def forecast(time_series, args, session_file=None): """Computes a time-series forecast """ local_time_series = TimeSeries(time_series, api=args.retrieve_api_) output = args.predictions # Local forecasts: Forecasts are computed locally message = u.dated("Creating local forecasts.\n") u.log_message(message, log_file=session_file, console=args.verbosity) input_data = [] if args.test_set is not None: input_data = [u.read_json(args.test_set)] elif args.horizon is not None: input_data = [{local_time_series.objective_id: { \ "horizon": args.horizon}}] write_forecasts(local_time_series.forecast(*input_data), output)
def remote_forecast(time_series, forecast_args, args, api, resume, prediction_file=None, session_file=None, path=None, log=None): """Computes a remote forecast. """ time_series_id = bigml.api.get_time_series_id( \ time_series) # if resuming, try to extract dataset form log files if resume: message = u.dated("Forecast not found. Resuming.\n") resume, forecast = c.checkpoint(c.is_forecast_created, path, debug=args.debug, message=message, log_file=session_file, console=args.verbosity) if not resume: local_time_series = TimeSeries(time_series, api=args.retrieve_api_) output = args.predictions if args.test_set is not None: input_data = u.read_json(args.test_set) elif args.horizon is not None: input_data = {local_time_series.objective_id: { \ "horizon": args.horizon}} forecast = create_forecast(time_series_id, input_data, forecast_args, args, api, session_file=session_file, path=path, log=log) write_forecasts(forecast["object"]["forecast"]["result"], output)
def create_local_time_series(step): world.local_time_series = TimeSeries(world.time_series["resource"], world.api)
def i_create_local_time_series_from_file(step, export_file): world.local_time_series = TimeSeries(res_filename(export_file))