def optimize_value_creation_test(self):
"""Testing the first part of optimize_forecasting_method."""
self.bfm._requiredParameters = ["param1", "param2", "param3", "param4", "param5"]
try:
GridSearch(SMAPE, -1).optimize_forecasting_method(self.timeSeries, self.bfm)
# we looped down to the NotImplemetedError of BaseMethod.execute
except NotImplementedError:
pass
else:
assert False # pragma: no cover
self.bfm._parameterIntervals = {
"param3": [0.0, 1.0, True, True],
"param4": [0.0, 1.0, True, True],
"param5": [0.0, 1.0, True, True]
}
try:
GridSearch(SMAPE, -5).optimize_forecasting_method(self.timeSeries, self.bfm)
# we looped down to the NotImplemetedError of BaseMethod.execute
except NotImplementedError:
pass
else:
assert False # pragma: no cover
def inner_optimization_result_accuracy_test(self):
"""Test for the correct result of a GridSearch optimization."""
fm = ExponentialSmoothing()
startingPercentage = 0.0
endPercentage = 100.0
# manually select the best alpha
self.timeSeries.normalize("second")
results = []
for smoothingFactor in [alpha / 100.0 for alpha in xrange(1, 100)]: # pragma: no cover
fm.set_parameter("smoothingFactor", smoothingFactor)
resultTS = self.timeSeries.apply(fm)
error = SMAPE()
error.initialize(self.timeSeries, resultTS)
results.append([error, smoothingFactor])
bestManualResult = min(results, key=lambda item: item[0].get_error(startingPercentage, endPercentage))
# automatically determine the best alpha using GridSearch
gridSearch = GridSearch(SMAPE, precision=-4)
# used, because we test a submethod here
gridSearch._startingPercentage = startingPercentage
gridSearch._endPercentage = endPercentage
result = gridSearch.optimize_forecasting_method(self.timeSeries, fm)
# the grid search should have determined the same alpha
bestManualAlpha = bestManualResult[1]
errorManualResult = bestManualResult[0].get_error()
bestGridSearchAlpha = result[1]["smoothingFactor"]
errorGridSearchResult = result[0].get_error()
assert errorManualResult > errorGridSearchResult
def create_generator_test(self):
"""Test the parameter generation function."""
# initialize a correct result
precision = 10**-2
values_one = [i * precision for i in range(1, 100)]
values_two = [i * precision for i in range(201)]
generator_one = GridSearch(
SMAPE, precision=-2)._generate_next_parameter_value(
"parameter_one", self.bfm)
generator_two = GridSearch(
SMAPE, precision=-2)._generate_next_parameter_value(
"parameter_two", self.bfm)
generated_one = [val for val in generator_one]
generated_two = [val for val in generator_two]
assert len(values_one) == len(generated_one)
assert len(values_two) == len(generated_two)
for idx in range(len(values_one)):
value = str(values_one[idx])[:12]
assert str(value) == str(generated_one[idx])[:len(value)]
for idx in range(len(values_two)):
value = str(values_two[idx])[:12]
assert str(value) == str(generated_two[idx])[:len(value)]
def outer_optimization_result_test(self):
"""Test the multiple method optimization."""
fm1 = ExponentialSmoothing()
fm2 = HoltMethod()
self.timeSeries.normalize("second")
# automatically determine the best alpha using GridSearch
gridSearch = GridSearch(SMAPE, precision=-2)
result = gridSearch.optimize(self.timeSeries, [fm1, fm2])
def optimize_exception_test(self):
"""Test for exception while calling GridSearch.optimize."""
try:
GridSearch(SMAPE, -2).optimize(self.timeSeries)
except ValueError:
pass
else:
assert False # pragma: no cover
try:
GridSearch(SMAPE, -1).optimize(self.timeSeries, [self.bfm])
# we looped down to the NotImplemetedError of BaseMethod.execute
except NotImplementedError:
pass
else:
assert False # pragma: no cover
def optimization_loop_test(self):
"""Testing the optimozation loop."""
gridSearch = GridSearch(SMAPE, precision=-2)
def crap_execute(ignoreMe):
ts = self.timeSeries.to_twodim_list()
ts = TimeSeries.from_twodim_list(ts)
for entry in ts:
entry[0] += 0.1
return ts
self.bfm.execute = crap_execute
result = gridSearch.optimization_loop(self.timeSeries, self.bfm, [], {})
assert result == []
