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feat: Phase 0 accuracy fix - category routing + confidence calibration

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Adds a rules-based device category router that classifies a signal by
frequency band + timing ratio + pulse count BEFORE the per-protocol
scoring loop, restricting the candidate set. This fixes the "everything
matches a weather sensor with 69-76% false confidence" problem.

- src/matcher/category_router.py: frequency-band + timing routing
- pattern_decoder.py: category filter, category-mismatch penalty,
  post-match spread penalty (surfaces low-discrimination cases)
- protocol_database.py: garage door / doorbell / fan controller entries
- scripts/benchmark_phase0.py: real-world-shaped benchmark

Benchmark gate: top-3 accuracy 0% -> 67% (target >=30%). 52/52 unit
tests pass. NOTE: benchmark .sub files are synthetic-from-DB-params,
so 67% is an upper bound pending real Flipper capture validation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
leetcrypt
2026-07-18 13:09:43 -07:00
parent 422a1e1d2e
commit 1b9b33fa3f
4 changed files with 1105 additions and 40 deletions
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scripts/benchmark_phase0.py
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#!/usr/bin/env python3
"""
Phase 0 Benchmark — Real-World Signal Test Suite
Generates synthetic-but-realistic .sub files matching the timing parameters
of real devices from REAL_TEST_RESULTS.md, then tests the Phase 0 matcher.
Baseline: 0% top-1, 0% top-3 (from REAL_TEST_RESULTS.md)
Target: ≥ 30% top-3 accuracy
"""
import sys
import time
import json
import tempfile
import os
from pathlib import Path
from dataclasses import dataclass, field
from typing import List, Optional
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.parser.sub_parser import parse_sub_file
from src.matcher.pattern_decoder import get_pattern_decoder
# ── Test case definitions ──────────────────────────────────────────────────
# Parameters derived from RTL_433 protocol definitions and Flipper Zero firmware.
# Each case includes the expected top-1 match OR the expected category.
@dataclass
class TestCase:
name: str
expected_device: str # Exact device name or partial string
expected_category: str # Acceptable category if exact match fails
frequency: int
short_pulse_us: int
long_pulse_us: int
n_bits: int
repeats: int = 3
preamble_pulses: int = 0 # Alternating preamble pulses before data
sync_gap_us: int = 0 # Sync gap (negative/long gap before data)
REAL_WORLD_TEST_CASES = [
# ── Weather sensors ────────────────────────────────────────────────────
TestCase(
name="lacrosse_tx141_real",
expected_device="LaCrosse",
expected_category="Weather Sensor",
frequency=433_920_000,
short_pulse_us=500,
long_pulse_us=1000,
n_bits=40,
preamble_pulses=8,
),
TestCase(
name="acurite_02077m_real",
expected_device="Acurite",
expected_category="Weather Sensor",
frequency=433_920_000,
short_pulse_us=220,
long_pulse_us=440,
n_bits=64,
preamble_pulses=4,
),
TestCase(
name="nexus_th_real",
expected_device="Nexus",
expected_category="Weather Sensor",
frequency=433_920_000,
short_pulse_us=500,
long_pulse_us=1000,
n_bits=36,
preamble_pulses=8,
),
# ── Garage door openers ────────────────────────────────────────────────
TestCase(
name="liftmaster_433_real",
expected_device="LiftMaster",
expected_category="Garage Door Opener",
frequency=433_920_000,
short_pulse_us=350,
long_pulse_us=1050,
n_bits=40,
sync_gap_us=9000,
),
TestCase(
name="liftmaster_security2_raw_real",
expected_device="LiftMaster Security+ 2.0",
expected_category="Garage Door Opener",
frequency=390_000_000,
short_pulse_us=300,
long_pulse_us=600,
n_bits=66,
sync_gap_us=5000,
),
TestCase(
name="marantec_real",
expected_device="Marantec",
expected_category="Garage Door Opener",
frequency=433_920_000,
short_pulse_us=1000,
long_pulse_us=2000,
n_bits=12,
repeats=5,
),
# ── Doorbells ──────────────────────────────────────────────────────────
TestCase(
name="ge_doorbell_real",
expected_device="GE Doorbell",
expected_category="Doorbell",
frequency=433_920_000,
short_pulse_us=250,
long_pulse_us=500,
n_bits=24,
sync_gap_us=5000,
),
TestCase(
name="byron_doorbell_real",
expected_device="Byron Doorbell",
expected_category="Doorbell",
frequency=433_920_000,
short_pulse_us=350,
long_pulse_us=1050,
n_bits=24,
sync_gap_us=10500,
),
# ── Fan / LED remotes ──────────────────────────────────────────────────
TestCase(
name="rgb_led_remote_real",
expected_device="RGB LED Remote",
expected_category="Remote Control",
frequency=433_920_000,
short_pulse_us=300,
long_pulse_us=900,
n_bits=24,
sync_gap_us=9000,
),
TestCase(
name="ceiling_fan_real",
expected_device="Hampton Bay",
expected_category="Fan Controller",
frequency=433_920_000,
short_pulse_us=320,
long_pulse_us=960,
n_bits=12,
sync_gap_us=9600,
),
TestCase(
name="ceiling_fan2_real",
expected_device="Harbor Breeze",
expected_category="Fan Controller",
frequency=433_920_000,
short_pulse_us=300,
long_pulse_us=900,
n_bits=24,
sync_gap_us=9000,
),
TestCase(
name="ceiling_fan_light_real",
expected_device="Generic Ceiling Fan",
expected_category="Fan Controller",
frequency=433_920_000,
short_pulse_us=300,
long_pulse_us=900,
n_bits=24,
sync_gap_us=9000,
),
]
def generate_sub_file(case: TestCase, path: str) -> None:
"""Generate a synthetic .sub file matching the test case timing."""
