def setUp(self): """Initializes self.forecastingMethod.""" bfm = BaseForecastingMethod(["parameter_one", "parameter_two"]) bfm._parameterIntervals = {} bfm._parameterIntervals["parameter_one"] = [0.0, 1.0, False, False] bfm._parameterIntervals["parameter_two"] = [0.0, 2.0, True, True] self.bfm = bfm data = [[0.0, 0.0], [1.1, 0.2], [2.2, 0.6], [3.3, 0.2], [4.4, 0.3], [5.5, 0.5]] self.timeSeries = TimeSeries.from_twodim_list(data) self.timeSeries.normalize("second")
def get_optimizable_parameters_test(self): """Test get optimizable parameters.""" # Initialize parameter lists parameters = ["param1", "param2", "param3", "param4", "param5"] intervals = { "param3": [0.0, 1.0, True, True], "param4": [0.0, 1.0, True, True], "param5": [0.0, 1.0, True, True], "param6": [0.0, 1.0, True, True] } # initialize BaseForecastingMethod and set some parameter intervals bfm = BaseForecastingMethod(parameters, valuesToForecast=4, hasToBeNormalized=False, hasToBeSorted=True) bfm._parameterIntervals = intervals # check, if the BaseForecastingMethod returns the correct parameters correctResult = ["param3", "param4", "param5"] result = sorted(bfm.get_optimizable_parameters()) assert correctResult == result