7894d39f1c
Replace the LogisticRegression meta-learner with a PyTorch MetaFusionMLP (Linear(3,16)->BN->ReLU->Dropout->Linear(16,1)->Sigmoid) for non-linear fusion of EIF, NF, and XGBoost scores. Replace KS-test + quantile digest drift detection with ADWIN (adaptive sliding window, Hoeffding bound). Replace weekly XGBoost batch retraining with River HoeffdingAdaptiveTree for incremental online learning (learn_one per cycle). Update all thesis documentation sections (2.4.2c, 2.4.3, 3.8, discussion, conclusion). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
16 lines
254 B
Plaintext
16 lines
254 B
Plaintext
clickhouse-connect==0.8.12
|
|
pandas==2.2.3
|
|
scikit-learn==1.6.1
|
|
shap==0.47.2
|
|
scipy>=1.14
|
|
hdbscan>=0.8.38
|
|
isotree>=0.6.1
|
|
torch>=2.0
|
|
torch_geometric>=2.4
|
|
FrEIA>=0.2
|
|
xgboost>=2.0
|
|
cleanlab>=2.6
|
|
river>=0.19
|
|
pyyaml>=6.0
|
|
ja4-common @ file:///app/shared/ja4_common
|