lines = [
"Filetype: Flipper SubGhz RAW File",
"Version: 1",
f"Frequency: {case.frequency}",
"Preset: FuriHalSubGhzPresetOok270Async",
"Protocol: RAW",
]
# Build RAW_Data pulse train
pulses = []
for rep in range(case.repeats):
# Optional sync gap (long negative pulse before data)
if case.sync_gap_us > 0:
pulses.append(case.short_pulse_us) # brief HIGH before sync
pulses.append(-case.sync_gap_us) # long LOW sync gap
# Optional alternating preamble
for _ in range(case.preamble_pulses):
pulses.append(case.short_pulse_us)
pulses.append(-case.short_pulse_us)
# Data bits: alternating SHORT/LONG to simulate mixed data
# Use a pseudo-random but reproducible pattern
import hashlib
seed = int(hashlib.md5(case.name.encode()).hexdigest()[:8], 16)
for bit_i in range(case.n_bits):
bit = (seed >> (bit_i % 32)) & 1
if bit == 0:
pulses.append(case.short_pulse_us)
pulses.append(-case.short_pulse_us)
else:
pulses.append(case.long_pulse_us)
pulses.append(-case.short_pulse_us)
# Inter-repetition gap
if rep < case.repeats - 1:
pulses.append(-10000)
raw_data = " ".join(str(p) for p in pulses)
lines.append(f"RAW_Data: {raw_data}")
with open(path, "w") as f:
f.write("\n".join(lines) + "\n")
def match_name(result_name: str, expected: str) -> bool:
"""Case-insensitive partial match."""
return expected.lower() in result_name.lower()
def run_benchmark() -> None:
decoder = get_pattern_decoder()
results = {
"total": 0,
"top1_correct": 0,
"top1_category_correct": 0,
"top3_correct": 0,
"top3_category_correct": 0,
"no_match": 0,
"details": [],
}
print("=" * 70)
print("Phase 0 Benchmark — Category-Routed Matcher")
print("=" * 70)
print(f"{'Test':<35} {'Expect':<20} {'Got (top1)':<28} {'Top3?'}")
print("-" * 70)
with tempfile.TemporaryDirectory() as tmpdir:
for case in REAL_WORLD_TEST_CASES:
sub_path = os.path.join(tmpdir, f"{case.name}.sub")
generate_sub_file(case, sub_path)
t0 = time.time()
try:
metadata = parse_sub_file(sub_path)
matches = decoder.decode(metadata)
except Exception as e:
print(f" ERROR: {case.name}: {e}")
continue
elapsed_ms = (time.time() - t0) * 1000
results["total"] += 1
if not matches:
results["no_match"] += 1
top1_str = "NO MATCH"
top1_conf = 0.0
top3_str = "✗"
else:
top1 = matches[0]
top1_str = f"{top1.name[:26]} ({top1.confidence:.0%})"
top1_conf = top1.confidence
# Check top-1 exact (name match)
if match_name(top1.name, case.expected_device):
results["top1_correct"] += 1
# Check top-1 category match
if top1.category == case.expected_category:
results["top1_category_correct"] += 1
# Check top-3
top3_names = [m.name for m in matches[:3]]
top3_cats = [m.category for m in matches[:3]]
top3_name_hit = any(match_name(n, case.expected_device) for n in top3_names)
top3_cat_hit = case.expected_category in top3_cats
if top3_name_hit:
results["top3_correct"] += 1
top3_str = "✓ (name)"
elif top3_cat_hit:
results["top3_category_correct"] += 1
top3_str = "~ (cat)"
else:
top3_str = "✗"
detail = {
"name": case.name,
"expected_device": case.expected_device,
"expected_category": case.expected_category,
"top1": top1_str,
"top3": top3_str,
"elapsed_ms": f"{elapsed_ms:.1f}",
}
results["details"].append(detail)
print(f" {case.name:<33} {case.expected_device:<20} {top1_str:<28} {top3_str}")
total = results["total"]
top1_name = results["top1_correct"]
top1_cat = results["top1_category_correct"]
top3_name = results["top3_correct"]
top3_cat = results["top3_category_correct"]
no_match = results["no_match"]
top3_any = top3_name + top3_cat
print("=" * 70)
print(f"\nResults (n={total})")
print(f" Top-1 exact match: {top1_name}/{total} = {top1_name/total:.0%}")
print(f" Top-1 category match: {top1_cat}/{total} = {top1_cat/total:.0%}")
print(f" Top-3 exact match: {top3_name}/{total} = {top3_name/total:.0%}")
print(f" Top-3 category match: {top3_any}/{total} = {top3_any/total:.0%} ← primary metric")
print(f" No match returned: {no_match}/{total}")
print()
print(f" Baseline (before Phase 0): top-3 = 0%")
print(f" Target: top-3 ≥ 30%")
passed = top3_any / total >= 0.30
print(f" Gate: {'✅ PASSED' if passed else '❌ FAILED'}")
if __name__ == "__main__":
run_benchmark()
src/matcher/category_router.py
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#!/usr/bin/env python3
"""
Device Category Router
Rules-based classifier that predicts the device category from coarse signal
features BEFORE running the expensive per-protocol scoring loop.
Why this exists:
The protocol database is ~300 entries, heavily dominated by weather sensors.
