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https://github.com/zjs81/meshcore-open.git
synced 2026-04-20 22:13:48 +00:00
feat: add ML-based adaptive timeout prediction using LinearRegressor
Train a linear regression model on actual message delivery times to predict tighter timeouts, replacing worst-case physics estimates. Features: path length, message bytes, seconds since last RX, flood mode. Global model with per-contact blending after 10+ observations per contact. Falls back to existing physics formula when model has insufficient data.
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zjs81
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9 changed files with 683 additions and 20 deletions
122
test/services/ml_algo_sanity_test.dart
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122
test/services/ml_algo_sanity_test.dart
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import 'package:flutter/foundation.dart';
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import 'package:flutter_test/flutter_test.dart';
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import 'package:ml_algo/ml_algo.dart';
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import 'package:ml_dataframe/ml_dataframe.dart';
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void main() {
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test('LinearRegressor basic sanity check', () {
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// Simple: y = 2x + 100
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final data = DataFrame([
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[1.0, 102.0],
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[2.0, 104.0],
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[3.0, 106.0],
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[4.0, 108.0],
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[5.0, 110.0],
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[10.0, 120.0],
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[20.0, 140.0],
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[50.0, 200.0],
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[0.0, 100.0],
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[100.0, 300.0],
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], headerExists: false, header: ['x', 'y']);
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debugPrint('Training data columns: ${data.header}');
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debugPrint('Training data rows: ${data.rows.length}');
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final model = LinearRegressor(data, 'y');
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final testDf = DataFrame(
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[[25.0]],
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headerExists: false,
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header: ['x'],
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);
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final prediction = model.predict(testDf);
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final value = prediction.rows.first.first;
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debugPrint('Predict x=25 → y=$value (expected ~150)');
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expect((value as num).toDouble(), closeTo(150, 5));
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});
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test('LinearRegressor multi-feature with constant column produces zeros', () {
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// isFlood=0 for all rows → zero-variance column → singular matrix
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final data = DataFrame([
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[0.0, 50.0, 14.0, 0.0, 1900.0],
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[0.0, 80.0, 14.0, 0.0, 2200.0],
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[2.0, 50.0, 14.0, 0.0, 5000.0],
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[4.0, 50.0, 14.0, 0.0, 9500.0],
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], headerExists: false, header: [
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'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
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]);
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final model = LinearRegressor(data, 'deliveryMs');
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final testDf = DataFrame(
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[[2.0, 50.0, 14.0, 0.0]],
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headerExists: false,
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header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('With constant isFlood column: hops=2 → ${(pred as num).round()}ms (likely 0)');
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});
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test('LinearRegressor 2-feature works correctly', () {
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// Just pathLength + messageBytes → deliveryMs
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final data = DataFrame([
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[0.0, 50.0, 1900.0],
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[0.0, 80.0, 2200.0],
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[2.0, 50.0, 5000.0],
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[2.0, 80.0, 5500.0],
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[4.0, 50.0, 9500.0],
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[4.0, 80.0, 10000.0],
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[0.0, 30.0, 1800.0],
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[2.0, 30.0, 4800.0],
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[4.0, 30.0, 9000.0],
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[0.0, 60.0, 2000.0],
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], headerExists: false, header: ['pathLength', 'messageBytes', 'deliveryMs']);
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final model = LinearRegressor(data, 'deliveryMs');
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for (final hops in [0.0, 2.0, 4.0]) {
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final testDf = DataFrame(
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[[hops, 50.0]],
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headerExists: false,
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header: ['pathLength', 'messageBytes'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('2-feature: hops=$hops → ${(pred as num).round()}ms');
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}
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});
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test('LinearRegressor multi-feature with variance in all columns', () {
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// Mix flood and direct so isFlood has variance
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final data = DataFrame([
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[0.0, 50.0, 14.0, 0.0, 1900.0],
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[0.0, 80.0, 10.0, 0.0, 2200.0],
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[2.0, 50.0, 16.0, 0.0, 5000.0],
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[2.0, 80.0, 20.0, 0.0, 5500.0],
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[4.0, 50.0, 8.0, 0.0, 9500.0],
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[4.0, 80.0, 12.0, 0.0, 10000.0],
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[-1.0, 40.0, 14.0, 1.0, 5000.0],
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[-1.0, 60.0, 18.0, 1.0, 6500.0],
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[-1.0, 30.0, 10.0, 1.0, 4000.0],
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[-1.0, 80.0, 22.0, 1.0, 7000.0],
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], headerExists: false, header: [
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'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
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]);
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final model = LinearRegressor(data, 'deliveryMs');
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for (final tc in [
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[0.0, 50.0, 14.0, 0.0],
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[2.0, 50.0, 14.0, 0.0],
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[4.0, 50.0, 14.0, 0.0],
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[-1.0, 50.0, 14.0, 1.0],
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]) {
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final testDf = DataFrame(
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[tc],
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headerExists: false,
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header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('4-feature: hops=${tc[0]} flood=${tc[3]} → ${(pred as num).round()}ms');
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}
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});
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}
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164
test/services/timeout_prediction_service_test.dart
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test/services/timeout_prediction_service_test.dart
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import 'package:flutter/foundation.dart';
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import 'package:flutter_test/flutter_test.dart';
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import 'package:meshcore_open/models/delivery_observation.dart';
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import 'package:meshcore_open/services/timeout_prediction_service.dart';
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void main() {
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late TimeoutPredictionService service;
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setUp(() {
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service = TimeoutPredictionService.noStorage();
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});
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test('trains on sample data and predicts sensible timeouts', () {
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// Simulate realistic delivery data:
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// Direct 0-hop messages: ~1500-2500ms
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// 2-hop messages: ~4000-6000ms
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// 4-hop messages: ~8000-12000ms
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// Flood messages: ~3000-8000ms
