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This commit is contained in:
Home Assistant Version Control
2026-10-09 11:57:31 +00:00
parent 28fa34d78a
commit 1e1b12d52b
187 changed files with 40899 additions and 32654 deletions
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custom_components/ha_washdata/ml/feature_extraction.py
+5 -108
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@@ -24,12 +24,13 @@ Three feature extractors are implemented:
- **Cycle-end detector** (``END_FEATURE_COLUMNS``): self-contained from a live
power series + profile expectation; call ``latest_end_event_features``.
- **Live-match commit confidence** (``LIVE_MATCH_FEATURE_COLUMNS``): requires the
match ranking from ``ProfileStore`` plus the observed prefix; call
``live_match_features``.
- **Remaining-time / energy regressors** (``PROGRESS_FEATURE_COLUMNS``): a
running-cycle prefix + profile expectation; call ``progress_features``.
- **Hybrid cycle quality** (``QUALITY_FEATURE_COLUMNS``): requires the complete
cycle power trace plus profile/match context; call ``quality_features``.
(The live-match commit features went with the early match commit in 0.5.8.)
All inputs are plain Python/NumPy (offset-seconds, watts), so this module has no
Home Assistant dependency and is unit-tested directly. It is only invoked when
the user opts into experimental ML models (see engine.py).
@@ -108,38 +109,6 @@ def profile_expectation(cycles_points: Sequence[Sequence[Point]]) -> dict[str, f
}
def profile_expectations(cycles: list[dict]) -> dict[str, dict[str, float]]:
"""Median duration/energy/peak per profile from stored cycle dicts.
The dict-based counterpart of :func:`profile_expectation` (which works from
decompressed traces): reads the ``duration``/``energy_wh``/``max_power``
scalar fields already stored on each cycle. Shared by on-device training
(``training_task``) and the ML suggestion engine so the "profile expectation"
definition lives in one place. Profiles with no usable duration are skipped;
missing energy/peak default to 500.
"""
stats: dict[str, dict[str, list[float]]] = {}
for c in cycles:
name = c.get("profile_name")
if not isinstance(name, str) or not name:
continue
s = stats.setdefault(name, {"d": [], "e": [], "p": []})
for key, field in (("d", "duration"), ("e", "energy_wh"), ("p", "max_power")):
v = c.get(field)
if isinstance(v, (int, float)) and not isinstance(v, bool):
s[key].append(float(v))
out: dict[str, dict[str, float]] = {}
for name, s in stats.items():
if not s["d"]:
continue
out[name] = {
"duration": float(np.median(s["d"])),
"energy": float(np.median(s["e"])) if s["e"] else 500.0,
"peak": float(np.median(s["p"])) if s["p"] else 500.0,
}
return out
def latest_end_event_features(
points: Sequence[Point],
expectation: dict[str, float],
@@ -195,83 +164,11 @@ def latest_end_event_features(
}
# ---------------------------------------------------------------------------
# Live-match commit confidence
# ---------------------------------------------------------------------------
# Mirrors ml_washdata/wash_ml/live_matching.py COMMIT_FEATURE_COLUMNS.
# The test suite asserts this equals the embedded model's FEATURE_COLUMNS.
LIVE_MATCH_FEATURE_COLUMNS = [
"match_progress_top1",
"top1_distance",
"margin",
"distance_ratio",
"candidate_count_log",
"prefix_active_fraction",
"duration_ratio_top1",
"elapsed_log",
]
def live_match_features(
points: Sequence[Point],
elapsed_s: float,
top1_distance: float,
top2_distance: float | None,
top1_median_duration_s: float,
candidate_count: int,
) -> dict[str, float]:
"""Features for the live-match commit-confidence model.
Args:
points: Observed power readings (offset_s, watts) for the current prefix.
elapsed_s: Seconds elapsed since cycle start.
top1_distance: Blended RMSE+DTW shape distance to the top-1 candidate
prefix (as returned by the profile matcher).
top2_distance: Distance to the top-2 candidate; pass ``None`` or ``0.0``
when only one candidate is available (margin defaults to 1.0).
top1_median_duration_s: Expected (median) duration of the top-1 candidate
profile in seconds.
candidate_count: Number of candidate profiles on this device.
Returns a dict with exactly ``LIVE_MATCH_FEATURE_COLUMNS`` keys.
"""
elapsed = max(0.0, float(elapsed_s))
top1 = max(0.0, float(top1_distance))
top2_raw = float(top2_distance) if top2_distance is not None else 0.0
top2 = top2_raw if top2_raw > 1e-9 else top1 + 1.0
margin = max(0.0, top2 - top1)
dur = float(top1_median_duration_s)
progress = (elapsed / dur) if dur > 0 else 1.0
# prefix_active_fraction: fraction of prefix readings clearly above idle.
# Lab uses > 0.05 on a peak-normalised trace; equivalent here is > 5% of
# peak, with a 1 W floor so a cold trace never divides by near-zero.
if points:
powers = np.asarray([max(0.0, float(p)) for _, p in points], dtype=float)
peak = float(np.max(powers)) if powers.size else 0.0
active_thr = max(1.0, 0.05 * peak)
prefix_active_fraction = float(np.mean(powers > active_thr)) if powers.size else 0.0
else:
prefix_active_fraction = 0.0
return {
"match_progress_top1": float(min(progress, 2.0)),
"top1_distance": float(top1),
"margin": float(margin),
"distance_ratio": float(top1 / top2) if top2 > 1e-9 else 1.0,
"candidate_count_log": float(math.log1p(max(0, int(candidate_count)))),
"prefix_active_fraction": float(prefix_active_fraction),
"duration_ratio_top1": float(min(progress, 2.0)),
"elapsed_log": float(math.log1p(elapsed)),
}
# ---------------------------------------------------------------------------
# Remaining-time / progress regressor
# ---------------------------------------------------------------------------
# Feature columns for the on-device remaining-time regressor. Unlike the three
# Feature columns for the on-device remaining-time regressor. Unlike the
# classifier heads this model is a ``standardized_linear`` regressor whose target
# is the cycle completion fraction (elapsed / total_actual). There is no shipped
# baseline: the model exists only once on-device training promotes one over the