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wifi-densepose
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fix(adr-120): hybrid priority — adaptive owns waving/transition 

W-MLP claimed 90.4% training accuracy in ADR-120 but live UI kept
showing only the 4 baseline classes (absent/still/moving/active).
Root cause: 3 amp_presence_override / amp_classify_from_latest call
sites ALWAYS overwrite classification.motion_level after
adaptive_override runs, regardless of what the model decided. The
rule-based path only knows 4 classes; the 2 new ones (waving,
transition) emitted by the adaptive W-MLP were silently clobbered
every tick.

Hybrid priority:
  rule-based wins → absent / present_still / present_moving / active
                    (ESPectre-style F1>96%, battle-tested)
  adaptive wins   → waving / transition  (exclusive to ADR-120 W-MLP)

Implementation: new helper adaptive_owns_class() + ADAPTIVE_EXCLUSIVE_CLASSES
constant. Each of the 3 rule-based override blocks (multi-BSSID tick,
feature_state path, per-node loop) now guards on `if !adaptive_owns_class(
classification.motion_level)`. Skips the overwrite when the adaptive
model has just emitted a new class.

Live verification (30s sample):
  transition: 14/30 (47%) — visible in live UI for the first time
  present_still: 10/30 (33%)
  present_moving: 1/30
  absent: 1/30

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
arsen 2026-05-18 01:16:27 +07:00
parent da4c123df9
commit 442c03da3b
1 changed files with 44 additions and 17 deletions
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61
v2/crates/wifi-densepose-sensing-server/src/main.rs
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@ -2720,6 +2720,19 @@ fn adaptive_override(state: &AppStateInner, features: &FeatureInfo, classificati
}
}
/// ADR-120: classes that ONLY the adaptive W-MLP model can produce.
/// The rule-based amp_presence_override / amp_classify_from_latest paths
/// know only {absent, present_still, present_moving, active}; if the
/// adaptive model has just emitted `waving` or `transition`, we must NOT
/// overwrite it with the rule-based output. Hybrid priority: rule-based
/// wins for the 4 baseline classes (it's battle-tested at F1 > 96%);
/// adaptive wins exclusively when emitting a class outside that set.
const ADAPTIVE_EXCLUSIVE_CLASSES: &[&str] = &["waving", "transition"];
fn adaptive_owns_class(label: &str) -> bool {
ADAPTIVE_EXCLUSIVE_CLASSES.iter().any(|&c| c == label)
}
/// ADR-120: push the current frame's feature vector into the rolling
/// window buffer, evicting the oldest entry when at capacity. Called
/// once per tick from the broadcast tick task where `&mut AppStateInner`
@ -3035,12 +3048,16 @@ async fn windows_wifi_task(state: SharedState, tick_ms: u64) {
// reference, see #[allow(dead_code)]). With gain-lock active (ADR-100)
// CV of broadband mean amplitude separates EMPTY/STILL/WALK by 3-6×
// on this deployment, where RSSI MAD-Δ overlapped within ±0.03.
if let Some((level, presence, conf)) =
amp_presence_override(frame.node_id, &frame.amplitudes)
{
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
// ADR-120: skip the rule-based override when the adaptive model
// has emitted a class only it can produce (waving / transition).
if !adaptive_owns_class(&classification.motion_level) {
if let Some((level, presence, conf)) =
amp_presence_override(frame.node_id, &frame.amplitudes)
{
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
}
}
// ADR-104 phase-domain: update phase drift score for this node
// alongside the amplitude classifier. No-op if no phase baseline.
@ -6045,10 +6062,14 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
// ADR-101: inherit the raw-amplitude classifier from the
// CSI path (this feature_state path doesn't carry amps).
if let Some((level, presence, conf)) = amp_classify_from_latest() {
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
// ADR-120: skip when adaptive model produced a class only
// it knows (waving / transition).
if !adaptive_owns_class(&classification.motion_level) {
if let Some((level, presence, conf)) = amp_classify_from_latest() {
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
}
}
// ADR-112: prefer multistatic-derived signal_field
@ -6282,18 +6303,24 @@ async fn udp_receiver_task(state: SharedState, udp_port: u16) {
classification.confidence = (conf * 0.7 + classification.confidence * 0.3).clamp(0.0, 1.0);
}
// ADR-101: amp classifier wins over the legacy adaptive model.
// ADR-101: amp classifier wins over the legacy adaptive
// model for absent/still/moving/active. ADR-120: but the
// adaptive W-MLP retains exclusive ownership of the new
// classes (waving / transition) — skip the override when
// the model has already emitted one.
let amps_now = ns.frame_history.back().cloned().unwrap_or_default();
if !amps_now.is_empty() {
if let Some((level, presence, conf)) = amp_presence_override(node_id, &amps_now) {
if !adaptive_owns_class(&classification.motion_level) {
if !amps_now.is_empty() {
if let Some((level, presence, conf)) = amp_presence_override(node_id, &amps_now) {
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
}
} else if let Some((level, presence, conf)) = amp_classify_from_latest() {
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
}
} else if let Some((level, presence, conf)) = amp_classify_from_latest() {
classification.motion_level = level;
classification.presence = presence;
classification.confidence = conf;
}
// ADR-104 phase-domain: update phase drift if a
// phase baseline is loaded and the latest frame