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.
2026-07-11 19:17:04 +10:00 · 2026-03-14 16:56:11 -07:00
parent 8b280b37be
commit 2ee2358ecc
9 changed files with 683 additions and 20 deletions
@@ -0,0 +1,122 @@
+import 'package:flutter/foundation.dart';
+import 'package:flutter_test/flutter_test.dart';
+import 'package:ml_algo/ml_algo.dart';
+import 'package:ml_dataframe/ml_dataframe.dart';
+
+void main() {
+  test('LinearRegressor basic sanity check', () {
+    // Simple: y = 2x + 100
+    final data = DataFrame([
+      [1.0, 102.0],
+      [2.0, 104.0],
+      [3.0, 106.0],
+      [4.0, 108.0],
+      [5.0, 110.0],
+      [10.0, 120.0],
+      [20.0, 140.0],
+      [50.0, 200.0],
+      [0.0, 100.0],
+      [100.0, 300.0],
+    ], headerExists: false, header: ['x', 'y']);
+
+    debugPrint('Training data columns: ${data.header}');
+    debugPrint('Training data rows: ${data.rows.length}');
+
+    final model = LinearRegressor(data, 'y');
+
+    final testDf = DataFrame(
+      [[25.0]],
+      headerExists: false,
+      header: ['x'],
+    );
+
+    final prediction = model.predict(testDf);
+    final value = prediction.rows.first.first;
+    debugPrint('Predict x=25 → y=$value (expected ~150)');
+    expect((value as num).toDouble(), closeTo(150, 5));
+  });
+
+  test('LinearRegressor multi-feature with constant column produces zeros', () {
+    // isFlood=0 for all rows → zero-variance column → singular matrix
+    final data = DataFrame([
+      [0.0, 50.0, 14.0, 0.0, 1900.0],
+      [0.0, 80.0, 14.0, 0.0, 2200.0],
+      [2.0, 50.0, 14.0, 0.0, 5000.0],
+      [4.0, 50.0, 14.0, 0.0, 9500.0],
+    ], headerExists: false, header: [
+      'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
+    ]);
+
+    final model = LinearRegressor(data, 'deliveryMs');
+    final testDf = DataFrame(
+      [[2.0, 50.0, 14.0, 0.0]],
+      headerExists: false,
+      header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
+    );
+    final pred = model.predict(testDf).rows.first.first;
+    debugPrint('With constant isFlood column: hops=2 → ${(pred as num).round()}ms (likely 0)');
+  });
+
+  test('LinearRegressor 2-feature works correctly', () {
+    // Just pathLength + messageBytes → deliveryMs
+    final data = DataFrame([
+      [0.0, 50.0, 1900.0],
+      [0.0, 80.0, 2200.0],
+      [2.0, 50.0, 5000.0],
+      [2.0, 80.0, 5500.0],
+      [4.0, 50.0, 9500.0],
+      [4.0, 80.0, 10000.0],
+      [0.0, 30.0, 1800.0],
+      [2.0, 30.0, 4800.0],
+      [4.0, 30.0, 9000.0],
+      [0.0, 60.0, 2000.0],
+    ], headerExists: false, header: ['pathLength', 'messageBytes', 'deliveryMs']);
+
+    final model = LinearRegressor(data, 'deliveryMs');
+
+    for (final hops in [0.0, 2.0, 4.0]) {
+      final testDf = DataFrame(
+        [[hops, 50.0]],
+        headerExists: false,
+        header: ['pathLength', 'messageBytes'],
+      );
+      final pred = model.predict(testDf).rows.first.first;
+      debugPrint('2-feature: hops=$hops → ${(pred as num).round()}ms');
+    }
+  });
+
+  test('LinearRegressor multi-feature with variance in all columns', () {
+    // Mix flood and direct so isFlood has variance
+    final data = DataFrame([
+      [0.0, 50.0, 14.0, 0.0, 1900.0],
+      [0.0, 80.0, 10.0, 0.0, 2200.0],
+      [2.0, 50.0, 16.0, 0.0, 5000.0],
+      [2.0, 80.0, 20.0, 0.0, 5500.0],
+      [4.0, 50.0, 8.0, 0.0, 9500.0],
+      [4.0, 80.0, 12.0, 0.0, 10000.0],
+      [-1.0, 40.0, 14.0, 1.0, 5000.0],
+      [-1.0, 60.0, 18.0, 1.0, 6500.0],
+      [-1.0, 30.0, 10.0, 1.0, 4000.0],
+      [-1.0, 80.0, 22.0, 1.0, 7000.0],
+    ], headerExists: false, header: [
+      'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
+    ]);
+
+    final model = LinearRegressor(data, 'deliveryMs');
+
+    for (final tc in [
+      [0.0, 50.0, 14.0, 0.0],
+      [2.0, 50.0, 14.0, 0.0],
+      [4.0, 50.0, 14.0, 0.0],
+      [-1.0, 50.0, 14.0, 1.0],
+    ]) {
+      final testDf = DataFrame(
+        [tc],
+        headerExists: false,
+        header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
+      );
+      final pred = model.predict(testDf).rows.first.first;
+      debugPrint('4-feature: hops=${tc[0]} flood=${tc[3]} → ${(pred as num).round()}ms');
+    }
+  });
+}