test_runtime.py

#!/usr/bin/env python3
"""Test the PipelineRuntime with DiabetesSignificanceModel"""

from collections import deque
from typing import Dict, Any, Tuple
import numpy as np

# Import Sundew
from sundew.config_presets import get_preset
from sundew.interfaces import ProcessingContext, SignificanceModel
from sundew.runtime import PipelineRuntime, SimpleGatingStrategy, SimpleControlPolicy, SimpleEnergyModel

print("Testing Sundew Diabetes Watch Runtime...")
print("=" * 60)

# Simple test significance model
class TestDiabetesModel(SignificanceModel):
    def __init__(self, config):
        self.glucose_history = deque(maxlen=12)

    def compute_significance(self, context: ProcessingContext) -> Tuple[float, Dict[str, Any]]:
        # THIS IS THE FIX - access context.features properly
        features = context.features if hasattr(context, 'features') else {}
        glucose = float(features.get("glucose_mgdl", 120.0)) if isinstance(features, dict) else 120.0

        # Simple risk calc
        if glucose < 70:
            sig = 0.9
        elif glucose > 180:
            sig = 0.8
        else:
            sig = 0.3

        return float(sig), {"glucose": glucose}

    def update(self, context, outcome):
        pass

    def get_parameters(self):
        return {}

    def set_parameters(self, params):
        pass

# Create runtime
print("\n1. Creating PipelineRuntime...")
config = get_preset("tuned_v2")
config.target_activation_rate = 0.15

model = TestDiabetesModel({})

runtime = PipelineRuntime(
    config=config,
    significance_model=model,
    gating_strategy=SimpleGatingStrategy(config.hysteresis_gap),
    control_policy=SimpleControlPolicy(config),
    energy_model=SimpleEnergyModel(
        processing_cost=config.base_processing_cost,
        idle_cost=config.dormant_tick_cost,
    ),
)
print("✅ Runtime created successfully!")

# Test with some data
print("\n2. Testing with sample glucose values...")
test_values = [150, 65, 200, 100, 75, 185]

for i, glucose in enumerate(test_values):
    context = ProcessingContext(
        timestamp=float(i),
        sequence_id=i,
        features={
            "glucose_mgdl": glucose,
            "roc_mgdl_min": 0.0,
        },
        history=[],
        metadata={},
    )

    result = runtime.process(context)

    # THIS IS THE FIX - use correct attribute names
    print(f"  Event {i}: glucose={glucose} mg/dL, "
          f"sig={result.significance:.2f}, "
          f"threshold={result.threshold_used:.2f}, "
          f"activated={result.activated}")

print("\n✅ ALL TESTS PASSED!")
print("=" * 60)
print("\nThe fixes are working correctly:")
print("  ✓ context.features accessed properly")
print("  ✓ result.threshold_used (not result.threshold)")
print("  ✓ result.energy_consumed available")
print("\nYou can now run the full Streamlit app!")