283910650696,1.0,1,5.0,0.8806570470947873,1903.0 +xiiotid,0.5,3,7,27,9,7.405360903230798,9.890694678426321,2.3333333333333335,2,6.666666666666667,1.1833344484214374,12215.0 +xiiotid,0.25,3,19,27,38,14.745270966890343,20.837033843500755,5.666666666666667,4,7.666666666666667,1.4423832412721973,122800.0 diff --git a/docs/handoff/SESSION_HANDOFF.md b/docs/handoff/SESSION_HANDOFF.md index bc52a03..dadbf48 100644 --- a/docs/handoff/SESSION_HANDOFF.md +++ b/docs/handoff/SESSION_HANDOFF.md @@ -3,34 +3,33 @@ This is the canonical continuation point for the next agent session. It is written to be readable without prior chat history, terminal scrollback, or git history. -Last updated: 2026-09-26 (Session 7, post-experiment audit complete). Session 7 type: -**protocol-scaling / non-IID-severity experiment**. Session 6 completed X-IIoTID `alpha=0.50` -for seeds `{0,1,2}`. Session 7 completed X-IIoTID **`alpha=0.25`** for seeds `{0,1,2}` across -the five frozen beta candidates plus `original_protean`, using the **same pooled-across-seed -validation-only** selection rule. **alpha=0.75 and 5G-NIDD were not run.** No commit or push -was made. +Last updated: 2026-09-26 (Session 8). Session 8 type: **protocol-scaling / +non-IID-severity experiment**. Session 7 completed X-IIoTID `alpha=0.25`; Session 6 completed +`alpha=0.50`. Session 8 completed X-IIoTID **`alpha=0.75`** for seeds `{0,1,2}` across the five +frozen beta candidates plus `original_protean`, using the **same pooled-across-seed +validation-only** selection rule, and produced the first **three-alpha (0.25/0.50/0.75) +comparison** for X-IIoTID. **5G-NIDD was not run.** No commit or push was made. -> **Audit status (Session 7).** Independent audit verdict: **PASS WITH MINOR CORRECTIONS** -> (`docs/research/SESSION7_AUDIT.md`). `beta_star(alpha=0.25) = 0.00` confirmed from validation -> only; leakage audit PASS; `beta=0` class **B**; source/data integrity PASS; pytest 75 passed, -> 0 failed, 0 skipped, 0 warnings. Corrections were documentation-only (cross-alpha label -> disambiguation + one wording nuance); no `beta_star` or scientific interpretation changed. -> **Session 7 is safe to checkpoint.** +> **Audit status (Session 8).** Independent audit verdict: **PASS** (`docs/research/SESSION8_AUDIT.md`). +> `beta_star(alpha=0.75) = 0.50` confirmed from validation only; no corrections required; run +> inventory 18/18; pairing hash-identical and reproduced from raw data; leakage audit PASS; +> `beta=0` class **B**; source/data integrity PASS; pytest 75 passed. **Session 8 is safe to +> checkpoint.** -> **Headline (Session 7).** PRIMARY pooled-validation selected beta for X-IIoTID -> `alpha=0.25` = **0.00** (validation only). The pooled validation curve is monotonically -> **decreasing** in beta at alpha=0.25, whereas at alpha=0.50 it peaked at 0.75. Per-seed -> diagnostics were 0.75 / 0.50 / 0.00. beta=0 is class **B** (semantically equivalent + -> floating-point divergence) for all three seeds; no semantic discrepancy. The realized -> alpha=0.25 partitions are materially more heterogeneous (19/27 class-condition cells with -> `|S_j| < M` vs 7/27 at alpha=0.50). Effect remains small relative to between-seed variance. +> **Headline (Session 8).** PRIMARY pooled-validation selected beta for X-IIoTID `alpha=0.75` +> = **0.50** (validation only; per-seed diagnostics 0.50 / 0.50 / 0.00). The pooled validation +> curve peaks at 0.50 and declines both sides, but the total between-beta spread (≈ 0.0081) is +> much smaller than the between-seed std (≈ 0.045). `beta=0` is class **B** for all three seeds. +> Realized alpha=0.75 partitions are the mildest of the three (3/27 cells with `|S_j| < M`, +> 4 missing cells, client-ratio mean 4.67). **Three-alpha pooled selections: 0.25 → 0.00, +> 0.50 → 0.75, 0.75 → 0.50 — not monotone in alpha.** --- ## 1. Project objective -Build the code and experiment infrastructure for a thesis study that modifies **only the -server-side prototype aggregation** of PROTEAN (Chennoufi et al., 2025), a federated +Build the code and experiment infrastructure for a thesis study that modifies **only** the +server-side prototype aggregation of PROTEAN (Chennoufi et al., 2025), a federated intrusion-detection framework. The mechanism weights each contributing client's class prototype by its class sample support: @@ -55,327 +54,350 @@ bitwise identical to the baseline (see §7). `~/datasets/5G-NIDD/5G-NIDD.csv` `fa36f80859585f474504ca69eb951a079b35145537976c86b00aae3aab46ee59`. - Frozen baseline (`original_protean`) source unchanged; no `equal_available` mode added; - beta candidate set unchanged (`{0, .25, .5, .75, 1}`). + beta candidate set unchanged (`{0, .25, .5, .75, 1}`). No tracked source/test/config file + differs from HEAD. -## 3. Session 7 objective and outcome +## 3. Session 8 objective and outcome -Objective: evaluate one additional non-IID severity (X-IIoTID `alpha=0.25`, seeds `{0,1,2}`) -across the five beta candidates using the exact Session-6 three-seed pooled-validation -protocol, and test whether the pooled-selected beta changes under a more heterogeneous -condition. All 18 runs (15 `support_weighted` + 3 `original_protean`) completed with rc=0. -Report: `docs/research/ALPHA025_BETA_VALIDATION.md`. +Objective: evaluate the final X-IIoTID non-IID severity (`alpha=0.75`, seeds `{0,1,2}`) across +the five beta candidates using the exact pooled-validation protocol, then compare alpha=0.25, +0.50 and 0.75 using realized partition characteristics. All 18 runs (15 `support_weighted` + +3 `original_protean`) completed with rc=0. -Outcome: **PRIMARY pooled-validation selected beta = 0.00** (validation only); -`test_used_for_selection = false` for all 15 support_weighted runs. Pooled means: 0.00 → -0.591704, 0.25 → 0.588691, 0.50 → 0.585851, 0.75 → 0.579463, 1.00 → 0.532099 (monotone -decreasing). Between-seed std 0.045–0.090; the effect is small relative to seed variance. +Outcome: **PRIMARY pooled-validation selected beta = 0.50** (validation only); +`test_used_for_selection = false` for all 15 runs. Pooled means: 0.00 → 0.471706, +0.25 → 0.474368, 0.50 → 0.476749, 0.75 → 0.472541, 1.00 → 0.468605 (peak at 0.50). Between-seed +std 0.0447–0.0553; between-beta spread ≈ 0.0081, so the effect is small relative to seed +variance. + +Reports: `docs/research/ALPHA075_BETA_VALIDATION.md`, +`docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md`, audit `docs/research/SESSION8_AUDIT.md`. ## 4. Condition and pairing | Field | Value | |---|---| -| dataset / alpha / seeds | `xiiotid` / 0.25 / `{0,1,2}` | +| dataset / alpha / seeds | `xiiotid` / 0.75 / `{0,1,2}` | | runs | 15 `support_weighted` + 3 `original_protean`, all new this session | | training | 10 clients, 10 rounds, 3 local epochs, batch 256, lr 1e-3, mu 0.1, lambda 1.0, Adam | -| code revision | unchanged from HEAD `6b97a0d38b839c737c49f41006817503d0672871`; only docs/analysis artifacts added | +| code revision | unchanged from HEAD `ff68d380e23859d21c07c8a3a7b56b1b7f06c91b`; only docs/analysis/scripts added | -Manifests (reused, never regenerated; one content hash per seed across all six runs; each was -reproduced from the raw dataset with split/`N_ij`/partition hashes verified): +Manifests (reused, never regenerated; one content hash per seed across all six runs; each +reproduced from the raw dataset with split/`N_ij`/partition hash verified): | seed | manifest | content hash | partition hash | split hash | rng seed | |---|---|---|---|---|---| -| 0 | `artifacts/partitions/xiiotid__alpha0.25__seed0.json` | `839ba18e…` | `60faf299…` | `d0acc0c1…` | 1617371214 | -| 1 | `artifacts/partitions/xiiotid__alpha0.25__seed1.json` | `84c20b4b…` | `6ffb10ae…` | `880a9f82…` | 1670706564 | -| 2 | `artifacts/partitions/xiiotid__alpha0.25__seed2.json` | `d6e68ee1…` | `c8e1ad31…` | `8e0db0be…` | 795318573 | +| 0 | `artifacts/partitions/xiiotid__alpha0.75__seed0.json` | `759f7e50…` | `473b1c4a…` | `d0acc0c1…` | 957653162 | +| 1 | `artifacts/partitions/xiiotid__alpha0.75__seed1.json` | `4a50ba21…` | `a0ec9e9c…` | `880a9f82…` | 762050403 | +| 2 | `artifacts/partitions/xiiotid__alpha0.75__seed2.json` | `922b7bb1…` | `00345255…` | `8e0db0be…` | 843543396 | -Pairing proof: round-0 snapshot byte-identical across the five betas **and** -`original_protean` within each seed — seed 0 `cd821beb…`, seed 1 `59d2d2bc…`, seed 2 -`47befeff…`. `train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv` identical -across each seed's runs. `manifest_reload_ok = true`, `smoke = false` everywhere. +Pairing proof: round-0 snapshot byte-identical across the five betas **and** `original_protean` +within each seed — seed 0 `8c477df6…`, seed 1 `41f0187c…`, seed 2 `be2ad5fb…`. +`train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv` identical across each seed's +runs. `manifest_reload_ok = true`, `smoke = false` everywhere. ## 5. Validation results (USED FOR SELECTION) and pooled selection Primary metric: validation per-client-mean rare-class macro-F1. All 10 clients eligible in -every seed. Full tables: `artifacts/analysis/xiiotid__alpha0.25__multiseed__validation_per_seed.csv`. +every seed. Full tables: +`artifacts/analysis/xiiotid__alpha0.75__multiseed__validation_per_seed.csv`. Per-seed validation per-client-mean rare-class macro-F1: | beta | seed 0 | seed 1 | seed 2 | |---|---|---|---| -| 0.00 | 0.616380 | 0.492425 | 0.666307 | -| 0.25 | 0.609913 | 0.514237 | 0.641922 | -| 0.50 | 0.595247 | 0.537040 | 0.625267 | -| 0.75 | 0.654033 | 0.535672 | 0.548685 | -| 1.00 | 0.578385 | 0.530942 | 0.486970 | +| 0.00 | 0.496803 | 0.408276 | 0.510038 | +| 0.25 | 0.496892 | 0.421386 | 0.504827 | +| 0.50 | 0.504388 | 0.422919 | 0.502942 | +| 0.75 | 0.486868 | 0.421053 | 0.509702 | +| 1.00 | 0.479146 | 0.419534 | 0.507136 | Pooled across seeds (`...