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
