Trains a PyTorch 1D CNN on encoded RAW pulse arrays as the Phase 3B leg
of the designed ensemble, scored on the SAME group-disjoint held-out test
set as the heuristic and statistical models (GroupShuffleSplit by device
sub-folder, no near-duplicate leakage).
RESULT — CNN is data-starved and loses decisively:
CNN balanced 0.338, top-1 0.795 (garage-inflated)
statistical balanced 0.625, top-1 0.868
heuristic top-1 0.300
Only 489 train samples with severe class imbalance (Garage 565, Security 2,
Weather 12). A blend sweep confirmed every non-zero CNN weight degrades the
statistical model (0.625 -> 0.613 at 15% CNN, worse beyond), so the CNN is
NOT wired into decode(). Production ensemble stays heuristic + statistical,
both already live.
Committing the trainer + shared pulse_encoder (trainer/inference parity) +
metrics.json to document the reproducible negative result. The benched
.pt/.onnx binaries are intentionally NOT committed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
leetcrypt
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