__validation_pooled.csv`): | beta | pooled mean | std (sample) | min | max | selected | |---|---|---|---|---|---| -| **0.00** | **0.591704** | 0.089529 | 0.492425 | 0.666307 | **yes** | -| 0.25 | 0.588691 | 0.066435 | 0.514237 | 0.641922 | | -| 0.50 | 0.585851 | 0.044858 | 0.537040 | 0.625267 | | -| 0.75 | 0.579463 | 0.064906 | 0.535672 | 0.654033 | | -| 1.00 | 0.532099 | 0.045718 | 0.486970 | 0.578385 | | +| 0.00 | 0.471706 | 0.055329 | 0.408276 | 0.510038 | | +| 0.25 | 0.474368 | 0.046055 | 0.421386 | 0.504827 | | +| **0.50** | **0.476749** | 0.046624 | 0.422919 | 0.504388 | **yes** | +| 0.75 | 0.472541 | 0.046028 | 0.421053 | 0.509702 | | +| 1.00 | 0.468605 | 0.044742 | 0.419534 | 0.507136 | | -- **PRIMARY selected beta (`beta_star_alpha025`) = 0.00**, max pooled validation +- **PRIMARY selected beta (`beta_star_alpha075`) = 0.50**, max pooled validation per-client-mean rare-class macro-F1; **no tie-break used**; selection is validation-only. -- **SECONDARY per-seed diagnostics only:** seed 0 → 0.75, seed 1 → 0.50, seed 2 → 0.00. +- **SECONDARY per-seed diagnostics only:** seed 0 → 0.50, seed 1 → 0.50, seed 2 → 0.00. ## 6. Final test evaluation of the pooled-selected beta -`beta_star = 0.00` was fixed from pooled validation before test was read. +`beta_star = 0.50` was fixed from pooled validation before test was read. | metric (test) | seed 0 | seed 1 | seed 2 | mean ± std (sample) | |---|---|---|---|---| -| rare-class macro-F1 | 0.641362 | 0.580555 | 0.674489 | 0.632136 ± 0.047642 | -| rare-class recall | 0.830701 | 0.825699 | 0.807370 | 0.821257 ± 0.012283 | -| macro-F1 | 0.674558 | 0.665039 | 0.704433 | 0.681343 ± 0.020555 | -| accuracy | 0.874551 | 0.863490 | 0.884002 | 0.874015 ± 0.010267 | -| weighted-F1 | 0.906156 | 0.899864 | 0.914627 | 0.906882 ± 0.007408 | +| rare-class macro-F1 | 0.511889 | 0.624144 | 0.679704 | 0.605246 ± 0.085489 | +| rare-class recall | 0.803887 | 0.876726 | 0.865681 | 0.848765 ± 0.039256 | +| macro-F1 | 0.741977 | 0.761752 | 0.769179 | 0.757636 ± 0.014060 | +| accuracy | 0.914365 | 0.932611 | 0.937352 | 0.928110 ± 0.012137 | +| weighted-F1 | 0.935566 | 0.948344 | 0.951740 | 0.945216 ± 0.008528 | `original_protean` for the same seeds/condition: | metric (test) | seed 0 | seed 1 | seed 2 | mean ± std (sample) | |---|---|---|---|---| -| rare-class macro-F1 | 0.639810 | 0.582522 | 0.676039 | 0.632790 ± 0.047152 | -| rare-class recall | 0.829601 | 0.826700 | 0.809595 | 0.821965 ± 0.010811 | -| macro-F1 | 0.673103 | 0.666981 | 0.705969 | 0.682018 ± 0.020967 | -| accuracy | 0.873722 | 0.867341 | 0.885231 | 0.875431 ± 0.009067 | -| weighted-F1 | 0.905466 | 0.902075 | 0.915416 | 0.907652 ± 0.006934 | +| rare-class macro-F1 | 0.521540 | 0.614061 | 0.676068 | 0.603890 ± 0.077764 | +| rare-class recall | 0.816440 | 0.883238 | 0.869835 | 0.856504 ± 0.035338 | +| macro-F1 | 0.742471 | 0.747395 | 0.765680 | 0.751849 ± 0.012228 | +| accuracy | 0.913254 | 0.913898 | 0.934576 | 0.920576 ± 0.012129 | +| weighted-F1 | 0.932563 | 0.935937 | 0.948908 | 0.939136 ± 0.008629 | Descriptive difference for test rare-class macro-F1 (`beta_star` − `original_protean`) is -+0.001552 / −0.001967 / −0.001550 (mean −0.000655) — i.e. essentially identical, as expected -because `beta = 0` is mathematically the equal mean over `S_j`. Test is descriptive only; no -superiority or generalisation is claimed. +−0.009651 / +0.010083 / +0.003636 (mean +0.001356). Test is descriptive only; no superiority +or generalisation claimed. ## 7. `beta = 0` equivalence result (all three seeds) For seeds 0, 1, 2, `support_weighted beta=0` vs frozen `original_protean`: - Same manifest/split/hashes: **yes**. -- Round-0 snapshot byte-identical: **yes** (`cd821beb…`, `59d2d2bc…`, `47befeff…`). +- Round-0 snapshot byte-identical: **yes** (`8c477df6…`, `41f0187c…`, `be2ad5fb…`). - `train_support_Nij.csv`, `characterization.csv`, `rare_unseen.csv`: **identical**. -- Independent aggregation (`ΣC/n` vs `(1/n)ΣC`): ≤ 4.4e-16 (1 ULP ordering). -- Test/history/per-client/per-class: **differ slightly** (history rare-macro-F1 max diff - 3.6e-3–6.6e-3; test metric max diff 1.6e-3–3.9e-3). +- Independent aggregation (`ΣC/n` vs `(1/n)ΣC`): 4.4e-16 / 4.4e-16 / 2.2e-16. +- History rare-macro-F1 max diff (rounds ≥ 1): 1.10e-2 / 8.12e-3 / 3.64e-3; test metric max + diff 4.33e-3 / 1.14e-3 / 0.85e-3. - Classification: **B — mathematically equivalent + floating-point numerical divergence; no semantic discrepancy** in any seed. Machine-readable: - `artifacts/analysis/xiiotid__alpha0.25__multiseed__beta0_equivalence.csv`. + `artifacts/analysis/xiiotid__alpha0.75__multiseed__beta0_equivalence.csv`. -Conditional on the shared `S_j` reproduction assumption; not proof `beta=0` reproduces -original PROTEAN. +Conditional on the shared `S_j` reproduction assumption; not proof `beta=0` reproduces original +PROTEAN. ## 8. Partition characterization (realized) -`|S_j| < M` class-condition cells and missing client-class cells (training split): +`|S_j| < M` class-condition cells, missing client-class cells, and client-size ratios +(training split): -| condition | `|S_j| < M` cells | missing client-class cells | client-size max/min (seeds) | -|---|---|---|---| -| alpha=0.25 | **19 / 27** | **38** | 15.6, 20.8, 7.8 | -| alpha=0.50 | 7 / 27 | 9 | 5.1, 7.2, 9.9 | +| condition | `|S_j| < M` cells | missing client-class cells | client-size ratio (seeds) | union unseen | mean class CV | +|---|---|---|---|---|---| +| alpha=0.25 | **19 / 27** | **38** | 15.6, 20.8, 7.8 | 6/6/5 | 1.442 | +| alpha=0.50 | 7 / 27 | 9 | 5.1, 7.2, 9.9 | 3/3/1 | 1.183 | +| alpha=0.75 | **3 / 27** | **4** | 4.15, 5.07, 4.78 | 2/1/0 | 0.881 | -Union unseen classes per seed: alpha=0.25 = 6/6/5 (max 4 on a client); alpha=0.50 = 3/3/1 -(max 2). Union rare classes per seed: alpha=0.25 = 8/7/8; alpha=0.50 = 6/7/7. All clients have -exactly two rare classes at both alphas. Full per-class `N_ij`, CV and max/min in -`docs/research/ALPHA025_BETA_VALIDATION.md` §B. The lower alpha does **not** uniformly increase -every realized statistic (e.g. alpha=0.25 seed 2 client-size ratio 7.8 < alpha=0.50 seed 2's -9.9), so interpretation uses the realized partitions, not alpha alone. +Union rare classes per seed: alpha=0.75 = 4/5/6; all clients have exactly two rare classes. +Full per-class `N_ij`, CV and max/min in `docs/research/ALPHA075_BETA_VALIDATION.md` §C and +`artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_*.csv`. Heterogeneity +decreases with alpha overall but is **not uniformly monotone per seed** (alpha=0.75 seed 0 +ratio 4.15 < seed 2's 4.78; alpha=0.50 seed 2 ratio 9.9 > alpha=0.25 seed 2's 7.8), so +interpretation uses realized partitions, not alpha alone. ## 9. Direct prototype-effect findings (fixed round-0 snapshots, no retraining) -`||Cbar_j(1) - Cbar_j(0)||`, cross-seed mean (std), alpha=0.25 vs alpha=0.50: +`||Cbar_j(1) - Cbar_j(0)||`, cross-seed mean, alpha=0.25 vs 0.50 vs 0.75 (mean across classes +3.422 / 2.099 / **1.305**): -| class | a25 mean (std) | a50 mean (std) | -|---|---|---| -| Exfiltration | 4.316 (0.898) | 2.020 (0.502) | -| Tampering | 4.248 (0.241) | 2.354 (1.165) | -| C&C | 4.020 (0.290) | 1.694 (0.505) | -| Lateral_movement | 3.726 (1.383) | 2.724 (1.035) | -| Exploitation | 3.464 (1.113) | 2.344 (0.412) | -| Reconnaissance | 3.339 (1.107) | 1.002 (0.261) | -| Weaponization | 3.124 (0.585) | 2.561 (1.228) | -| RDOS | 2.566 (1.219) | 2.631 (1.165) | -| Normal | 1.997 (0.533) | 1.557 (0.548) | +| class | a25 | a50 | a75 | +|---|---|---|---| +| Tampering | 4.248 | 2.354 | 1.934 | +| Exfiltration | 4.316 | 2.020 | 1.448 | +| Exploitation | 3.464 | 2.344 | 1.539 | +| Lateral_movement | 3.726 | 2.724 | 1.369 | +| C&C | 4.020 | 1.694 | 1.284 | +| Weaponization | 3.124 | 2.561 | 1.472 | +| Reconnaissance | 3.339 | 1.002 | 0.983 | +| Normal | 1.997 | 1.557 | 0.903 | +| RDOS | 2.566 | 2.631 | 0.814 | -Mean displacement across classes is larger at alpha=0.25 (3.42) than alpha=0.50 (2.10), because -support is more imbalanced and more classes are missing contributors. Displacement is a -direct-aggregation measure only, not a detection result. Outputs: -`artifacts/analysis/xiiotid__alpha0.25__multiseed__prototype_effect_pooled.csv` and per-seed +Displacement tracks realized support imbalance and is a direct-aggregation measure only, not a +detection result. Outputs: +`artifacts/analysis/xiiotid__alpha0.75__multiseed__prototype_effect_pooled.csv` and per-seed `...__seed{0,1,2}__support_weighted__beta0__prototype_snapshot__prototype_effect.csv`. ## 10. Client-level findings (validation, vs beta = 0) -Detail: `...__multiseed__client_validation_detail.csv`; summary: -`...__client_validation_summary.csv`. +Detail: `...__client_validation_detail.csv`; summary: `...__client_validation_summary.csv`; and +`artifacts/analysis/xiiotid__cross_alpha__cross_alpha_client_summary.csv`. -- At alpha=0.25 the higher-beta response is **seed-dependent and often negative**: - improve/degrade/unchanged at beta=0.75 = 5/4/1 (seed 0, mean Δ +0.038), 5/4/1 (seed 1, - +0.043), 3/5/2 (seed 2, −0.118). At beta=1.00 seed 2 degrades 6/10 clients with a worst - client Δ = −0.678, while seed 1 still gains on average. -- This **contrasts with alpha=0.50**, where beta=0.75 improved the majority of clients in every - seed. Client ids are per-seed realisations and are not one population; the analysis is - descriptive and non-causal, and `N_ij` vs ΔF1 remains non-monotonic. +- At alpha=0.75, the response is smaller and seed-dependent. At the selected `beta=0.50`: + 6/4/0 (seed 0, mean Δ +0.008), 9/0/1 (seed 1, +0.015), 6/4/0 (seed 2, −0.007; worst client + −0.152). +- At a common `beta=0.75`: alpha=0.25 = 5/4/1, 5/4/1, 3/5/2 (often negative); alpha=0.50 = + 7/3/0, 8/1/1, 8/1/1 (majority improve); alpha=0.75 = 3/7/0, 9/0/1, 5/5/0. +- `N_ij` vs ΔF1 remains non-monotonic at every alpha. Client ids are per-seed realisations and + are not pooled. -## 11. Comparison with alpha=0.50 (RQ3) +## 11. Three-alpha comparison (RQ3) -| | alpha=0.50 | alpha=0.25 | -|---|---|---| -| pooled-selected beta | 0.75 | **0.00** | -| pooled validation curve | rises to 0.75 | monotone decreasing | -| pooled mean range | 0.544–0.579 | 0.532–0.592 | -| pooled std range | 0.075–0.100 | 0.045–0.090 | -| `|S_j| < M` cells | 7/27 | 19/27 | -| mean `||Cbar(1)−Cbar(0)||` | 2.10 | 3.42 | +| | alpha=0.25 | alpha=0.50 | alpha=0.75 | +|---|---|---|---| +| pooled-selected beta | **0.00** | **0.75** | **0.50** | +| pooled validation curve | monotone decreasing | rises to peak at 0.75 | peaks at 0.50 | +| pooled mean range (spread) | 0.532–0.592 (0.060) | 0.544–0.579 (0.035) | 0.469–0.477 (0.008) | +| pooled std range | 0.045–0.090 | 0.075–0.100 | 0.045–0.055 | +| `|S_j| < M` cells | 19/27 | 7/27 | 3/27 | +| mean `||Cbar(1)−Cbar(0)||` | 3.422 | 2.099 | 1.305 | -The selection **changes** with non-IID severity. This is documented as a descriptive -association accompanied by the realized partition characteristics; **no causal claim is made -that alpha by itself caused the difference**. The effect is small relative to between-seed -variance at both settings. +The pooled-selected beta **changes** with the non-IID condition (0.00 → 0.75 → 0.50) but is +**not monotone** in alpha. This is documented as a descriptive association accompanied by the +realized partition characteristics; **no causal claim** is made. With only three alpha levels, +three project-defined seeds, one dataset and no inferential trend test, monotonicity is not +claimed. -> **Audited label (do not confuse).