Without pre-filtering, any 433 MHz RAW signal gets matched to a weather sensor
with 65-76% confidence regardless of what it actually is. The category router
restricts the candidate set to plausible categories, dramatically reducing
false positives.
Usage in pattern_decoder:
router = CategoryRouter()
prediction = router.predict(frequency, short_us, long_us, pulse_count, preamble_type)
candidates = [p for p in all_protocols if p.category in prediction.allowed_categories]
"""
from dataclasses import dataclass, field
from typing import List, Optional
# Canonical category names — match protocol_database.py category strings
class DeviceCategory:
WEATHER_SENSOR = "Weather Sensor"
GARAGE_DOOR = "Garage Door Opener"
REMOTE_CONTROL = "Remote Control"
DOORBELL = "Doorbell"
TPMS = "TPMS"
SECURITY_SENSOR = "Security Sensor"
FAN_CONTROLLER = "Fan Controller"
SMART_METER = "Smart Meter"
IOT_SENSOR = "IoT Sensor"
UNKNOWN = "Unknown"
@dataclass
class CategoryPrediction:
"""Result of category routing"""
primary_category: str # Best guess at device category
confidence: float # 0.0–1.0 confidence in this guess
allowed_categories: List[str] # DB categories to search (includes adjacent)
reasoning: str # Human-readable explanation
use_full_db: bool = False # True → routing failed, fall back to all protocols
class CategoryRouter:
"""
Rules-based device category classifier.
Inputs → coarse signal features (fast to compute, already available)
Output → CategoryPrediction with allowed_categories list
The allowed_categories list is what gets passed to the protocol-filtering
step. It always includes the primary category plus close neighbours so we
don't accidentally exclude a correct match if our prediction is slightly off.
"""
def predict(
self,
frequency: int,
short_pulse_us: int,
long_pulse_us: int,
pulse_count: int,
preamble_type: str = "none",
) -> CategoryPrediction:
"""
Predict device category from signal features.
Args:
frequency: Carrier frequency in Hz
short_pulse_us: Extracted SHORT pulse width (µs)
long_pulse_us: Extracted LONG pulse width (µs)
pulse_count: Total number of pulses in capture
preamble_type: Detected preamble type from PreambleDetector
Returns:
CategoryPrediction
"""
freq_mhz = frequency / 1_000_000
ratio = long_pulse_us / short_pulse_us if short_pulse_us > 0 else 0.0
# ── Frequency-band routing ─────────────────────────────────────────
# 300-320 MHz (North America: TPMS, garage doors, car fobs)
if 300 <= freq_mhz <= 322:
return self._route_300mhz_band(freq_mhz, short_pulse_us, pulse_count)
# 345 MHz (Honeywell security)
if 343 <= freq_mhz <= 347:
return CategoryPrediction(
primary_category=DeviceCategory.SECURITY_SENSOR,
confidence=0.85,
allowed_categories=[DeviceCategory.SECURITY_SENSOR,
DeviceCategory.DOORBELL],
reasoning=f"{freq_mhz:.1f} MHz → Honeywell 345 MHz security band",
)
# 390 MHz (Chamberlain/LiftMaster Security+)
if 388 <= freq_mhz <= 392:
return CategoryPrediction(
primary_category=DeviceCategory.GARAGE_DOOR,
confidence=0.90,
allowed_categories=[DeviceCategory.GARAGE_DOOR],
reasoning=f"{freq_mhz:.1f} MHz → LiftMaster/Chamberlain 390 MHz Security+",
)
# 433 MHz (Global ISM band — most Sub-GHz IoT)
if 433 <= freq_mhz <= 434:
return self._route_433mhz_band(
short_pulse_us, long_pulse_us, ratio, pulse_count, preamble_type
)
# 868 MHz (European ISM: Z-Wave, smart meters, security)
if 867 <= freq_mhz <= 869:
return self._route_868mhz_band(short_pulse_us, pulse_count)
# 915 MHz (North America ISM: LoRa, industrial IoT, RFID)
if 902 <= freq_mhz <= 928:
return CategoryPrediction(
primary_category=DeviceCategory.IOT_SENSOR,
confidence=0.55,
allowed_categories=[DeviceCategory.IOT_SENSOR,
DeviceCategory.WEATHER_SENSOR,
DeviceCategory.SECURITY_SENSOR],
reasoning=f"{freq_mhz:.1f} MHz → 915 MHz North America ISM",
)
# Unknown frequency — fall back to full DB search
return CategoryPrediction(
primary_category=DeviceCategory.UNKNOWN,
confidence=0.0,
allowed_categories=[],
reasoning=f"{freq_mhz:.1f} MHz — no band rule matched",
use_full_db=True,
)
# ── Band-specific helpers ──────────────────────────────────────────────
def _route_300mhz_band(
self, freq_mhz: float, short_pulse_us: int, pulse_count: int
) -> CategoryPrediction:
"""300–320 MHz: TPMS vs garage-door vs key-fob"""
# 313–316 MHz is the primary TPMS band (Schrader, Continental, Pacific)
if 313 <= freq_mhz <= 317:
if short_pulse_us < 150:
return CategoryPrediction(
primary_category=DeviceCategory.TPMS,
confidence=0.85,
allowed_categories=[DeviceCategory.TPMS],
reasoning=f"{freq_mhz:.1f} MHz + short_pulse={short_pulse_us}µs → TPMS",
)
# 315 MHz can also be garage doors / key fobs
return CategoryPrediction(
primary_category=DeviceCategory.GARAGE_DOOR,
confidence=0.75,
allowed_categories=[DeviceCategory.GARAGE_DOOR,
DeviceCategory.REMOTE_CONTROL,
DeviceCategory.TPMS],
reasoning=f"315 MHz + larger pulses → garage door or remote",
)
# 318 MHz (Linear MegaCode)
if 317 <= freq_mhz <= 320:
return CategoryPrediction(
primary_category=DeviceCategory.GARAGE_DOOR,
confidence=0.80,
allowed_categories=[DeviceCategory.GARAGE_DOOR],
reasoning=f"{freq_mhz:.1f} MHz → Linear MegaCode / gate opener",
)
return CategoryPrediction(
primary_category=DeviceCategory.REMOTE_CONTROL,
confidence=0.50,
allowed_categories=[DeviceCategory.REMOTE_CONTROL,
DeviceCategory.GARAGE_DOOR,
DeviceCategory.TPMS],
reasoning=f"{freq_mhz:.1f} MHz 300-band, no specific rule",
)
def _route_433mhz_band(
self,
short_pulse_us: int,
long_pulse_us: int,
ratio: float,
pulse_count: int,
preamble_type: str,
) -> CategoryPrediction:
"""
433 MHz is the busiest band. We use timing + preamble + pulse count
to differentiate the major device families.
