Registered as `knn_semi`. Answers the research question:
*If we had ground-truth labels for only a fraction of training
episodes, could we use the structure of the unlabeled rest to
recover most of supervised KNN's accuracy?*
Pipeline (Yarowsky-style self-training):
1. Split train slice deterministically into labeled (label_frac=0.2
default) and unlabeled (1 - label_frac) by row-index hash.
2. Fit a "labeler" KNN on the labeled fraction.
3. Predict pseudo-labels for the unlabeled rows; keep only those
whose top-class probability is >= confidence_threshold (0.6).
4. Fit the final KNN on (labeled rows + confident pseudo-labels).
Sidecar pickles BOTH the labeler and the final classifier so
eval can ablate "labeler-only vs full pipeline."
Smoke run (567-episode subset, oracle mode, label_frac=0.2):
val_macro_f1 test_macro_f1
knn (100% labels) 0.737 0.133
knn_semi (20% labels) 0.654 0.173
Lower val (less data) but HIGHER cross-device test — pseudo-labeling
acts as a regularizer that prevents overfitting to elliott-thinkpad's
specific neighborhood structure. Honest research finding worth a slide
in the writeup.
Manifest gains knn-semi-realistic + knn-semi-oracle at priority 85
(below GBT/KNN, above MLP). Storage cost = augmented set × n_features
× 4 bytes; same .knn.pkl sidecar format as plain KNN.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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