** Condition-level **pooled** selections are alpha=0.50 → -> **0.75** and alpha=0.25 → **0.00**. The value **0.50** is only the Session-5 **seed-0 -> single-condition** choice and the Session-6 alpha=0.50 **per-seed diagnostic** for seed 0; -> it is not the alpha=0.50 pooled selection. Any "0.50 → 0.00" phrasing for condition-level -> pooled selection is wrong. Verified in `docs/research/SESSION7_AUDIT.md` §M. +> **Audited label (do not confuse).** Condition-level **pooled** selections are alpha=0.25 → +> **0.00**, alpha=0.50 → **0.75**, alpha=0.75 → **0.50**. The value **0.50** is *not* the +> alpha=0.50 pooled selection: it is only the Session-5 **seed-0 single-condition** choice, the +> Session-6 alpha=0.50 **per-seed diagnostic** for seed 0, and now the alpha=0.75 pooled +> selection. See `docs/research/SESSION7_AUDIT.md` §M and `SESSION8_AUDIT.md` §M. ## 12. Runtime / resources -CPU only, no GPU, no swap, exit 0 on all 18 runs. Per-run wall-clock 39:49–42:12; CPU -741–746 %; peak RSS 8.38 GiB (seed 0), 10.80 GiB (seed 1), 7.51 GiB (seed 2). New compute: -18 runs, ≈ 43,799 s ≈ **12.17 h** wall-clock, sequential (8 cores). Machine-readable: -`artifacts/analysis/xiiotid__alpha0.25__multiseed__runtime.csv`. +CPU only, no GPU, no swap, exit 0 on all 18 runs. Per-run wall-clock 40:00–49:02 (two runs +slower under transient host contention); CPU 714–746 %; peak RSS 6.49 GiB (seed 0), 6.91 GiB +(seed 1), 7.20 GiB (seed 2). New compute: 18 runs, 44,371 s ≈ **12.33 h** wall-clock, +sequential (8 cores). Machine-readable: +`artifacts/analysis/xiiotid__alpha0.75__multiseed__runtime.csv`. ## 13. Methodological decisions / assumptions -1. **`S_j` participant/denominator rule** (`S_j = {i | N_ij > 0 and valid C_ij}`, equal - weights in the baseline) is a **REPRODUCTION ASSUMPTION**; material in 19/27 - class-condition cells at alpha=0.25 (7/27 at alpha=0.50). Carry this caveat in all reporting. +1. **`S_j` participant/denominator rule** (`S_j = {i | N_ij > 0 and valid C_ij}`, equal weights + in the baseline) is a **REPRODUCTION ASSUMPTION**; material in 19/27 class-condition cells at + alpha=0.25, 7/27 at alpha=0.50, 3/27 at alpha=0.75. Carry this caveat in all reporting. 2. `N_ij` is class-specific sample support, not prototype quality. 3. Beta is one value for all classes/clients; candidates frozen at `{0,.25,.5,.75,1}`. 4. **PRIMARY selection = pooled validation across clients and seeds**; per-seed selection is secondary stability diagnostics; test used only after the pooled beta is fixed. -5. The `scripts/analyze_multiseed_beta.py` already supported `--alpha` (default 0.5) and - already includes alpha in output names; **no code change was needed**. alpha=0.50 outputs - were re-run and are byte-identical to the Session-6 artifacts (reproducibility verified). +5. Three new read-only analysis scripts were added this session (no training/aggregation code + changed): `scripts/analyze_partition_heterogeneity.py`, + `scripts/verify_beta0_equivalence.py`, `scripts/analyze_cross_alpha.py`. 6. `lambda=1.0`, Adam, lr 1e-3, batch 256 remain PROJECT DECISIONS. No `equal_available` mode. ## 14. Unresolved questions / blockers 1. `S_j` reproduction ambiguity — unresolved; no official PROTEAN code exists. -2. **Pooled beta selection remains statistically weak at both alphas (descriptive only):** - the across-seed sample std (0.045–0.090) is comparable to, or larger than, the total - between-beta pooled-mean spread (0.035 at alpha=0.50, 0.060 at alpha=0.25). No inferential - test was performed, so this is a descriptive comparison, not evidence of insignificance. -3. `beta = 0` is not bitwise identical to `original_protean`; documentation decision on how to - report this remains open. +2. **Pooled beta selection remains statistically weak at all three alphas (descriptive only):** + the across-seed sample std is comparable to or larger than the between-beta spread. No + inferential test was performed. +3. `beta = 0` is not bitwise identical to `original_protean`; the documentation decision on how + to report this remains open. 4. Session-2 manifests still store `preprocessing_hash = null` / `config_hash = null`. -5. Only alpha=0.25 and 0.50, X-IIoTID, three seeds measured. No 5G-NIDD beta result. -6. The alpha=0.25 result (pooled-selected beta = 0) is **one additional condition, not yet a - trend across alpha**. Whether any relationship between non-IID severity and the selected beta - is monotone would require alpha=0.75 (and is still not implied by two conditions). +5. Only alpha=0.25/0.50/0.75, X-IIoTID, three seeds measured. No 5G-NIDD beta result. +6. The three-alpha selected-beta sequence (0.00 / 0.75 / 0.50) is **not monotone**; no trend + claim is made and no inference has been run. ## 15. Git status No commit or push was made. `git status --short` shows only untracked docs/analysis/run -artifacts (`results/` is gitignored; `artifacts/analysis/` is intentionally tracked): +artifacts and the three new scripts (`results/` is gitignored; `artifacts/analysis/` is +intentionally tracked): ``` M docs/handoff/SESSION_HANDOFF.md -?? docs/research/ALPHA025_BETA_VALIDATION.md -?? docs/research/SESSION7_AUDIT.md -?? artifacts/analysis/xiiotid__alpha0.25__* (runs' snapshots, effect CSVs, multiseed tables) -?? (pre-existing Session-5/6 untracked alpha=0.5 views, unchanged) +?? docs/research/ALPHA075_BETA_VALIDATION.md +?? docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md +?? docs/research/SESSION8_AUDIT.md +?? scripts/analyze_cross_alpha.py +?? scripts/analyze_partition_heterogeneity.py +?? scripts/verify_beta0_equivalence.py +?? artifacts/analysis/xiiotid__alpha0.75__* (runs' snapshots, effect CSVs, multiseed tables) +?? artifacts/analysis/xiiotid__cross_alpha__* (heterogeneity, cross-alpha tables) +?? (pre-existing Session-5/6/7 untracked alpha=0.25/0.5 views, unchanged) ``` -`planningawal/` and both raw datasets are unchanged (audit re-verified SHA-256: -X-IIoTID `7b929005…`, 5G-NIDD `fa36f808…`). No model/training/aggregation source changed -(working-tree blobs equal HEAD). `artifacts/partitions/` unmodified. No alpha=0.75 run and no -5G-NIDD `support_weighted` run exists. `equal_available` absent. -`HEAD = 6b97a0d38b839c737c49f41006817503d0672871`. No commit or push was made. +No tracked source/test/config file differs from HEAD. `planningawal/` and both raw datasets are +unchanged (audit re-verified SHA-256: X-IIoTID `7b929005…`, 5G-NIDD `fa36f808…`). +`artifacts/partitions/` unmodified. No 5G-NIDD `support_weighted` run exists. `equal_available` +absent. `HEAD = ff68d380e23859d21c07c8a3a7b56b1b7f06c91b`. No commit or push was made. -## 15b. Session 7 audit result (independent) +## 16. Session 8 audit result -- Verdict: **PASS WITH MINOR CORRECTIONS** (`docs/research/SESSION7_AUDIT.md`). +- Verdict: **PASS** (`docs/research/SESSION8_AUDIT.md`). - Run inventory 18/18, pairing hash-identical, manifests reproduced from raw data. -- Validation recomputed exactly; **pooled `beta_star(alpha=0.25) = 0.00`** (validation only, - monotone decreasing in beta). `beta_star` test and `original_protean` test means/stds exact. -- Leakage audit: selection reads validation only; `test_used_for_selection=false` upheld. +- Validation recomputed exactly; **pooled `beta_star(alpha=0.75) = 0.50`** (validation only). + `beta_star` test and `original_protean` test means/stds exact. +- Leakage audit PASS; `test_used_for_selection=false` upheld. - `beta=0` equivalence: class **B** for all three seeds (no semantic discrepancy). -- Source/data integrity: PASS. pytest: **75 passed, 0 failed, 0 skipped, 0 warnings**. -- Corrections: cross-alpha label disambiguation (documentation-only) and one wording nuance. - No arithmetic, metric-label, std-convention, or provenance defect was found. +- Direct-effect, client-level, rare/unseen, cross-alpha labels: PASS. +- Source/data integrity PASS. pytest: **75 passed, 0 failed, 0 skipped, 0 warnings**. -## 16. Files created / modified this session +## 17. Files created / modified this session Created: -- `docs/research/ALPHA025_BETA_VALIDATION.md` -- `docs/research/SESSION7_AUDIT.md` (independent post-experiment audit) -- `artifacts/analysis/xiiotid__alpha0.25__seed{0,1,2}__*__prototype_snapshot.json` -- `artifacts/analysis/xiiotid__alpha0.25__seed{0,1,2}__support_weighted__beta0__prototype_snapshot__{prototype_effect,prototype_weights}.csv` -- `artifacts/analysis/xiiotid__alpha0.25__multiseed__{validation_per_seed,validation_pooled,client_validation_deltas,client_validation_detail,client_validation_summary,prototype_effect_pooled,test_descriptive,runtime,beta0_equivalence}.csv` and `...__selection_summary.json` -- `results/xiiotid__alpha0.25__seed{0,1,2}__*` (18 run dirs, gitignored) +- `docs/research/ALPHA075_BETA_VALIDATION.md` +- `docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md` +- `docs/research/SESSION8_AUDIT.md` +- `scripts/analyze_partition_heterogeneity.py` +- `scripts/verify_beta0_equivalence.py` +- `scripts/analyze_cross_alpha.py` +- `artifacts/analysis/xiiotid__alpha0.75__*` (run snapshots, effect/weights CSVs, multiseed + tables, `beta0_equivalence.csv`, `runtime.csv`) +- `artifacts/analysis/xiiotid__cross_alpha__*` (partition heterogeneity per-seed/per-class/summary, + cross-alpha beta table/displacement/client summary/summary) +- `results/xiiotid__alpha0.75__seed{0,1,2}__*` (18 run dirs, gitignored) Modified: - `docs/handoff/SESSION_HANDOFF.md` (this file) -No source or test code was changed. `scripts/analyze_multiseed_beta.py` was re-run for -alpha=0.50 and produced byte-identical outputs. +No source or test code was changed. -## 17. Exact recommended next phase +## 18. Exact recommended next phase -1. **Session 7 is audited and safe to checkpoint** (verdict PASS WITH MINOR CORRECTIONS; - see §15b and `docs/research/SESSION7_AUDIT.md`). alpha=0.25 behaved cleanly: pairing valid, - beta=0 class B, pooled selection clean, artifacts complete, no test leakage, runtime - manageable (≈12 h per alpha). Before checkpointing, stage only the intended docs/analysis - artifacts; do not commit `results/` or the raw datasets. -2. **Next experiment:** run X-IIoTID `alpha=0.75` under the same protocol (seeds `{0,1,2}`, - five betas + `original_protean`, sequential, reusing frozen manifests, pooled - validation-only selection), then test evaluation, direct-effect, client-level, and - cross-alpha comparison. This would complete the three planned non-IID severities. Do **not** - infer a monotone alpha→beta relation from two conditions. -3. Do **not** treat `beta = 0.00` (alpha=0.25) or `0.75` (alpha=0.50) as optima. Report each as - "best among tested candidates under pooled validation for that condition". -4. **Do not launch 5G-NIDD or the full grid** until alpha=0.75 is inspected. -5. Use `.venv/bin/python`. Run one alpha at a time; measure runtime as before. -6. Update `docs/research/ALPHA025_BETA_VALIDATION.md` (or add `ALPHA075_BETA_VALIDATION.md`), - `EXPERIMENT_PROTOCOL.md` only if a real ambiguity appears, and this handoff. Do not commit or - push unless asked. +1. **Session 8 is audited and safe to checkpoint** (verdict PASS; see §16 and + `docs/research/SESSION8_AUDIT.md`). X-IIoTID alpha=0.75 behaved cleanly. Before + checkpointing, stage only the intended docs/scripts/analysis artifacts; do not commit + `results/` or the raw datasets. +2. **X-IIoTID three-alpha study is complete** (0.25, 0.50, 0.75 × seeds `{0,1,2}`), with + audited condition-level pooled selections 0.00 / 0.75 / 0.50 and a cross-alpha report. +3. **Next major phase: 5G-NIDD.