def local_prediction(deepnets, test_reader, output, args, exclude=None): """Get local deepnet and issue prediction """ kwargs = {"full": True} if args.operating_point_: kwargs.update({"operating_point": args.operating_point_}) # Only one deepnet at present local_deepnet = Deepnet(deepnets[0], api=args.retrieve_api_) for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_deepnet.predict(input_data_dict, **kwargs) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def local_prediction(deepnets, test_reader, output, args, exclude=None): """Get local deepnet and issue prediction """ # Only one deepnet at present local_deepnet = Deepnet(deepnets[0], api=args.retrieve_api_) test_set_header = test_reader.has_headers() for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_deepnet.predict( input_data_dict, by_name=test_set_header) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
def local_prediction(deepnets, test_reader, output, args, exclude=None): """Get local deepnet and issue prediction """ kwargs = {"full": True} if args.operating_point_: kwargs.update({"operating_point": args.operating_point_}) # Only one deepnet at present local_deepnet = Deepnet(deepnets[0], api=args.retrieve_api_) for input_data in test_reader: input_data_dict = test_reader.dict(input_data, filtering=False) prediction_info = local_deepnet.predict( input_data_dict, **kwargs) write_prediction(prediction_info, output, args.prediction_info, input_data, exclude)
"YEAR": 1, "DATE": 1, "Index_": 1, "ACCNUM": 1, "X": 1, "Y": 1, "AUTOMOBILE": "yes", "MOTORCYCLE": "yes", "CYCLIST": "yes", "STREET1": "ave", "STREET2": "ave", "DRIVACT": "Driving Properly", "MANOEUVER": "Going Ahead", "DRIVCOND": "Normal", "FATAL_NO": 1, "VEHTYPE": "automobile", "INITDIR": "West", "DATE.hour": 1, "DATE.day-of-month": 1, "DATE.day-of-week": 1, "DATE.year": 1, "DATE.month": 1 } deepnet.predict(input_data, full=True) # # input_data: dict for the input values # (e.g. {"petal length": 1, "sepal length": 3}) # full: if set to True, the output will be a dictionary that includes all the # available information about the prediction. The attributes vary depending # on the ensemble type. Please check: # https://bigml.readthedocs.io/en/latest/#local-deepnet-predictions