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#!/usr/bin/env python3
"""Test the DiabetesSignificanceModel"""
from collections import deque
from dataclasses import dataclass
from typing import Dict, Any, List, Tuple
# Mock ProcessingContext if not available
@dataclass
class ProcessingContext:
timestamp: float
sequence_id: int
features: Dict[str, Any]
history: List[Dict[str, Any]]
metadata: Dict[str, Any]
class DiabetesSignificanceModel:
"""Test version of the model"""
def __init__(self, config: Dict[str, Any]):
self.hypo_threshold = config.get("hypo_threshold", 70.0)
self.hyper_threshold = config.get("hyper_threshold", 180.0)
self.target_glucose = config.get("target_glucose", 100.0)
self.weights = {
"glycemic_deviation": 0.35,
"velocity_risk": 0.25,
"iob_risk": 0.15,
"cob_risk": 0.10,
"variability": 0.10,
"activity_risk": 0.05,
}
self.glucose_history: deque = deque(maxlen=12)
self.significance_ema = 0.5
self.ema_alpha = 0.15
def compute_significance(self, context: ProcessingContext) -> Tuple[float, Dict[str, Any]]:
# THIS IS THE FIXED VERSION
features = context.features if hasattr(context, 'features') else {}
# Extract features safely with proper dict access
glucose = float(features.get("glucose_mgdl", 120.0)) if isinstance(features, dict) else 120.0
print(f"✅ Successfully accessed features!")
print(f" Glucose: {glucose}")
print(f" Features type: {type(features)}")
print(f" Features is dict: {isinstance(features, dict)}")
return 0.5, {"components": {"test": 0.5}}
# Test it
if __name__ == "__main__":
print("Testing DiabetesSignificanceModel...")
context = ProcessingContext(
timestamp=1234567890.0,
sequence_id=0,
features={
'glucose_mgdl': 150.0,
'roc_mgdl_min': 2.0,
},
history=[],
metadata={},
)
config = {
'hypo_threshold': 70.0,
'hyper_threshold': 180.0,
'target_glucose': 100.0,
}
model = DiabetesSignificanceModel(config)
sig, explanation = model.compute_significance(context)
print(f"\nSignificance: {sig:.3f}")
print(f"✅ Model works correctly!")
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