** Follow the same protocol (the planned alphas `{0.75, 0.50, + 0.25}`, seeds `{0,1,2}`, five betas + `original_protean`, frozen manifests, pooled + validation-only selection). Do **not** launch the full grid blindly: verify 5G-NIDD manifests + exist and reproduce, run the same one-condition-at-a-time pacing, and measure runtime first. + A single smoke/one-condition run before scaling is recommended. +4. Do **not** treat any selected beta as an optimum. Report each as "best among tested + candidates under pooled validation for that condition". +5. Do **not** infer a monotone alpha→beta relation from three X-IIoTID conditions. +6. Update `EXPERIMENT_PROTOCOL.md` only if a real ambiguity appears; update this handoff. +7. Do not commit or push unless asked. -## 18. Important commands +## 19. Important commands ```bash # Tests (75 expected) .venv/bin/python -m pytest -# One full paired beta run (CPU, ~40 min, ~7.5-11.3 GiB) -.venv/bin/python scripts/run_baseline.py --dataset xiiotid --alpha 0.25 --seed 1 \ +# One full paired beta run (CPU, ~40 min, ~6.5-7.2 GiB) +.venv/bin/python scripts/run_baseline.py --dataset xiiotid --alpha 0.75 --seed 1 \ --clients 10 --rounds 10 --local-epochs 3 \ --aggregation-mode support_weighted --beta 0.5 -# Pooled multi-seed validation analysis (read-only, no retraining; --alpha already supported) -.venv/bin/python scripts/analyze_multiseed_beta.py --dataset xiiotid --alpha 0.25 --seeds 0 1 2 +# Pooled multi-seed validation analysis (read-only, no retraining) +.venv/bin/python scripts/analyze_multiseed_beta.py --dataset xiiotid --alpha 0.75 --seeds 0 1 2 # Direct prototype-effect analysis (no retraining) .venv/bin/python scripts/analyze_prototype_effect.py \ - --snapshot artifacts/analysis/xiiotid__alpha0.25__seed0__support_weighted__beta0__prototype_snapshot.json \ + --snapshot artifacts/analysis/xiiotid__alpha0.75__seed0__support_weighted__beta0__prototype_snapshot.json \ --class-names "C&C,Exfiltration,Exploitation,Lateral_movement,Normal,RDOS,Reconnaissance,Tampering,Weaponization" \ --output-dir artifacts/analysis + +# Realized partition characterization (all alphas) +.venv/bin/python scripts/analyze_partition_heterogeneity.py --dataset xiiotid --alphas 0.75 0.5 0.25 + +# beta=0 equivalence audit +.venv/bin/python scripts/verify_beta0_equivalence.py --dataset xiiotid --alpha 0.75 --seeds 0 1 2 + +# Cross-alpha comparison +.venv/bin/python scripts/analyze_cross_alpha.py --dataset xiiotid --alphas 0.25 0.5 0.75 ``` -## 19. Recommended next-session prompt +## 20. Recommended next-session prompt ``` -You are continuing the perdalam-protean research repository (Session 8). +You are continuing the perdalam-protean research repository (Session 9). FIRST, read completely: 1. docs/handoff/SESSION_HANDOFF.md - 2. docs/research/ALPHA025_BETA_VALIDATION.md - 3. docs/research/MULTISEED_BETA_VALIDATION.md - 4. docs/research/EXPERIMENT_PROTOCOL.md - 5. docs/baseline/BASELINE_VALIDATION.md + 2. docs/research/ALPHA075_BETA_VALIDATION.md + 3. docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md + 4. docs/research/SESSION8_AUDIT.md + 5. docs/research/EXPERIMENT_PROTOCOL.md + 6. docs/baseline/BASELINE_VALIDATION.md Critical rules: - planningawal/ is immutable. @@ -383,19 +405,18 @@ Critical rules: - The S_j participant rule is a REPRODUCTION ASSUMPTION (material in 19/27 cells at alpha=0.25). - Do NOT add an executable equal_available mode. - Beta selection uses validation only; PRIMARY is pooled across clients and seeds. - - Do NOT launch 5G-NIDD or the full grid. + - Do NOT launch the full grid blindly; verify manifests and pace one condition at a time. -Context: Session 7 completed X-IIoTID alpha=0.25 for seeds {0,1,2} (18 runs, ~12.17 h) and was -independently audited (PASS WITH MINOR CORRECTIONS; docs/research/SESSION7_AUDIT.md). Pooled -validation selected beta=0.00 (pooled mean 0.591704; monotone decreasing in beta), unlike -alpha=0.50 which selected 0.75. Per-seed diagnostics 0.75/0.50/0.00. beta=0 equivalence class B -(no semantic discrepancy). Realized partitions more heterogeneous (19/27 |S_j| 0.75, alpha=0.25 -> 0.00 (the value 0.50 is only the -Session-5 seed-0 choice, not a condition-level selection). +Context: Session 8 completed X-IIoTID alpha=0.75 for seeds {0,1,2} (18 runs, ~12.33 h) and was +independently audited (PASS; docs/research/SESSION8_AUDIT.md). Pooled validation selected +beta=0.50. Three-alpha X-IIoTID pooled selections: alpha=0.25 -> 0.00, alpha=0.50 -> 0.75, +alpha=0.75 -> 0.50 (NOT monotone). beta=0 equivalence class B. Realized heterogeneity: 19/27, +7/27, 3/27 cells |S_j| 0 and valid C_ij}`, equal weights in the baseline) remains a + **REPRODUCTION ASSUMPTION**. At alpha=0.75 it is material in only **3/27** class-condition + cells, but the `beta = 0` comparison is still conditional on both modes sharing this rule and + does **not** prove `beta = 0` reproduces original PROTEAN. +- Train/validation/test adaptation (70/10/20 stratified) differs from the paper's literal + 80/20; the validation eval partition is a project decision. +- **No 5G-NIDD beta result.** +- **No global beta optimum claim.** `beta = 0.50` is only the best among the five tested + candidates under pooled validation for X-IIoTID alpha=0.75 across seeds `{0,1,2}`. +- The union of rare classes (and hence global rare support) differs by seed and by alpha; + only the per-client-mean metric is directly comparable within a condition. +- Test metrics of the selected beta are single-condition and must not be read as + generalisation; no test metric influenced selection. diff --git a/docs/research/SESSION8_AUDIT.md b/docs/research/SESSION8_AUDIT.md new file mode 100644 index 0000000..468299e --- /dev/null +++ b/docs/research/SESSION8_AUDIT.md @@ -0,0 +1,268 @@ +# Session 8 Post-Experiment Audit (X-IIoTID alpha=0.75) + +Status: **PASS.** Independent recomputation from machine-readable artifacts confirms every +Session 8 claim. No discovered issue changes `beta_star` or the scientific interpretation. No +corrections were required. **No training was run, no aggregation/model source changed, no +test-based selection occurred.** + +Audit performed: 2026-09-26. Audited HEAD: `ff68d380e23859d21c07c8a3a7b56b1b7f06c91b`. + +Companion documents: `docs/research/ALPHA075_BETA_VALIDATION.md`, +`docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md`, `docs/research/EXPERIMENT_PROTOCOL.md`, +`docs/handoff/SESSION_HANDOFF.md`. + +--- + +## A. Scope + +Independently verify the Session 8 claim for: + +- dataset `xiiotid`, alpha `0.75`, seeds `{0,1,2}`, beta `{0, 0.25, 0.5, 0.75, 1.0}` plus + `original_protean` (18 runs); +- PRIMARY pooled-validation selection `beta_star = 0.50`, using validation per-client + rare-class macro-F1 averaged within seed then across seeds; +- cross-alpha labels (alpha=0.25 → 0.00, alpha=0.50 → 0.75, alpha=0.75 → 0.50). + +The audit recomputed from run artifacts and re-derived selection; it repeated no training and +ran no new condition. + +--- + +## B. Run inventory + +18 full-data runs enumerated from `results/*/metadata.json` (15 `support_weighted` + 3 +`original_protean`), **18/18 present**, none duplicated, none partial, none unexpected. + +- Metadata verified per run: dataset=xiiotid, alpha=0.75, seed, aggregation mode, beta, + clients=10, rounds=10, local_epochs=3, batch=256, lr=1e-3, mu=0.1, lambda=1.0, + `manifest_reload_ok=true`, `smoke=false`. +- All 14 expected artifacts present for every run; the round-0 snapshot exists for every run. +- No run exists for any alpha ≠ 0.75 in this session; **no 5G-NIDD `support_weighted` run** + exists; `equal_available` absent from `src/`, `scripts/`, `tests/`. + +--- + +## C. Pairing / manifest integrity + +Within each seed, all six runs share one manifest content hash and a byte-identical round-0 +snapshot (SHA-256): + +| seed | manifest content hash | round-0 snapshot SHA-256 | support/char/rare_unseen | +|---|---|---|---| +| 0 | `759f7e504a7d442101238726334d121a746ea4a8ff366428c1c174e47f358723` | `8c477df6b3af645bf2db1c8005d86ea6258b283370ff3ef3e6e88d6f40095564` | identical | +| 1 | `4a50ba219de20af14f6ad73976111659fba2d5b25cd5eeb1a2f21b76de0d9303` | `41f0187c4f0a8e3db0a8ab7eff0aa50a8f3e4a64ded2448228bd133ea017869d` | identical | +| 2 | `922b7bb15169302be7dd4d4eb1644d077537872e1e91fe59b7525b7e3343cc1c` | `be2ad5fb5fcf87c49b2826936c8d5dae6cdc4ab25fe2677081acec32f8116648` | identical | + +Each manifest was independently reproduced from the raw dataset (`prepare_dataset` → +`reproduce_train_assignments`): all three seeds return exact `N_ij` match, partition-hash match, +split sizes 569,909 / 81,414 / 162,831, `N_ij` sum 569,909, 10 clients. The split hash is +identical across alpha for a given seed (splits are per dataset-seed); the partition hash +differs, as it must. Pairing is proven by content hashes, not filenames. + +--- + +## D. Partition characterization audit + +Independently recomputed from the manifests. Matches the report exactly: + +| statistic | alpha=0.75 seed 0 / 1 / 2 | reality (sum) | +|---|---|---| +| `|S_j| < M` cells | 2 / 1 / 0 | **3 / 27** | +| missing client-class cells | 3 / 1 / 0 | **4** | +| client-size ratio | 4.15 / 5.07 / 4.78 | mean **4.67** | +| union unseen classes | 2 / 1 / 0 | max 1 on a client | +| union rare classes | 4 / 5 / 6 | 2 rare per client (all) | + +Cross-alpha comparison confirmed: alpha=0.25 = 19/27 cells / 38 missing; alpha=0.50 = 7/27 / 9. +Interpretation uses realized statistics, not alpha alone. + +--- + +## E. Validation metric recomputation + +Recomputed from raw artifacts (no report numbers copied): + +- seed-level primary score = mean over eligible clients of `val_per_client.csv` + `rare_class_macro_f1`; equals `val_selection.json` per-client-mean to **< 1e-12** for all 15 + runs; +- global validation accuracy / macro-F1 / weighted-F1 / rare macro-F1 / rare recall recomputed + independently from `val_confusion_matrix.json` with the production formulas: **max diff 0.0** + versus `val_global_metrics.json`. + +All 10 clients are eligible in every seed/beta. Per-seed diagnostics reproduce **0.50 / 0.50 / +0.00**. + +--- + +## F. Rare / unseen integrity + +Per-client rare classes recomputed from training `N_ij` (two smallest positive-support attack +classes, tie by class id) and compared to `rare_unseen.csv` flags/N_ij across all 18 runs: + +- **1,620 cells checked, 0 mismatches.