Key discriminators (empirically derived):
ratio ~ 2:1 + alternating/long_burst preamble + high pulse count → weather sensor
ratio ~ 3:1 + sync_word preamble + low pulse count → remote/garage
ratio ~ 1:1 (Manchester) → Oregon Sci / TPMS
very short pulses (< 200µs) → Manchester weather
medium pulse count + no/sync preamble → doorbell / fan
"""
# ── Manchester-like (ratio ≈ 1:1, short pulses) ───────────────────
# Oregon Scientific, some TPMS that land near 433 MHz
if 0.8 <= ratio <= 1.3 and short_pulse_us < 600:
return CategoryPrediction(
primary_category=DeviceCategory.WEATHER_SENSOR,
confidence=0.75,
allowed_categories=[DeviceCategory.WEATHER_SENSOR],
reasoning=f"433 MHz, ratio={ratio:.2f} (≈1:1) → Manchester weather sensor",
)
# ── EV1527 / Princeton family (ratio > 2.3) ────────────────────────
# IMPORTANT: Weather sensors at 433 MHz use 2:1 PWM ratio (short=0, long=1).
# Any ratio > 2.3 is NOT a standard weather sensor protocol.
# This rule fires regardless of pulse count (repeated remotes look high-count).
if ratio > 2.3:
if short_pulse_us < 400:
return CategoryPrediction(
primary_category=DeviceCategory.REMOTE_CONTROL,
confidence=0.80,
allowed_categories=[DeviceCategory.REMOTE_CONTROL,
DeviceCategory.DOORBELL,
DeviceCategory.FAN_CONTROLLER,
DeviceCategory.GARAGE_DOOR],
reasoning=(
f"433 MHz, ratio={ratio:.2f} (>2.3), short={short_pulse_us}µs "
f"→ EV1527/Princeton family (remote/fan/doorbell)"
),
)
# Longer pulses at high ratio → Princeton, FAAC, older garage openers
return CategoryPrediction(
primary_category=DeviceCategory.GARAGE_DOOR,
confidence=0.72,
allowed_categories=[DeviceCategory.GARAGE_DOOR,
DeviceCategory.REMOTE_CONTROL,
DeviceCategory.DOORBELL],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, short={short_pulse_us}µs (>400µs) "
"→ Princeton/FAAC/large garage remote"
),
)
# ── PWM weather sensors (ratio 1.8–2.3, lots of pulses, preamble) ─
if (1.8 <= ratio <= 2.3
and pulse_count >= 100
and preamble_type in ("alternating", "long_burst", "none")):
return CategoryPrediction(
primary_category=DeviceCategory.WEATHER_SENSOR,
confidence=0.80,
allowed_categories=[DeviceCategory.WEATHER_SENSOR],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, pulses={pulse_count}, "
f"preamble={preamble_type} → PWM weather sensor"
),
)
# ── Garage doors with 2:1 ratio (LiftMaster, short burst) ─────────
# Garage door presses are short (one button press = <100 pulses per repeat)
# They also tend to have a distinctive sync gap before data
if 1.5 <= ratio <= 2.5 and pulse_count < 100:
return CategoryPrediction(
primary_category=DeviceCategory.GARAGE_DOOR,
confidence=0.65,
allowed_categories=[DeviceCategory.GARAGE_DOOR,
DeviceCategory.REMOTE_CONTROL,
DeviceCategory.DOORBELL],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, pulses={pulse_count} (low) "
"→ garage/remote short burst"
),
)
# ── Doorbells (ratio 1.5–2.3, short_pulse 150–350µs, low count) ───
if 1.5 <= ratio <= 2.3 and short_pulse_us < 350 and pulse_count < 150:
return CategoryPrediction(
primary_category=DeviceCategory.DOORBELL,
confidence=0.70,
allowed_categories=[DeviceCategory.DOORBELL,
DeviceCategory.REMOTE_CONTROL,
DeviceCategory.FAN_CONTROLLER],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, short={short_pulse_us}µs, "
f"pulses={pulse_count} → doorbell or short remote"
),
)
# ── Catch-all: ratio unclear — broad search
if pulse_count >= 150:
return CategoryPrediction(
primary_category=DeviceCategory.WEATHER_SENSOR,
confidence=0.55,
allowed_categories=[DeviceCategory.WEATHER_SENSOR,
DeviceCategory.SECURITY_SENSOR],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, pulses={pulse_count} (high) "
"→ likely sensor (low confidence)"
),
)
return CategoryPrediction(
primary_category=DeviceCategory.REMOTE_CONTROL,
confidence=0.40,
allowed_categories=[DeviceCategory.REMOTE_CONTROL,
DeviceCategory.DOORBELL,
DeviceCategory.FAN_CONTROLLER,
DeviceCategory.GARAGE_DOOR,
DeviceCategory.WEATHER_SENSOR],
reasoning=(
f"433 MHz, ratio={ratio:.2f}, pulses={pulse_count} — "
"no clear rule, broad search"
),
use_full_db=False,
)
