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
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
