nightr/server/nightr/app.py
2019-04-07 04:50:11 +02:00

109 lines
3.5 KiB
Python

import inspect
import json
import statistics
import timeit
from dataclasses import asdict
from datetime import timedelta
from logging import DEBUG
from typing import List
import requests_cache
from flask import Flask, jsonify, logging, request
from .strategies import miloStrats, iss, cars_in_traffic, tide_strat, upstairs_neighbour, bing, svm_strat, battery, just_eat, steam
from .util import Context
app = Flask(__name__)
logger = logging.create_logger(app)
logger.setLevel(DEBUG)
requests_cache.install_cache("requests_cache", expire_after=timedelta(minutes=2))
strategies = {
# name: (weight, probability function)
"TV2 News": miloStrats.tv2newsStrat,
"Australia": miloStrats.australiaStrat,
"Camera Image": miloStrats.camImgStrat,
"The International Space Station": iss.night_on_iss,
"Nearby Traffic situation": cars_in_traffic.cars_in_traffic,
"Tidal Measurements": tide_strat.is_tide,
"Legends of Nighttime": upstairs_neighbour.check_games,
"Bing AI": bing.clock,
"ML Parking": svm_strat.perform_svm_pred,
"Phone Battery Level": battery.battery_level,
"Pizza Availability": just_eat.do_just_eat_strat,
"Steam Players Online": steam.dota2_players,
}
@app.route("/", methods=["GET", "POST"])
def probabilities():
if request.method == "GET":
logger.warning("GET request: using default context parameters")
context = Context()
else:
phone_data = request.get_json(force=True)
logger.debug("phone_data:\n%s", json.dumps(phone_data, indent=2))
context = Context(**phone_data["data"])
#logger.debug("Context: %s", context)
predictions: List[dict] = []
for name, strategy in strategies.items():
try:
logger.debug("Executing %s..", name)
start = timeit.default_timer()
prediction = strategy(context)
stop = timeit.default_timer()
logger.debug("Execution time for %s: %ss", name, round(stop - start, 3 ))
except Exception as e:
logger.warning("Strategy '%s' failed:", name)
logger.exception(e)
continue
predictions.append({
"name": name,
"description": inspect.getdoc(strategy),
"weight": prediction.weight,
"weighted_probability": prediction.probability * prediction.weight,
"night": prediction.probability > 0.5,
**asdict(prediction),
})
mean = statistics.mean(p["weighted_probability"] for p in predictions)
median = statistics.median(p["weighted_probability"] for p in predictions)
night = mean > 0.5
# Invert if we're in Australia
if context.in_australia:
night = not night
for prediction in predictions:
prediction["night"] = not prediction["night"]
# Calculate contributions of predictions
weight_sum = sum(p["weight"] for p in predictions)
for prediction in predictions:
prediction["contribution"] = prediction["weight"] / weight_sum
# If this prediction disagrees with the consensus it contributed negatively
if prediction["night"] != night:
prediction["contribution"] *= -1
predictions.sort(key=lambda p: (p["contribution"], p["probability"]), reverse=True)
return jsonify({
"predictions": predictions,
"weighted_probabilities_mean": mean,
"weighted_probabilities_median": median,
"night": night,
})
def main():
app.run(host='0.0.0.0', debug=True)
if __name__ == '__main__':
main()