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final sampleData = [
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// 0-hop direct
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_obs(pathLength: 0, messageBytes: 20, deliveryMs: 1800),
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_obs(pathLength: 0, messageBytes: 50, deliveryMs: 2100),
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_obs(pathLength: 0, messageBytes: 80, deliveryMs: 2400),
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_obs(pathLength: 0, messageBytes: 30, deliveryMs: 1925),
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// 2-hop direct
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_obs(pathLength: 2, messageBytes: 40, deliveryMs: 4500),
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_obs(pathLength: 2, messageBytes: 60, deliveryMs: 5200),
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_obs(pathLength: 2, messageBytes: 25, deliveryMs: 4100),
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// 4-hop direct
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_obs(pathLength: 4, messageBytes: 50, deliveryMs: 9800),
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_obs(pathLength: 4, messageBytes: 30, deliveryMs: 8500),
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_obs(pathLength: 4, messageBytes: 70, deliveryMs: 10570),
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// Flood
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_obs(pathLength: -1, messageBytes: 40, deliveryMs: 5000),
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_obs(pathLength: -1, messageBytes: 60, deliveryMs: 6500),
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];
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// Feed all observations
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for (final obs in sampleData) {
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service.recordObservation(
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contactKey: obs.contactKey,
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pathLength: obs.pathLength,
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messageBytes: obs.messageBytes,
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tripTimeMs: obs.deliveryMs,
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);
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}
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expect(service.hasModel, isTrue);
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expect(service.observationCount, equals(12));
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// Predict for different scenarios
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final direct0 = service.predictTimeout(pathLength: 0, messageBytes: 50);
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final direct2 = service.predictTimeout(pathLength: 2, messageBytes: 50);
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final direct4 = service.predictTimeout(pathLength: 4, messageBytes: 50);
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final flood = service.predictTimeout(pathLength: -1, messageBytes: 50);
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// All should return non-null (model is trained)
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expect(direct0, isNotNull);
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expect(direct2, isNotNull);
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expect(direct4, isNotNull);
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expect(flood, isNotNull);
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// More hops should predict longer timeouts
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expect(direct4!, greaterThan(direct2!));
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expect(direct2, greaterThan(direct0!));
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// All should be within the clamp range
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expect(direct0, greaterThanOrEqualTo(2000));
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expect(direct4, lessThanOrEqualTo(120000));
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// Print predictions for visibility
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debugPrint('Predictions (with 1.5x safety margin):');
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debugPrint(' 0-hop direct: ${direct0}ms');
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debugPrint(' 2-hop direct: ${direct2}ms');
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debugPrint(' 4-hop direct: ${direct4}ms');
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debugPrint(' flood: ${flood}ms');
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});
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test('returns null before minimum observations', () {
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for (var i = 0; i < TimeoutPredictionService.minObservations - 1; i++) {
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service.recordObservation(
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contactKey: 'abc',
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pathLength: 0,
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messageBytes: 50,
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tripTimeMs: 2000,
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);
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}
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expect(service.hasModel, isFalse);
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expect(service.predictTimeout(pathLength: 0, messageBytes: 50), isNull);
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});
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test('caps observations at maxObservations', () {
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for (var i = 0; i < TimeoutPredictionService.maxObservations + 20; i++) {
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service.recordObservation(
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contactKey: 'abc',
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pathLength: 0,
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messageBytes: 50,
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tripTimeMs: 2000 + i,
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);
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}
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expect(
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service.observationCount,
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equals(TimeoutPredictionService.maxObservations),
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);
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});
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test('blends per-contact stats after enough observations', () {
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// Train with mixed contacts and varied features:
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// contactA is fast (0-hop), contactB is slow (2-hop)
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for (var i = 0; i < 12; i++) {
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service.recordObservation(
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contactKey: 'contactA',
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pathLength: 0,
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messageBytes: 30 + i,
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tripTimeMs: 1500,
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);
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service.recordObservation(
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contactKey: 'contactB',
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pathLength: 2,
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messageBytes: 30 + i,
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tripTimeMs: 8000,
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);
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}
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final predA = service.predictTimeout(
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contactKey: 'contactA',
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pathLength: 0,
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messageBytes: 50,
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);
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final predB = service.predictTimeout(
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contactKey: 'contactB',
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pathLength: 0,
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messageBytes: 50,
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);
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expect(predA, isNotNull);
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expect(predB, isNotNull);
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// Contact B (slow) should have a higher predicted timeout than A (fast)
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expect(predB!, greaterThan(predA!));
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debugPrint('Per-contact blending:');
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debugPrint(' contactA (fast): ${predA}ms');
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debugPrint(' contactB (slow): ${predB}ms');
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});
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}
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DeliveryObservation _obs({
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required int pathLength,
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required int messageBytes,
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required int deliveryMs,
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String contactKey = 'test_contact',
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}) {
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return DeliveryObservation(
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contactKey: contactKey,
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pathLength: pathLength,
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messageBytes: messageBytes,
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secondsSinceLastRx: 5,
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isFlood: pathLength < 0,
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deliveryMs: deliveryMs,
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timestamp: DateTime.now(),
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);
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}
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