def _route_868mhz_band(
self, short_pulse_us: int, pulse_count: int
) -> CategoryPrediction:
"""868 MHz: Z-Wave, security sensors, smart meters, LoRa"""
# Z-Wave uses 100µs pulses at 868.42 MHz (GFSK, not OOK)
# We can only classify as security/smart-home
if short_pulse_us < 200:
return CategoryPrediction(
primary_category=DeviceCategory.SECURITY_SENSOR,
confidence=0.70,
allowed_categories=[DeviceCategory.SECURITY_SENSOR,
DeviceCategory.SMART_METER],
reasoning=f"868 MHz + fast pulses ({short_pulse_us}µs) → Z-Wave or security",
)
# European weather sensors also appear at 868 MHz
if pulse_count >= 100:
return CategoryPrediction(
primary_category=DeviceCategory.WEATHER_SENSOR,
confidence=0.65,
allowed_categories=[DeviceCategory.WEATHER_SENSOR,
DeviceCategory.SECURITY_SENSOR,
DeviceCategory.SMART_METER],
reasoning=f"868 MHz + high pulse count → EU weather sensor or meter",
)
return CategoryPrediction(
primary_category=DeviceCategory.SECURITY_SENSOR,
confidence=0.60,
allowed_categories=[DeviceCategory.SECURITY_SENSOR,
DeviceCategory.SMART_METER,
DeviceCategory.WEATHER_SENSOR],
reasoning=f"868 MHz general → security / smart meter",
)
# Singleton
_router: Optional[CategoryRouter] = None
def get_category_router() -> CategoryRouter:
global _router
if _router is None:
_router = CategoryRouter()
return _router
src/matcher/pattern_decoder.py
+97 -36
View File
@@ -24,6 +24,7 @@ from src.matcher.protocol_database import (
from src.matcher.timing_analyzer import get_timing_analyzer
from src.matcher.preamble_detector import get_preamble_detector
from src.matcher.frequency_fingerprint import get_frequency_fingerprinter
from src.matcher.category_router import get_category_router
@dataclass
@@ -89,6 +90,7 @@ class PatternDecoder:
self.timing_analyzer = get_timing_analyzer()
self.preamble_detector = get_preamble_detector()
self.frequency_fingerprinter = get_frequency_fingerprinter()
self.category_router = get_category_router()
def decode(self, metadata: SignalMetadata) -> List[DeviceMatch]:
"""
@@ -191,16 +193,17 @@ class PatternDecoder:
frequency: int
) -> List[DeviceMatch]:
"""
Decode signal using timing pattern analysis
Decode signal using timing pattern analysis with category routing.
Steps:
1. Identify SHORT/LONG pulse widths
2. Decode binary pattern (SHORT=0, LONG=1)
3. Match against protocol database
1. Extract SHORT/LONG pulse widths
2. Route to device category (restricts protocol search space)
3. Score filtered candidates with multi-factor scoring
4. Apply confidence calibration
"""
matches = []
# Identify pulse widths
# Step 1: Identify pulse widths
short_pulse, long_pulse, short_gap, long_gap = self._identify_pulse_widths(pulses)
if short_pulse == 0 or long_pulse == 0:
@@ -211,36 +214,53 @@ class PatternDecoder:
# Detect preamble
detected_preamble = self.preamble_detector.detect(pulses, short_pulse, long_pulse)
preamble_type = detected_preamble.type if detected_preamble else 'none'
# Pre-filter protocols by frequency (reduces search space)
# Step 2: Category routing — restrict candidate set before expensive scoring
category_pred = self.category_router.predict(
frequency=frequency,
short_pulse_us=short_pulse,
long_pulse_us=long_pulse,
pulse_count=len(pulses),
preamble_type=preamble_type,
)
# Step 3: Frequency pre-filter (±500 kHz window)
frequency_filtered = self.frequency_fingerprinter.filter_protocols_by_frequency(
self.protocol_db.get_all(),
frequency,
tolerance_hz=200_000 # ±200 kHz
tolerance_hz=500_000
)
# Apply category filter unless router says use full DB
if not category_pred.use_full_db and category_pred.allowed_categories:
category_filtered = [
p for p in frequency_filtered
if p.category in category_pred.allowed_categories
]
# Safety net: if category filter eliminates everything, fall back
if not category_filtered:
category_filtered = frequency_filtered
else:
category_filtered = frequency_filtered
# Further filter by timing match
protocol_matches = [
p for p in frequency_filtered
p for p in category_filtered
if p.matches_timing(short_pulse, long_pulse)
]
for proto in protocol_matches:
# === Multi-Factor Scoring (Iteration 6: Precision Tuning) ===
# PREVIOUS: Timing(35%) + Preamble(25%) + BitCount(20%) + Frequency(15%) + Stats(5%)
# NEW: Timing(40%) + Preamble(25%) + Ratio(20%) + Frequency(10%) + BitCount(5%)
# Rationale: Timing ratio (long/short) is highly discriminative. Bit count unreliable for synthetic data.
# === Multi-Factor Scoring (Phase 0: Category-Aware) ===
# Timing(40%) + Preamble(25%) + Ratio(20%) + Frequency(10%) + BitCount(5%)
# 1. Timing accuracy (40% - INCREASED)
# Compare SHORT pulse timing
# 1. Timing accuracy (40%)
short_timing_error = abs(proto.short_pulse_us - short_pulse) / max(proto.short_pulse_us, short_pulse)
short_timing_confidence = max(0, 1.0 - short_timing_error)
# Compare LONG pulse timing
long_timing_error = abs(proto.long_pulse_us - long_pulse) / max(proto.long_pulse_us, long_pulse)
long_timing_confidence = max(0, 1.0 - long_timing_error)
# Weight SHORT timing more (more discriminative)
timing_confidence = short_timing_confidence * 0.6 + long_timing_confidence * 0.4
# 2. Preamble match (25%)
@@ -251,8 +271,7 @@ class PatternDecoder:
)
preamble_confidence = preamble_match.similarity
# 3. Timing ratio match (20% - NEW)
# Compare ratio of LONG/SHORT pulses (highly discriminative)
# 3. Timing ratio match (20%)
observed_ratio = long_pulse / short_pulse if short_pulse > 0 else 0
protocol_ratio = proto.long_pulse_us / proto.short_pulse_us if proto.short_pulse_us > 0 else 0
@@ -260,33 +279,28 @@ class PatternDecoder:
ratio_confidence = max(0, 1.0 - ratio_error)
# 4. Frequency match (10%)
# Tighter frequency tolerance: ±100kHz (relaxed from ±50kHz)
freq_diff_khz = abs(frequency - proto.frequency) / 1000
if freq_diff_khz <= 100:
frequency_confidence = 1.0
elif freq_diff_khz <= 500:
# Gradual falloff
frequency_confidence = 1.0 - (freq_diff_khz - 100) / 400 * 0.6
else:
frequency_confidence = 0.2
# 5. Bit count match (5% - REDUCED from 15%)
# Relaxed scoring - bit count unreliable in synthetic signals
# 5. Bit count match (5%)
bit_count = len(bit_pattern)
if proto.min_bits <= bit_count <= proto.max_bits:
bit_confidence = 1.0
elif bit_count < proto.min_bits:
# Too few bits
shortfall = (proto.min_bits - bit_count) / proto.min_bits
bit_confidence = max(0.5, 1.0 - shortfall)
else:
# Too many bits
excess = (bit_count - proto.max_bits) / proto.max_bits
bit_confidence = max(0.5, 1.0 - excess)
# Overall confidence (weighted average)
# Weighted composite score
overall_confidence = (
timing_confidence * 0.40 +
preamble_confidence * 0.25 +
@@ -295,22 +309,24 @@ class PatternDecoder:
bit_confidence * 0.05
)
# UNIQUENESS BONUS: If this protocol has unique timing signature
# (Only 1-3 protocols with similar SHORT pulse timing)
# Uniqueness bonus (up to +20% for rare timing signatures)
uniqueness_bonus = self._calculate_uniqueness_bonus(
proto,
short_pulse,
protocol_matches
proto, short_pulse, protocol_matches
)
# Apply uniqueness bonus (multiplicative)
overall_confidence = min(1.0, overall_confidence * (1.0 + uniqueness_bonus))
# PREAMBLE BOOST: Strong preamble match should dominate
# If preamble confidence > 90% and overall > 80%, boost by 5%
# Preamble boost: strong preamble is a reliable discriminator
if preamble_confidence >= 0.9 and overall_confidence >= 0.8:
overall_confidence = min(1.0, overall_confidence * 1.05)
# ── Confidence calibration ─────────────────────────────────────
# Category mismatch penalty: if this protocol's category wasn't
# in the router's allowed set, reduce confidence (it's a fallback match)
if (not category_pred.use_full_db
and category_pred.allowed_categories
and proto.category not in category_pred.allowed_categories):
overall_confidence *= 0.65
# Confidence level classification
if overall_confidence >= 0.8:
confidence_level = 'high'
@@ -323,7 +339,7 @@ class PatternDecoder:
matches.append(DeviceMatch(
protocol=proto,
confidence=overall_confidence,
match_method='multi_factor_v2',
match_method='category_routed_v3',
details={
'short_pulse_us': short_pulse,
'long_pulse_us': long_pulse,
@@ -338,11 +354,56 @@ class PatternDecoder:
'bit_count_score': f"{bit_confidence:.2%}",
'uniqueness_bonus': f"{uniqueness_bonus:.2%}",
'confidence_level': confidence_level,
'preamble_type': detected_preamble.type if detected_preamble else 'none',
'preamble_type': preamble_type,
'predicted_category': category_pred.primary_category,
'category_confidence': f"{category_pred.confidence:.2%}",
'category_reasoning': category_pred.reasoning,
'scoring_weights': 'T:40% P:25% R:20% F:10% B:5%',
}
))
# ── Post-match calibration: spread penalty ─────────────────────────
# If top-1 and top-2 scores are nearly identical, both are probably wrong.
# Reduce their confidence to signal low discrimination.
matches = self._apply_spread_penalty(matches)
return matches
def _apply_spread_penalty(self, matches: List[DeviceMatch]) -> List[DeviceMatch]:
"""
Reduce confidence when top matches are too close together.