** +- Benign class (`Normal`) never flagged rare/unseen; rare and unseen never overlap. +- Union training-derived rare classes equal the run's `evaluated_rare_classes`; validation/test + labels did not influence rare identity. +- Every client has exactly two rare classes. + +--- + +## G. Pooled-selection recomputation + +Computed from the independently recomputed seed-level scores (not from the report). Pooled = +mean of the three seed means; std sample (`ddof=1`). + +| beta | pooled mean | sample std | min | max | +|---|---|---|---|---| +| 0.00 | 0.471706 | 0.055329 | 0.408276 | 0.510038 | +| 0.25 | 0.474368 | 0.046055 | 0.421386 | 0.504827 | +| **0.50** | **0.476749** | 0.046624 | 0.422919 | 0.504388 | +| 0.75 | 0.472541 | 0.046028 | 0.421053 | 0.509702 | +| 1.00 | 0.468605 | 0.044742 | 0.419534 | 0.507136 | + +- Recompute **matches the report** to formatting precision. +- `argmax` = **0.50** (no tie; top-two gap ≈ 2.4e-3). `tie_break_used=false`. + +--- + +## H. Validation / test leakage audit + +- `run_baseline.py` derives `union_rare` from the training support matrix only; the + `val_selection` payload is built only from validation artifacts, and `test_used_for_selection` + / `selection_split` are recorded for all 15 `support_weighted` runs. +- `analyze_multiseed_beta.py` PRIMARY selection consumes only `per_seed_rows` built from + `val_selection.json`; `test_rows` are read later and never referenced by selection or + tie-break. No code path sorts/filters candidates by a test field. +- The audit's own `beta_star = 0.50` was reproduced from validation artifacts alone. + +Conclusion: selection is programmatic and validation-only; `test_used_for_selection = false` is +upheld. + +--- + +## I. Final test recomputation + +`beta_star = 0.50` fixed from pooled validation; only then test metrics read. Recomputed +independently from `test_confusion_matrix.json` (diff vs JSON = 0.0), then averaged across seeds +with sample std: + +| metric | selected beta 0.50 (mean ± std) | original_protean (mean ± std) | +|---|---|---| +| rare-class macro-F1 | 0.605246 ± 0.085489 | 0.603890 ± 0.077764 | +| rare-class recall | 0.848765 ± 0.039256 | 0.856504 ± 0.035338 | +| macro-F1 | 0.757636 ± 0.014060 | 0.751849 ± 0.012228 | +| accuracy | 0.928110 ± 0.012137 | 0.920576 ± 0.012129 | +| weighted-F1 | 0.945216 ± 0.008528 | 0.939136 ± 0.008629 | + +Matches the report exactly. No superiority claim is made. + +--- + +## J. beta = 0 equivalence audit + +For seeds 0, 1, 2, `original_protean` vs `support_weighted beta=0`: + +| property | seed 0 | seed 1 | seed 2 | +|---|---|---|---| +| same manifest / split / hashes | yes | yes | yes | +| round-0 snapshot byte-identical | yes | yes | yes | +| support/characterization/rare_unseen identical | yes | yes | yes | +| independent aggregation diff (`ΣC/n` vs `(1/n)ΣC`) | 4.4e-16 | 4.4e-16 | 2.2e-16 | +| history rare-macro-F1 round-0 diff | 0.0 | 0.0 | 0.0 | +| history rare-macro-F1 max diff (rounds ≥ 1) | 1.10e-2 | 8.12e-3 | 3.64e-3 | +| test metric max diff | 4.33e-3 | 1.14e-3 | 0.85e-3 | +| classification | **B** | **B** | **B** | + +Class **B** (semantically equivalent + floating-point divergence); **no semantic discrepancy**. +Equivalence remains conditional on the shared `S_j` reproduction assumption. + +--- + +## K. Direct-effect audit + +Independently recomputed `||Cbar_j(beta) − Cbar_j(0)||` from the fixed round-0 snapshot with a +hand-rolled NumPy aggregation: max absolute difference vs the per-seed +`...__prototype_effect.csv` = **8.9e-16** (seed 0 check across all classes/betas). Cross-seed +mean displacement at `beta=1`: **1.305** (alpha=0.75) vs 2.099 (alpha=0.50) and 3.422 +(alpha=0.25). Displacement is a direct-aggregation measure only; no detection causality is +inferred. + +--- + +## L. Client-level audit + +Deltas recomputed from `val_per_client.csv` vs the `client_validation_detail.csv` deltas: +**max absolute difference 0.0** across all seeds/betas/clients. At the selected +`beta=0.50`: 6/4/0 (seed 0, mean +0.008), 9/0/1 (seed 1, +0.015), 6/4/0 (seed 2, −0.007). +`N_ij` vs ΔF1 is non-monotonic. Client ids are per-seed realisations, not pooled. + +--- + +## M. Cross-alpha audit + +Condition-level **pooled** selections confirmed from `cross_alpha_summary.json`: + +| condition | selected beta | tie-break | test_used_for_selection | +|---|---|---|---| +| alpha=0.25 | 0.00 | no | false | +| alpha=0.50 | 0.75 | no | false | +| alpha=0.75 | 0.50 | no | false | + +The sequence 0.00 → 0.75 → 0.50 is **not monotone**. No document in the repository states a +monotone alpha→beta trend. The report explicitly refuses a monotonic interpretation. + +--- + +## N. Artifact provenance + +| Reported quantity | Source artifact(s) | Derivation | +|---|---|---| +| run existence / config | `results//metadata.json` | direct read | +| manifest hashes / reproduction | `artifacts/partitions/xiiotid__alpha0.75__seed{0,1,2}.json` | module + `reproduce_train_assignments` | +| pairing proof | `artifacts/analysis/...__prototype_snapshot.json` | sha256 across 6 runs/seed | +| seed-level validation score | `results/.../val_per_client.csv` + `val_selection.json` | mean of per-client rare macro-F1 | +| validation global metrics | `results/.../val_confusion_matrix.json` | independent numpy recompute | +| pooled mean/std, `beta_star` | recomputed seed scores | mean/`stdev`, argmax | +| test metrics | `results/.../test_confusion_matrix.json` | independent recompute + mean/std | +| beta=0 equivalence | original vs beta0 artifacts/snapshots | sha256 + independent aggregation + history diff | +| direct-effect displacement | snapshots + `...__prototype_effect_pooled.csv` | independent numpy recompute | +| client improve/degrade | `results/.../val_per_client.csv` | deltas vs beta=0 | +| rare-class identity | `results/.../rare_unseen.csv` + manifest | recompute definition | +| partition characterization | manifests + heterogeneity CSVs | independent numpy recompute | +| runtime / RSS | `artifacts/analysis/xiiotid__alpha0.75__multiseed__runtime.csv` (source `/tmp/opencode/s8_alpha075/logs/*.time`, present) | parse `/usr/bin/time -v`; sum 44,371 s = 12.33 h | +| cross-alpha table | `artifacts/analysis/xiiotid__cross_alpha__*` | read per-condition multiseed artifacts | + +--- + +## O. Deviations / corrections found + +**None.** No arithmetic, metric-label, std-convention, provenance or pairing defect was found in +the Session 8 report or analysis artifacts. The three new read-only analysis scripts +(`analyze_partition_heterogeneity.py`, `verify_beta0_equivalence.py`, `analyze_cross_alpha.py`) +do not touch training or aggregation source. + +Minor note (informational, not a defect): two runs were slower than 41 min (seed 1 beta 1.00 at +49:02, seed 2 beta 0.50 at 43:17) due to transient host contention; all 18 exited 0. + +--- + +## P. Final audit verdict + +**PASS.** + +- Run inventory: **PASS** (18/18; no duplicates/partials; no alpha≠0.75 or 5G-NIDD run). +- Pairing / manifest reproduction: **PASS** (hash-identical; raw-data reproduction exact). +- Partition characterization: **PASS** (3/27, 4, 4.15/5.07/4.78, unseen 2/1/0). +- Validation recomputation (seed-level and global): **PASS** (exact). +- Rare/unseen integrity: **PASS** (0 mismatches over 1,620 cells). +- Pooled selection: **PASS**; independent `beta_star = 0.50` using validation only. +- Validation/test leakage: **PASS**. +- Test summaries: **PASS** (exact). +- beta=0 equivalence: **PASS**, class **B** for all seeds. +- Direct-effect and client-level analyses: **PASS** (independent recompute exact). +- Cross-alpha labels: **PASS** (0.00 / 0.75 / 0.50; not monotone; no monotone claim). +- Source/data integrity: **PASS**; pytest 75 passed. + +Session 8 is **safe to checkpoint** with the documented limitations (single dataset, three +alphas, three project-defined seeds, weak effect relative to seed variance, `S_j` reproduction +assumption, no global-optimum claim). diff --git a/docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md b/docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md new file mode 100644 index 0000000..7dff327 --- /dev/null +++ b/docs/research/XIIOTID_CROSS_ALPHA_ANALYSIS.md @@ -0,0 +1,223 @@ +# X-IIoTID Cross-Alpha Analysis (alpha = 0.25 / 0.50 / 0.75) + +Status: **descriptive three-condition comparison** of the frozen beta protocol on X-IIoTID, +using the audited condition-level pooled results of Sessions 6 (`alpha=0.50`), 7 +(`alpha=0.25`) and 8 (`alpha=0.75`). This document does **not** run anything and does not +make a causal or monotonicity claim. Machine-readable counterpart: +`artifacts/analysis/xiiotid__cross_alpha__cross_alpha_beta_table.csv`, `..._summary.json`, +`..._displacement.csv`, `..._client_summary.csv`, and +`artifacts/analysis/xiiotid__cross_alpha__partition_heterogeneity_*.csv`. + +Companion documents: `docs/research/ALPHA075_BETA_VALIDATION.md`, +`docs/research/ALPHA025_BETA_VALIDATION.md`, `docs/research/MULTISEED_BETA_VALIDATION.md`, +`docs/research/EXPERIMENT_PROTOCOL.md`, `docs/handoff/SESSION_HANDOFF.md`. + +--- + +## A. Experimental scope + +| Field | Value | +|---|---| +| dataset | X-IIoTID (`xiiotid`) | +| alpha | 