# EgoSpeak dense-trajectory event proxy

> **Status:** boundary-centered diagnostic retained for reproducibility. The
> manuscript's current main behavioral protocol is the matched-opportunity
> evaluator in
> [`easycom_opportunity_event_evaluation.md`](easycom_opportunity_event_evaluation.md).

`evaluate_egospeak_event_proxy.py` provides a deliberately limited bridge
between EgoSpeak-compatible dense future-activity predictions and reviewed
wearer ENTRY/INT opportunities. It does **not** claim that wearer voice
activity is a semantic floor-event detector.

## Score

For every positive event, the default decision region contains the three 5-Hz
source rows immediately before its corrected behavior anchor. An independently
mined hard negative uses the first three rows of its labeled opportunity. A raw
wearer onset/offset relabeled as a hard negative uses the same pre-boundary
region as a positive. Regions shorter than 0.6 seconds are excluded and
counted; longer annotation evidence is not scored.

At each source row, the script recovers the target-aligned wearer trajectory
at 0.6, 0.8, ..., 2.0 seconds. ENTRY uses its mean and INT uses one minus its
mean. Max-pooling over the three decision rows produces one score per reviewed
opportunity. The output must be called an `activity-derived event proxy`.

## Threshold-free provisional run

This reports AP without fitting thresholds on Sessions 1--3:

```bash
python3 evaluate_egospeak_event_proxy.py \
  --reviews reports/easycom_entry_int_sessions1_4_v16/easycom_eot_int_provisional_llm_reviews.jsonl \
  --run egospeak=results/egospeak_miniroada_floor_eval \
  --sessions Session_1 Session_2 Session_3 \
  --allow-provisional-llm-reviews \
  --print-event-examples 5 \
  --output results/easycom_egospeak_event_proxy/sessions1_3_provisional_summary.json
```

The command writes JSON, a compact metrics CSV, and an event CSV containing the
three decision-frame scores and the winning selected-horizon wearer trajectory.

## Recall-matched operating points

`--matched-recalls` on official test sessions additionally requires disjoint
Sessions 4--12 reviews and predictions via `--calibration-reviews`,
`--calibration-sessions`, and optionally `--calibration-run`. Do not use
`--allow-in-sample-thresholds` for reported results.

The default 0.6-second decision window and 0.6--2.0-second trajectory range
remain protocol choices to freeze using training-side data. Report shorter or
longer windows only as predeclared sensitivity analyses.