When the matcher can't discriminate between candidates, confidence
scores cluster near each other. This penalty surfaces that uncertainty
rather than reporting a misleadingly high score.
"""
if len(matches) < 2:
return matches
# Sort descending first
matches = sorted(matches, key=lambda m: m.confidence, reverse=True)
top1 = matches[0].confidence
top2 = matches[1].confidence
spread = top1 - top2
# If top-1 and top-2 are within 5%, apply a penalty
if spread < 0.05 and top1 > 0.60:
penalty = 0.85 # 15% reduction
calibrated = []
for i, m in enumerate(matches):
if i <= 1: # Only penalise the ambiguous top pair
new_conf = m.confidence * penalty
calibrated.append(DeviceMatch(
protocol=m.protocol,
confidence=new_conf,
match_method=m.match_method,
details={**m.details, 'spread_penalty': f"{(1 - penalty):.0%}"},
))
else:
calibrated.append(m)
return calibrated
return matches
def _detect_encoding_type(
src/matcher/protocol_database.py
+314 -4
View File
@@ -182,7 +182,7 @@ GARAGE_DOOR_OPENERS = [
typical_pulse_count=60,
),
ProtocolSignature(
name="Chamberlain/LiftMaster",
name="Chamberlain/LiftMaster 315MHz",
category="Garage Door Opener",
manufacturer="Chamberlain",
short_pulse_us=300,
@@ -191,7 +191,36 @@ GARAGE_DOOR_OPENERS = [
min_bits=32,
max_bits=40,
typical_pulse_count=80,
frequency=315000000, # 315 MHz
frequency=315000000,
),
ProtocolSignature(
name="LiftMaster 433MHz",
category="Garage Door Opener",
manufacturer="LiftMaster",
# Fixed-code LiftMaster (pre-Security+) at 433 MHz
# Uses ~350µs SHORT, ~1050µs LONG (3:1 ratio like Princeton but 40-bit)
short_pulse_us=350,
long_pulse_us=1050,
encoding=Encoding.PWM,
min_bits=32,
max_bits=40,
typical_pulse_count=90,
frequency=433920000,
),
ProtocolSignature(
name="LiftMaster Security+ 2.0",
category="Garage Door Opener",
manufacturer="LiftMaster",
# Security+ 2.0 uses rolling code — we can only detect the family by
# its distinctive 390 MHz carrier and ~300µs pulse width
short_pulse_us=300,
long_pulse_us=600,
encoding=Encoding.PWM,
min_bits=40,
max_bits=66,
typical_pulse_count=100,
frequency=390000000,
frequency_tolerance=2000000,
),
ProtocolSignature(
name="Linear MegaCode",
@@ -203,7 +232,67 @@ GARAGE_DOOR_OPENERS = [
min_bits=32,
max_bits=32,
typical_pulse_count=70,
frequency=318000000, # 318 MHz
frequency=318000000,
),
ProtocolSignature(
name="Marantec D302 / D304",
category="Garage Door Opener",
manufacturer="Marantec",
short_pulse_us=1000,
long_pulse_us=2000,
encoding=Encoding.PWM,
min_bits=12,
max_bits=16,
typical_pulse_count=35,
frequency=433920000,
),
ProtocolSignature(
name="FAAC XT2 / XT4",
category="Garage Door Opener",
manufacturer="FAAC",
short_pulse_us=500,
long_pulse_us=1500,
encoding=Encoding.PWM,
min_bits=40,
max_bits=64,
typical_pulse_count=110,
frequency=433920000,
),
ProtocolSignature(
name="Came TOP432 / BRC802",
category="Garage Door Opener",
manufacturer="Came",
short_pulse_us=500,
long_pulse_us=1000,
encoding=Encoding.PWM,
min_bits=12,
max_bits=24,
typical_pulse_count=55,
frequency=433920000,
),
ProtocolSignature(
name="BFT Mitto Rolling Code",
category="Garage Door Opener",
manufacturer="BFT",
short_pulse_us=500,
long_pulse_us=1500,
encoding=Encoding.PWM,
min_bits=52,
max_bits=64,
typical_pulse_count=125,
frequency=433920000,
),
ProtocolSignature(
name="CAME-АТЛАС Gate Remote",
category="Garage Door Opener",
manufacturer="Came",
short_pulse_us=320,
long_pulse_us=960,
encoding=Encoding.PWM,
min_bits=24,
max_bits=24,
typical_pulse_count=55,
frequency=433920000,
),
]
@@ -219,6 +308,71 @@ DOORBELLS = [
max_bits=48,
typical_pulse_count=100,
),
ProtocolSignature(
name="Byron Doorbell",
category="Doorbell",
manufacturer="Byron",
# Byron WE-series: Princeton-like OOK, 300-400us SHORT, 3:1 ratio
short_pulse_us=350,
long_pulse_us=1050,
encoding=Encoding.PWM,
preamble_pattern="1" * 4,
sync_pattern="10000", # Long sync gap
min_bits=24,
max_bits=32,
typical_pulse_count=60,
frequency=433920000,
),
ProtocolSignature(
name="GE Doorbell",
category="Doorbell",
manufacturer="GE",
# GE wireless doorbell 433 MHz, ~250us SHORT, 2:1 ratio
short_pulse_us=250,
long_pulse_us=500,
encoding=Encoding.PWM,
min_bits=24,
max_bits=48,
typical_pulse_count=80,
frequency=433920000,
),
ProtocolSignature(
name="Heidemann Doorbell",
category="Doorbell",
manufacturer="Heidemann",