0.25, 0.50, 0.75 | +| seeds | 0, 1, 2 | +| betas | 0.00, 0.25, 0.50, 0.75, 1.00 | +| aggregation | `original_protean` + `support_weighted` | +| selection | pooled validation-only per condition, seeds `{0,1,2}` | +| test | read only after each condition's pooled beta is fixed | + +All three conditions share the frozen training protocol: 10 clients, 10 rounds, 3 local epochs, +batch 256, Adam `lr=1e-3`, `mu=0.1`, `lambda=1.0`, unchanged architecture/preprocessing/split +recipe/loss/inference. For each `(alpha, seed)` all six runs share one manifest and a +byte-identical round-0 snapshot. + +--- + +## B. Realized heterogeneity + +`|S_j| < M` counts class-condition cells with fewer than 10 contributing clients, summed over +the three seeds (out of 27). Missing cells count `(client, class)` pairs with `N_ij = 0`. + +| statistic | alpha=0.25 | alpha=0.50 | alpha=0.75 | +|---|---|---|---| +| `|S_j| < M` cells (sum over 3 seeds) | **19 / 27** | 7 / 27 | **3 / 27** | +| missing client-class cells | **38** | 9 | **4** | +| client-size ratio (seed 0/1/2) | 15.6 / 20.8 / 7.8 | 5.1 / 7.2 / 9.9 | 4.15 / 5.07 / 4.78 | +| client-size ratio mean | 14.75 | 7.41 | **4.67** | +| union rare classes per seed | 8 / 7 / 8 | 6 / 7 / 7 | 4 / 5 / 6 | +| union unseen classes per seed | 6 / 6 / 5 | 3 / 3 / 1 | 2 / 1 / 0 | +| max unseen classes on a client | 4 | 2 | 1 | +| mean class-support CV | 1.442 | 1.183 | **0.881** | +| max per-class max/min ratio (over seeds) | 46,541 | 6,204 | 1,903 | + +Realized heterogeneity decreases as alpha rises, but **not uniformly per seed** (e.g. at +alpha=0.75 seed 0 the client ratio 4.15 is lower than seed 2's 4.78; alpha=0.50 seed 2's ratio +9.9 exceeds alpha=0.25 seed 2's 7.8). Interpretation uses these realized partitions, not alpha +alone. + +--- + +## C. Pooled validation curves (alpha × beta) + +Pooled validation per-client-mean rare-class macro-F1 (selection metric; validation only): + +| beta | alpha=0.25 mean (std) | alpha=0.50 mean (std) | alpha=0.75 mean (std) | +|---|---|---|---| +| 0.00 | **0.591704** (0.089529) | 0.544361 (0.091447) | 0.471706 (0.055329) | +| 0.25 | 0.588691 (0.066435) | 0.555434 (0.090068) | 0.474368 (0.046055) | +| 0.50 | 0.585851 (0.044858) | 0.567322 (0.088539) | **0.476749** (0.046624) | +| 0.75 | 0.579463 (0.064906) | **0.579367** (0.075121) | 0.472541 (0.046028) | +| 1.00 | 0.532099 (0.045718) | 0.573479 (0.100450) | 0.468605 (0.044742) | +| **curve shape** | monotone decreasing | rises to peak at 0.75 | peaks at 0.50, declines both sides | +| **total between-beta spread** | 0.0596 | 0.0350 | **0.0081** | + +The across-seed sample std (0.045–0.100) is comparable to or larger than the between-beta +spread at every alpha, so these selections are statistically weak descriptions, not strong +preferences. + +--- + +## D. Selected beta by condition + +Condition-level **pooled** selections only (per-seed diagnostics are excluded): + +| condition | pooled-selected beta | pooled validation mean | tie-break used | +|---|---|---|---| +| alpha=0.25 | **0.00** | 0.591704 | no | +| alpha=0.50 | **0.75** | 0.579367 | no | +| alpha=0.75 | **0.50** | 0.476749 | no | + +The selected beta **changes with the non-IID condition but is not monotone** in alpha +(0.00 → 0.75 → 0.50). The value 0.50 at alpha=0.50 is only the Session-5 seed-0 choice and the +Session-6 per-seed diagnostic for seed 0; it is **not** the alpha=0.50 pooled selection. + +--- + +## E. Final test summaries (descriptive) + +Selected beta vs `original_protean`, mean ± sample std across seeds `{0,1,2}`. + +| condition | selected beta | test rare macro-F1 (selected) | test rare macro-F1 (original) | test macro-F1 (selected) | test macro-F1 (original) | test acc (selected) | test acc (original) | +|---|---|---|---|---|---|---|---| +| alpha=0.25 | 0.00 | 0.632136 ± 0.047642 | 0.632790 ± 0.047152 | 0.681343 ± 0.020555 | 0.682018 ± 0.020967 | 0.874015 ± 0.010267 | 0.875431 ± 0.009067 | +| alpha=0.50 | 0.75 | 0.665503 ± 0.026685 | 0.642520 ± 0.028683 | 0.731169 ± 0.006234 | 0.708965 ± 0.001172 | 0.906392 ± 0.018450 | 0.899454 ± 0.008398 | +| alpha=0.75 | 0.50 | 0.605246 ± 0.085489 | 0.603890 ± 0.077764 | 0.757636 ± 0.014060 | 0.751849 ± 0.012228 | 0.928110 ± 0.012137 | 0.920576 ± 0.012129 | + +**Do not compare the absolute test rare-class macro-F1 across conditions**: the training-derived +union of rare classes (and hence the global rare evaluation support) differs by alpha and seed +(e.g. alpha=0.75 test rare support 6,251 / 34,503 / 25,454). Only the selected-vs-original +difference *within* a condition is meaningful, and even that is dominated by between-seed +variance. No superiority or generalisation is claimed. + +--- + +## F. Direct prototype displacement (beta = 1 vs beta = 0) + +Cross-seed mean `||Cbar_j(1) − Cbar_j(0)||` on the fixed round-0 snapshots (no retraining): + +| class | alpha=0.25 | alpha=0.50 | alpha=0.75 | +|---|---|---|---| +| Tampering | 4.248 | 2.354 | 1.934 | +| Exfiltration | 4.316 | 2.020 | 1.448 | +| Exploitation | 3.464 | 2.344 | 1.539 | +| Lateral_movement | 3.726 | 2.724 | 1.369 | +| C&C | 4.020 | 1.694 | 1.284 | +| Weaponization | 3.124 | 2.561 | 1.472 | +| Reconnaissance | 3.339 | 1.002 | 0.983 | +| Normal | 1.997 | 1.557 | 0.903 | +| RDOS | 2.566 | 2.631 | 0.814 | +| **mean across classes** | **3.422** | **2.099** | **1.305** | + +Mean displacement tracks realized support imbalance (alpha=0.25 > 0.50 > 0.75). The per-seed +rank order of classes is seed-sensitive at every alpha. Displacement is a direct-aggregation +measure only; it is **not** interpreted as improved or degraded detection. + +--- + +## G. Client-level behaviour + +Improve/degrade/unchanged counts (validation, relative to `beta = 0`). + +At each condition's pooled-selected beta: + +| condition | selected beta | seed 0 | seed 1 | seed 2 | +|---|---|---|---|---| +| alpha=0.25 | 0.00 | 0/0/10 (trivial) | 0/0/10 (trivial) | 0/0/10 (trivial) | +| alpha=0.50 | 0.75 | 7/3/0 (+0.021) | 8/1/1 (+0.027) | 8/1/1 (+0.058) | +| alpha=0.75 | 0.50 | 6/4/0 (+0.008) | 9/0/1 (+0.015) | 6/4/0 (−0.007) | + +(mean Δ in parentheses.) At a common `beta = 0.75`: + +| condition | seed 0 | seed 1 | seed 2 | +|---|---|---|---| +| alpha=0.25 | 5/4/1 | 5/4/1 | 3/5/2 | +| alpha=0.50 | 7/3/0 | 8/1/1 | 8/1/1 | +| alpha=0.75 | 3/7/0 | 9/0/1 | 5/5/0 | + +The client-level response is seed-dependent at every alpha. `N_ij` vs ΔF1 is non-monotonic +everywhere. Client ids are per-seed realisations and are never pooled across seeds. + +--- + +## H. Relation to RQ1 (support imbalance ↔ displacement ↔ rare-class performance) + +Observed, descriptive: + +- Realized support imbalance decreases with alpha (mean class CV 1.442 / 1.183 / 0.881; + `|S_j| < M` cells 19 / 7 / 3), and mean prototype displacement decreases with it + (3.422 / 2.099 / 1.305). Directionally consistent across the three conditions. +- Rare-class validation *levels* do **not** follow the same ordering (pooled means ≈ 0.59 / + 0.56–0.58 / 0.47). Absolute cross-condition levels are not controlled because the + training-derived rare-class sets and per-client eval partitions differ. + +**No causal conclusion** is drawn: the association between support imbalance and displacement is +mechanical under the beta formula, while the relationship to rare-class performance is +confounded by which classes are rare and how much support they have in each partition. + +--- + +## I. Relation to RQ2 (how beta changes prototypes and rare-class performance) + +- **Prototype position:** increasing beta moves every class prototype monotonically away from + its beta=0 equal mean (by construction, because supports are unequal); the magnitude grows + with support imbalance (mean class displacement at beta=1: 1.31 / 2.10 / 3.42 as imbalance + grows). `|S_j| = 1` would leave the aggregate unchanged, but that case does not occur here. +- **Rare-class performance:** the validation response is **non-monotone in beta** and + condition-dependent: monotone decreasing at alpha=0.25, peaking at 0.75 at alpha=0.50, + peaking at 0.50 at alpha=0.75. The effect magnitude is small relative to between-seed + variance at all three conditions. + +--- + +## J. Relation to RQ3 (does selected beta differ across non-IID conditions?) + +Yes, descriptively: the pooled-selected beta is **0.00 (alpha=0.25), 0.75 (alpha=0.50), +0.50 (alpha=0.75)**. It is **not monotone** in alpha. + +Caveats that forbid a trend claim: + +- only **three** alpha levels and **three** project-defined seeds, **one** dataset; +- one selection per condition, each statistically weak (between-seed std ≈ between-beta + spread or larger) and with **no tie-break** exercised; +- the realized partitions differ, so the non-IID "condition" is only partially summarized by + alpha; +- **no inferential monotonic-trend test** has been performed. + +RQ3 asks whether beta changes with conditions; it does not require a monotonic answer. + +--- + +## K. Limitations + +- One dataset (X-IIoTID), three alpha values, three seeds; no cross-dataset claim. +- The `S_j` participant/denominator rule remains a **REPRODUCTION ASSUMPTION** (material in + 19/27 cells at alpha=0.25, 7/27 at alpha=0.50, 3/27 at alpha=0.75); all comparisons are + conditional on both modes sharing it. +- Train/validation/test adaptation (70/10/20 stratified) differs from the paper's literal + 80/20; the validation eval partition is a project decision. +- **No 5G-NIDD beta result yet.** +- **No inferential significance testing**; no global beta optimum. Each selected beta is + "best among the tested candidates under pooled validation for that condition". +- Cross-alpha absolute metrics are not directly comparable because the rare-class sets, global + rare support, and per-client eval partitions differ by condition/seed; only within-condition + contrasts and relative patterns are interpreted. diff --git a/scripts/analyze_cross_alpha.py b/scripts/analyze_cross_alpha.py new file mode 100644 index 0000000..517ae5e --- /dev/null +++ b/scripts/analyze_cross_alpha.py @@ -0,0 +1,230 @@ +#!/usr/bin/env python +"""Read-only cross-alpha comparison for the X-IIoTID beta study. + +Aggregates the audited per-condition multiseed artifacts of several Dirichlet +alpha levels into a single machine-readable comparison: + +- ``*__cross_alpha_beta_table.csv`` — alpha x beta pooled validation mean/std, + pooled recall, selected flag, and the realized heterogeneity summary fields; +- ``*__cross_alpha_displacement.csv`` — per-class direct prototype displacement + ``||Cbar_j(beta)-Cbar_j(0)||`` by alpha; +- ``*__cross_alpha_client_summary.csv`` — client improve/degrade/unchanged counts + by alpha/seed/beta; +- ``*__cross_alpha_summary.json`` — condition-level pooled selections and ranges. + +Reads only existing analysis artifacts; no training, no test-based selection. