Our safety scoring algorithm has been upgraded from a static, rule-based system to a predictive AI model for more dynamic and accurate risk assessments.
> impact
We've fundamentally overhauled the core safety scoring logic within Best Sleep Time, replacing the previous static, weighted algorithm with a new AI-powered predictive analysis model. The old system calculated a score based on a fixed set of rules, considering historical factors like alert frequency and density. The new system leverages a time-series forecasting model trained on historical alert data, including timestamps and locations, to generate a probabilistic forecast of future events.
The primary motivation for this shift was to move from a reactive to a predictive safety assessment. While the weighted algorithm was effective at summarizing past activity, it couldn't identify complex patterns, temporal correlations, or emerging trends that often precede safety events. By using an AI model, we can analyze subtle progressions and provide a more nuanced, forward-looking risk assessment that anticipates potential activity rather than just reacting to it.
This change directly impacts the safety recommendations provided to users. Instead of a simple score, the app now receives a probability forecast for the user's specific region and time window. This probabilistic output is then translated into our familiar recommendations ('sleep soundly', 'keep shoes nearby'), but they are now backed by a more sophisticated and dynamic prediction. For developers, this means interacting with a new predictive endpoint that provides a richer data object, including the raw probability and the final recommendation.
> Try this now
try this
# To get a predictive safety forecast, use the new PredictiveSafetyClient.
# This client interfaces with our AI model to generate a probabilistic risk assessment.
# 1. Import the new predictive safety client
from best_sleep_time.ai import PredictiveSafetyClient
# 2. Initialize the client, which connects to the prediction service
client = PredictiveSafetyClient()
# 3. Define the user's location and the time window you want to forecast for
user_location = {"lat": 34.0522, "lon": -118.2437} # Los Angeles
sleep_window_hours = 8
# 4. Request the safety forecast from the AI model
# The model analyzes recent alert patterns and predicts the likelihood
# of an event in the specified area and timeframe.
forecast = client.get_safety_forecast(
location=user_location,
time_window_hours=sleep_window_hours
)
# 5. The returned forecast object contains the raw probability and a human-readable recommendation
print(f"Predicted risk probability: {forecast.probability:.2%}")
# Expected output might be: Predicted risk probability: 12.75%
print(f"Recommendation: {forecast.recommendation}")
# Expected output might be: Recommendation: keep shoes nearby