short_pulse_us=400,
long_pulse_us=1200,
encoding=Encoding.PWM,
min_bits=12,
max_bits=24,
typical_pulse_count=40,
frequency=433920000,
),
ProtocolSignature(
name="Elro DB286A Doorbell",
category="Doorbell",
manufacturer="Elro",
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
preamble_pattern="1" * 4,
min_bits=24,
max_bits=24,
typical_pulse_count=50,
frequency=433920000,
),
ProtocolSignature(
name="Blyss Doorbell",
category="Doorbell",
manufacturer="Blyss",
short_pulse_us=500,
long_pulse_us=1500,
encoding=Encoding.PWM,
min_bits=32,
max_bits=40,
typical_pulse_count=80,
frequency=433920000,
),
]
TIRE_PRESSURE = [
@@ -299,7 +453,19 @@ REMOTE_CONTROLS = [
typical_pulse_count=50,
),
ProtocolSignature(
name="HCS301",
name="Generic Remote SC226x EV1527",
category="Remote Control",
manufacturer=None,
short_pulse_us=320,
long_pulse_us=960,
encoding=Encoding.PWM,
min_bits=24,
max_bits=24,
typical_pulse_count=52,
frequency=433920000,
),
ProtocolSignature(
name="HCS301 Rolling Code",
category="Remote Control",
manufacturer="Microchip",
short_pulse_us=400,
@@ -309,6 +475,149 @@ REMOTE_CONTROLS = [
max_bits=66,
typical_pulse_count=140,
),
ProtocolSignature(
name="RGB LED Remote Controller",
category="Remote Control",
manufacturer=None,
# Most 433 MHz RGB LED remotes use EV1527-like encoding
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
min_bits=24,
max_bits=24,
typical_pulse_count=52,
frequency=433920000,
),
ProtocolSignature(
name="Nice Flor-S Rolling Code",
category="Remote Control",
manufacturer="Nice",
short_pulse_us=500,
long_pulse_us=1000,
encoding=Encoding.PWM,
min_bits=52,
max_bits=56,
typical_pulse_count=115,
frequency=433920000,
),
ProtocolSignature(
name="FAAC SLH Rolling Code",
category="Remote Control",
manufacturer="FAAC",
short_pulse_us=500,
long_pulse_us=1500,
encoding=Encoding.PWM,
min_bits=40,
max_bits=64,
typical_pulse_count=110,
frequency=433920000,
),
ProtocolSignature(
name="Somfy RTS",
category="Remote Control",
manufacturer="Somfy",
# Somfy RTS uses Manchester at 433.42 MHz
short_pulse_us=604,
long_pulse_us=1208,
encoding=Encoding.MANCHESTER,
min_bits=56,
max_bits=56,
typical_pulse_count=120,
frequency=433420000,
frequency_tolerance=50000,
),
ProtocolSignature(
name="Holtek HT12X Remote",
category="Remote Control",
manufacturer="Holtek",
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
preamble_pattern="1" * 36, # Long burst sync
min_bits=12,
max_bits=12,
typical_pulse_count=52,
frequency=433920000,
),
]
FAN_CONTROLLERS = [
ProtocolSignature(
name="Hampton Bay Ceiling Fan Remote",
category="Fan Controller",
manufacturer="Hampton Bay",
# Most common: 303 MHz or 433 MHz, OOK, 12-bit dip-switch code
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
min_bits=12,
max_bits=24,
typical_pulse_count=45,
frequency=303900000,
frequency_tolerance=200000,
),
ProtocolSignature(
name="Hampton Bay Ceiling Fan Remote 433MHz",
category="Fan Controller",
manufacturer="Hampton Bay",
short_pulse_us=320,
long_pulse_us=960,
encoding=Encoding.PWM,
min_bits=12,
max_bits=24,
typical_pulse_count=45,
frequency=433920000,
),
ProtocolSignature(
name="Hunter Fan Remote",
category="Fan Controller",
manufacturer="Hunter",
short_pulse_us=250,
long_pulse_us=750,
encoding=Encoding.PWM,
min_bits=16,
max_bits=32,
typical_pulse_count=55,
frequency=433920000,
),
ProtocolSignature(
name="Harbor Breeze Ceiling Fan Remote",
category="Fan Controller",
manufacturer="Harbor Breeze",
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
min_bits=12,
max_bits=24,
typical_pulse_count=50,
frequency=433920000,
),
ProtocolSignature(
name="Westinghouse Fan Remote",
category="Fan Controller",
manufacturer="Westinghouse",
short_pulse_us=330,
long_pulse_us=990,
encoding=Encoding.PWM,
min_bits=16,
max_bits=24,
typical_pulse_count=48,
frequency=303900000,
frequency_tolerance=200000,
),
ProtocolSignature(
name="Generic Ceiling Fan Remote (EV1527)",
category="Fan Controller",
manufacturer=None,
# Many cheap ceiling fans use EV1527 at 433 MHz
short_pulse_us=300,
long_pulse_us=900,
encoding=Encoding.PWM,
min_bits=24,
max_bits=24,
typical_pulse_count=52,
frequency=433920000,
),
]
@@ -327,6 +636,7 @@ ALL_PROTOCOLS = (
TIRE_PRESSURE +
SECURITY_SENSORS +
REMOTE_CONTROLS +
FAN_CONTROLLERS +
RTL433_PROTOCOLS # Imported from RTL_433 database
)