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "src")) + +from protean_research.aggregation import BETA_CANDIDATES # noqa: E402 + + +def read_csv(path: Path) -> list[dict]: + with path.open("r", encoding="utf-8", newline="") as handle: + return list(csv.DictReader(handle)) + + +def read_json(path: Path): + with path.open("r", encoding="utf-8") as handle: + return json.load(handle) + + +def write_csv(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys())) + writer.writeheader() + writer.writerows(rows) + + +def parse_args(argv=None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dataset", default="xiiotid") + parser.add_argument("--alphas", type=float, nargs="+", default=[0.25, 0.5, 0.75]) + parser.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) + parser.add_argument("--analysis-dir", default="artifacts/analysis") + parser.add_argument("--tag", default=None) + return parser.parse_args(argv) + + +def _resolve(path: str) -> Path: + p = Path(path).expanduser() + return p if p.is_absolute() else REPO_ROOT / p + + +def main(argv=None) -> int: + args = parse_args(argv) + analysis = _resolve(args.analysis_dir) + tag = args.tag or f"{args.dataset}__cross_alpha" + + het_tag = f"{args.dataset}__cross_alpha__partition_heterogeneity" + het_summary = { + float(row["alpha"]): row + for row in read_csv(analysis / f"{het_tag}_summary.csv") + } + + beta_table: list[dict] = [] + displacement_rows: list[dict] = [] + client_rows: list[dict] = [] + summary = {"dataset": args.dataset, "alphas": {}, "betas": list(BETA_CANDIDATES)} + + for alpha in args.alphas: + stem = f"{args.dataset}__alpha{alpha:g}__multiseed" + pooled_path = analysis / f"{stem}__validation_pooled.csv" + selection_path = analysis / f"{stem}__selection_summary.json" + effect_path = analysis / f"{stem}__prototype_effect_pooled.csv" + client_path = analysis / f"{stem}__client_validation_summary.csv" + test_path = analysis / f"{stem}__test_descriptive.csv" + + pooled = read_csv(pooled_path) + selection = read_json(selection_path) + selected_beta = selection["primary_selection"]["selected_beta"] + het = het_summary.get(float(alpha), {}) + + effect = read_csv(effect_path) if effect_path.exists() else [] + for row in effect: + displacement_rows.append({ + "dataset": args.dataset, + "alpha": alpha, + "class_id": int(row["class_id"]), + "class_name": row["class_name"], + "beta": float(row["beta"]), + "n_seeds": int(row["n_seeds"]), + "mean_displacement": float(row["mean_displacement"]), + "std_sample_displacement": ( + float(row["std_sample_displacement"]) + if row["std_sample_displacement"] not in ("", "None") + else None + ), + "mean_relative_displacement": ( + float(row["mean_relative_displacement"]) + if row["mean_relative_displacement"] not in ("", "None") + else None + ), + }) + + if client_path.exists(): + for row in read_csv(client_path): + client_rows.append({ + "dataset": args.dataset, + "alpha": alpha, + "seed": int(row["seed"]), + "beta": float(row["beta"]), + "n_eligible": int(row["n_eligible"]), + "n_improve": int(row["n_improve_vs_beta0"]), + "n_degrade": int(row["n_degrade_vs_beta0"]), + "n_unchanged": int(row["n_unchanged"]), + "mean_delta": ( + float(row["mean_delta_vs_beta0"]) + if row["mean_delta_vs_beta0"] not in ("", "None") + else None + ), + "min_delta": ( + float(row["min_delta_vs_beta0"]) + if row["min_delta_vs_beta0"] not in ("", "None") + else None + ), + "max_delta": ( + float(row["max_delta_vs_beta0"]) + if row["max_delta_vs_beta0"] not in ("", "None") + else None + ), + }) + + disp_beta1 = [ + r for r in displacement_rows + if r["alpha"] == alpha and abs(r["beta"] - 1.0) <= 1e-9 + ] + mean_disp_beta1 = ( + sum(r["mean_displacement"] for r in disp_beta1) / len(disp_beta1) + if disp_beta1 else None + ) + + for row in pooled: + beta = float(row["beta"]) + beta_table.append({ + "dataset": args.dataset, + "alpha": alpha, + "beta": beta, + "n_seeds": int(row["n_seeds"]), + "pooled_val_mean": float( + row["pooled_val_rare_macro_f1_per_client_mean"] + ), + "pooled_val_std_sample": float(row["std_sample_val_rare_macro_f1"]), + "pooled_val_std_pop": float(row["std_pop_val_rare_macro_f1"]), + "pooled_val_min": float(row["min_val_rare_macro_f1"]), + "pooled_val_max": float(row["max_val_rare_macro_f1"]), + "pooled_val_recall": float( + row["pooled_val_rare_recall_per_client_mean"] + ), + "selected": abs(beta - selected_beta) <= 1e-12, + "cells_sj_lt_M_total": int(het.get("cells_sj_lt_M_total", -1)), + "cells_total": int(het.get("cells_total", -1)), + "missing_client_class_cells_total": int( + het.get("missing_client_class_cells_total", -1) + ), + "client_size_ratio_mean": float(het.get("client_size_ratio_mean", -1)), + "support_cv_mean": float(het.get("support_cv_mean", -1)), + "union_unseen_mean": float(het.get("union_unseen_mean", -1)), + "mean_displacement_beta1": mean_disp_beta1, + }) + + summary["alphas"][f"{alpha:g}"] = { + "selected_beta": selected_beta, + "tie_break_used": selection["primary_selection"]["tie_break_used"], + "selection_rule": selection["primary_selection"]["rule"], + "test_used_for_selection": selection["primary_selection"][ + "test_used_for_selection" + ], + "pooled_val_mean_range": [ + min(float(r["pooled_val_mean"]) for r in beta_table + if r["alpha"] == alpha), + max(float(r["pooled_val_mean"]) for r in beta_table + if r["alpha"] == alpha), + ], + "pooled_val_std_sample_range": [ + min(float(r["pooled_val_std_sample"]) for r in beta_table + if r["alpha"] == alpha), + max(float(r["pooled_val_std_sample"]) for r in beta_table + if r["alpha"] == alpha), + ], + "cells_sj_lt_M_total": int(het.get("cells_sj_lt_M_total", -1)), + "cells_total": int(het.get("cells_total", -1)), + "missing_client_class_cells_total": int( + het.get("missing_client_class_cells_total", -1) + ), + "client_size_ratio_mean": float(het.get("client_size_ratio_mean", -1)), + "support_cv_mean": float(het.get("support_cv_mean", -1)), + "union_unseen_mean": float(het.get("union_unseen_mean", -1)), + "mean_displacement_beta1": mean_disp_beta1, + "test_descriptive_present": test_path.exists(), + } + + write_csv(analysis / f"{tag}__cross_alpha_beta_table.csv", beta_table) + write_csv(analysis / f"{tag}__cross_alpha_displacement.csv", displacement_rows) + write_csv(analysis / f"{tag}__cross_alpha_client_summary.csv", client_rows) + with (analysis / f"{tag}__cross_alpha_summary.json").open( + "w", encoding="utf-8" + ) as handle: + json.dump(summary, handle, indent=2, sort_keys=True) + handle.write("\n") + + print(json.dumps(summary, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/analyze_partition_heterogeneity.py b/scripts/analyze_partition_heterogeneity.py new file mode 100644 index 0000000..1fb906b --- /dev/null +++ b/scripts/analyze_partition_heterogeneity.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python +"""Read-only realized non-IID characterization from persisted partition manifests. + +For each ``(dataset, alpha, seed)`` manifest this script reconstructs the training +class-support matrix ``N_ij`` and emits machine-readable heterogeneity statistics: + +- ``*__partition_heterogeneity_per_class.csv`` — one row per ``(alpha, seed, class)`` + with the protocol-required ``min/max/mean/std/CV/max-min`` inequality measures; +- ``*__partition_heterogeneity_per_seed.csv`` — one row per ``(alpha, seed)`` with + ``|S_j| < M`` cell count, missing client-class cells, client-size spread, + rare/unseen counts and class-CV summaries; +- ``*__partition_heterogeneity_summary.csv`` — per-alpha aggregation across seeds. + +This reads existing manifests only. It never regenerates a partition, never reads +test data and never trains. +""" + +from __future__ import annotations + +import argparse +import csv +import json +import statistics +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "src")) + +from protean_research.data import manifest as manifest_mod # noqa: E402 +from protean_research.data.characterization import characterize_support # noqa: E402 +from protean_research.data.rare_unseen import ( # noqa: E402 + rare_class_ids_for_client, + unseen_class_ids_for_client, +) + + +def write_csv(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + if not rows: + path.write_text("", encoding="utf-8") + return + with path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys())) + writer.writeheader() + writer.writerows(rows) + + +def parse_args(argv=None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dataset", default="xiiotid") + parser.add_argument("--alphas", type=float, nargs="+", default=[0.75, 0.5, 0.25]) + parser.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) + parser.add_argument("--partitions-dir", default="artifacts/partitions") + parser.add_argument("--analysis-dir", default="artifacts/analysis") + parser.add_argument( + "--tag", default=None, + help="output stem (default: __cross_alpha__partition_heterogeneity)", + ) + return parser.parse_args(argv) + + +def _resolve(path: str) -> Path: + p = Path(path).expanduser() + return p if p.is_absolute() else REPO_ROOT / p + + +def main(argv=None) -> int: + import numpy as np + + args = parse_args(argv) + partitions_dir = _resolve(args.partitions_dir) + analysis_dir = _resolve(args.analysis_dir) + tag = args.tag or f"{args.dataset}__cross_alpha__partition_heterogeneity" + + per_class_rows: list[dict] = [] + per_seed_rows: list[dict] = [] + + for alpha in args.alphas: + for seed in args.seeds: + path = manifest_mod.manifest_path(partitions_dir, args.dataset, alpha, seed) + manifest = manifest_mod.load_manifest(path) + support = np.asarray(manifest["support"], dtype=np.int64) + class_names = list(manifest["class_names"]) + benign_index = class_names.index(manifest["benign_class"]) + num_clients = int(manifest["num_clients"]) + num_classes = int(manifest["num_classes"]) + assert support.shape == (num_clients, num_classes) + + class_stats = characterize_support(support, class_names) + for row in class_stats: + per_class_rows.append({ + "dataset": args.dataset, + "alpha": alpha, + "seed": seed, + **row, + }) + + sj = (support > 0).sum(axis=0) + cells_lt_m = int((sj < num_clients).sum()) + missing_cells = int((support == 0).sum()) + + totals = support.sum(axis=1).astype(int) + client_min = int(totals.min()) + client_max = int(totals.max()) + client_ratio = float(client_max / client_min) if client_min > 0 else None + + rare_lists = [ + rare_class_ids_for_client(support[c], benign_index) + for c in range(num_clients) + ] + unseen_lists = [ + unseen_class_ids_for_client(support[c], benign_index) + for c in range(num_clients) + ] + union_rare = sorted({c for row in rare_lists for c in row}) + union_unseen = sorted({c for row in unseen_lists for c in row}) + + cvs = [row["cv"] for row in class_stats if row["total"] > 0] + max_min_values = [ + row["max_min_ratio"] for row in class_stats if row["max_min_ratio"] > 0 + ] + + per_seed_rows.append({ + "dataset": args.dataset, + "alpha": alpha, + "seed": seed, + "num_clients": num_clients, + "num_classes": num_classes, + "num_train": int(manifest["num_train"]), + "nij_sum": int(support.sum()), + "n_class_condition_cells": num_classes, + "n_cells_sj_lt_M": cells_lt_m, + "n_missing_client_class_cells": missing_cells, + "client_size_min": client_min, + "client_size_max": client_max, + "client_size_ratio": client_ratio, + "union_rare_count": len(union_rare), + "union_unseen_count": len(union_unseen), + "max_unseen_on_client": max((len(u) for u in unseen_lists), default=0), + "n_clients_2_rare": sum(1 for r in rare_lists if len(r) == 2), + "support_cv_mean": statistics.fmean(cvs) if cvs else None, + "support_cv_max": max(cvs) if cvs else None, + "support_max_min_ratio_max": max(max_min_values) if max_min_values else None, + "manifest_content_hash": manifest_mod.manifest_content_hash(manifest), + "partition_hash": manifest["partition_hash"], + "split_hash": manifest["split"]["hash"], + "rng_seed": int(manifest["rng_seed"]), + }) + + # per-alpha summary across seeds + summary_rows: list[dict] = [] + for alpha in args.alphas: + subset = [r for r in per_seed_rows if r["alpha"] == alpha] + if not subset: + continue + summary_rows.append({ + "dataset": args.dataset, + "alpha": alpha, + "n_seeds": len(subset), + "cells_sj_lt_M_total": sum(r["n_cells_sj_lt_M"] for r in subset), + "cells_total": sum(r["n_class_condition_cells"] for r in subset), + "missing_client_class_cells_total": sum( + r["n_missing_client_class_cells"] for r in subset + ), + "client_size_ratio_mean": statistics.fmean( + r["client_size_ratio"] for r in subset + ), + "client_size_ratio_max": max(r["client_size_ratio"] for r in subset), + "union_unseen_mean": statistics.fmean(r["union_unseen_count"] for r in subset), + "max_unseen_on_client": max(r["max_unseen_on_client"] for r in subset), + "union_rare_mean": statistics.fmean(r["union_rare_count"] for r in subset), + "support_cv_mean": statistics.fmean(r["support_cv_mean"] for r in subset), + "support_max_min_ratio_max": max( + r["support_max_min_ratio_max"] for r in subset + ), + }) + + write_csv(analysis_dir / f"{tag}_per_class.csv", per_class_rows) + write_csv(analysis_dir / f"{tag}_per_seed.csv", per_seed_rows) + write_csv(analysis_dir / f"{tag}_summary.csv", summary_rows) + + payload = { + "dataset": args.dataset, + "alphas": args.alphas, + "seeds": args.seeds, + "per_class": str(analysis_dir / f"{tag}_per_class.csv"), + "per_seed": str(analysis_dir / f"{tag}_per_seed.csv"), + "summary": str(analysis_dir / f"{tag}_summary.csv"), + "per_seed_rows": per_seed_rows, + "summary_rows": summary_rows, + } + out_json = analysis_dir / f"{tag}.json" + with out_json.open("w", encoding="utf-8") as handle: + json.dump(payload, handle, indent=2, sort_keys=True) + handle.write("\n") + + print(json.dumps({ + "tag": tag, + "n_per_class_rows": len(per_class_rows), + "n_per_seed_rows": len(per_seed_rows), + "summary": [{"alpha": r["alpha"], + "cells_sj_lt_M": f"{r['cells_sj_lt_M_total']}/{r['cells_total']}", + "missing_cells": r["missing_client_class_cells_total"], + "client_ratio_mean": round(r["client_size_ratio_mean"], 3)} + for r in summary_rows], + "json": str(out_json), + }, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/verify_beta0_equivalence.py b/scripts/verify_beta0_equivalence.py new file mode 100644 index 0000000..103f2b7 --- /dev/null +++ b/scripts/verify_beta0_equivalence.py @@ -0,0 +1,209 @@ +#!/usr/bin/env python +"""Read-only ``beta = 0`` vs ``original_protean`` equivalence audit. + +For each seed of one ``(dataset, alpha)`` condition this compares the frozen +``original_protean`` run with the ``support_weighted beta=0`` run: + +- manifest content hash, round-0 snapshot bytes, support/characterization/ + rare-unseen artifact bytes (pairing / identity); +- independent aggregation arithmetic on the shared round-0 snapshot + (``original_protean`` equal mean vs ``support_weighted beta=0``); +- downstream divergence (history and test-metric numeric deltas). + +Classification per seed: + A — semantic + numerical identity; + B — semantic equivalence + floating-point divergence; + C — semantic discrepancy. + +Read-only: no training, no aggregation source change. +""" + +from __future__ import annotations + +import argparse +import csv +import hashlib +import json +import sys +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT / "src")) + +from protean_research.aggregation import ( # noqa: E402 + AggregationMode, + aggregate_class_prototypes, +) +from protean_research.analysis.direct_prototype import ( # noqa: E402 + snapshot_contributions, +) + +PAIR_FILES = ("train_support_Nij.csv", "characterization.csv", "rare_unseen.csv") + + +def sha256_bytes(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def read_json(path: Path): + with path.open("r", encoding="utf-8") as handle: + return json.load(handle) + + +def numeric_max_abs_diff(a, b) -> float: + if isinstance(a, dict) and isinstance(b, dict): + diffs = [ + numeric_max_abs_diff(a[k], b[k]) + for k in a.keys() & b.keys() + if isinstance(a[k], (int, float)) and isinstance(b[k], (int, float)) + ] + return max(diffs) if diffs else 0.0 + if isinstance(a, list) and isinstance(b, list) and len(a) == len(b): + diffs = [numeric_max_abs_diff(x, y) for x, y in zip(a, b)] + return max(diffs) if diffs else 0.0 + if isinstance(a, (int, float)) and isinstance(b, (int, float)): + return abs(float(a) - float(b)) + return 0.0 + + +def history_rare_f1(history) -> list[float]: + return [ + float(row["rare_class_macro_f1"]) + for row in history + if row.get("rare_class_macro_f1") is not None + ] + + +def max_abs_diff(xs, ys) -> float: + return max((abs(x - y) for x, y in zip(xs, ys)), default=0.0) + + +def aggregation_difference(snapshot) -> float: + contributions = snapshot_contributions(snapshot) + original = aggregate_class_prototypes( + contributions, AggregationMode.ORIGINAL_PROTEAN + ) + beta0 = aggregate_class_prototypes( + contributions, AggregationMode.SUPPORT_WEIGHTED, beta=0.0 + ) + diffs = [] + for class_id in original: + diffs.extend(abs(x - y) for x, y in zip(original[class_id], beta0[class_id])) + return max(diffs) if diffs else 0.0 + + +def parse_args(argv=None) -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dataset", required=True) + parser.add_argument("--alpha", type=float, required=True) + parser.add_argument("--seeds", type=int, nargs="+", default=[0, 1, 2]) + parser.add_argument("--results-dir", default="results") + parser.add_argument("--analysis-dir", default="artifacts/analysis") + parser.add_argument("--tag", default=None) + return parser.parse_args(argv) + + +def _resolve(path: str) -> Path: + p = Path(path).expanduser() + return p if p.is_absolute() else REPO_ROOT / p + + +def main(argv=None) -> int: + args = parse_args(argv) + results = _resolve(args.results_dir) + analysis = _resolve(args.analysis_dir) + tag = args.tag or f"{args.dataset}__alpha{args.alpha:g}__multiseed" + + rows: list[dict] = [] + for seed in args.seeds: + orig_name = f"{args.dataset}__alpha{args.alpha:g}__seed{seed}__original_protean" + b0_name = ( + f"{args.dataset}__alpha{args.alpha:g}__seed{seed}" + "__support_weighted__beta0" + ) + orig_dir = results / orig_name + b0_dir = results / b0_name + + orig_meta = read_json(orig_dir / "metadata.json") + b0_meta = read_json(b0_dir / "metadata.json") + + orig_snap = analysis / f"{orig_name}__prototype_snapshot.json" + b0_snap = analysis / f"{b0_name}__prototype_snapshot.json" + orig_snap_hash = sha256_bytes(orig_snap) if orig_snap.exists() else None + b0_snap_hash = sha256_bytes(b0_snap) if b0_snap.exists() else None + + pair_hashes = {} + pair_identical = True + for filename in PAIR_FILES: + o = orig_dir / filename + b = b0_dir / filename + same = o.exists() and b.exists() and sha256_bytes(o) == sha256_bytes(b) + pair_hashes[filename] = same + pair_identical = pair_identical and same + + history_orig = read_json(orig_dir / "history.json") + history_b0 = read_json(b0_dir / "history.json") + f1_orig = history_rare_f1(history_orig) + f1_b0 = history_rare_f1(history_b0) + round0_diff = max_abs_diff(f1_orig[:1], f1_b0[:1]) + later_diff = max_abs_diff(f1_orig[1:], f1_b0[1:]) + + test_diff = numeric_max_abs_diff( + read_json(orig_dir / "test_global_metrics.json"), + read_json(b0_dir / "test_global_metrics.json"), + ) + + agg_diff = aggregation_difference(read_json(b0_snap)) + + same_manifest = ( + orig_meta.get("manifest_content_hash") == b0_meta.get("manifest_content_hash") + ) + semantic_ok = ( + same_manifest + and orig_snap_hash is not None + and orig_snap_hash == b0_snap_hash + and pair_identical + and agg_diff <= 1e-12 + ) + numerical_identity = ( + semantic_ok + and later_diff == 0.0 + and test_diff == 0.0 + and history_orig == history_b0 + ) + if numerical_identity: + classification = "A" + elif semantic_ok: + classification = "B" + else: + classification = "C" + + rows.append({ + "dataset": args.dataset, + "alpha": args.alpha, + "seed": seed, + "same_manifest_content_hash": same_manifest, + "round0_snapshot_sha256_original": orig_snap_hash, + "round0_snapshot_sha256_beta0": b0_snap_hash, + "round0_snapshot_byte_identical": orig_snap_hash == b0_snap_hash, + "support_char_rare_identical": pair_identical, + "aggregation_max_abs_diff": agg_diff, + "history_rare_f1_round0_diff": round0_diff, + "history_rare_f1_max_diff_rounds_ge1": later_diff, + "test_metric_max_abs_diff": test_diff, + "classification": classification, + }) + + out = analysis / f"{tag}__beta0_equivalence.csv" + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys())) + writer.writeheader() + writer.writerows(rows) + + print(json.dumps({"output": str(out), "rows": rows}, indent=2, default=str)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())