def initialization_error_test(self):
"""Test for the exceptions raised during initialization."""
MeanAbsoluteScaledError(minimalErrorCalculationPercentage=60.0,
historyLength=20.0)
try:
MeanAbsoluteScaledError(60.0, 0.0)
except ValueError:
pass
else:
assert False # pragma: no cover
try:
MeanAbsoluteScaledError(60.0, -12.0)
except ValueError:
pass
else:
assert False # pragma: no cover
try:
MeanAbsoluteScaledError(60.0, 120.0)
except ValueError:
pass
else:
assert False # pragma: no cover
try:
MeanAbsoluteScaledError(60.0, 60.0)
except ValueError:
pass
else:
assert False # pragma: no cover
def calculate_historic_means_test(self):
"""Test the calculation of the historic means."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
# 2 2 1 4 3 3 3 3 2 5 5 3 4 1 6 5 4 2 2
# Sum(History) 12 13 14 16 14 16 18 18 19 18 19 19 20 18 19
correctResult = [ 2.4, 2.6, 2.8, 3.2, 2.8, 3.2, 3.6, 3.6, 3.8, 3.6, 3.8, 3.8, 4.0, 3.6]
tsOrg = TimeSeries.from_twodim_list(dataOrg)
mase = MeanAbsoluteScaledError(historyLength=5)
result = mase._get_historic_means(tsOrg)
assert result == correctResult
def calculate_historic_means_test(self):
"""Test the calculation of the historic means."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
# 2 2 1 4 3 3 3 3 2 5 5 3 4 1 6 5 4 2 2
# Sum(History) 12 13 14 16 14 16 18 18 19 18 19 19 20 18 19
correctResult = [ 2.4, 2.6, 2.8, 3.2, 2.8, 3.2, 3.6, 3.6, 3.8, 3.6, 3.8, 3.8, 4.0, 3.6]
tsOrg = TimeSeries.from_twodim_list(dataOrg)
mase = MeanAbsoluteScaledError(historyLength=5)
result = mase._get_historic_means(tsOrg)
assert result == correctResult
def error_calculation_test(self):
"""Testing for the correct MASE calculation.
History length is 5 in this test.
"""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
# 2 2 1 4 3 3 3 3 2 5 5 3 4 1 6 5 4 2 2
# Sum(History) 12 13 14 16 14 16 18 18 19 18 19 19 20 18 19
# Mean(History) # # # # # 2.4 2.6 2.8 3.2 2.8 3.2 3.6 3.6 3.8 3.6 3.8 3.8 4.0 3.6 3.8
# AD 3 0 8 3 3 2 3 2 1 2 1 6 16 2
# Sum(AD) 3 3 11 14 17 19 22 24 25 27 28 34 50 52
# MAD 3 1.5 3.666 3.5 3.4 3.166 3.142 3 2.777 2.7 2.545 2.833 3.571 3.714
# MASE (0% - 100%) 1.25 0.625 1.527 1.458 1.416 1.319 1.309 1.25 1.157 1.125 1.06 1.18 1.602 1.547
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
historyLength = 5
em = MeanAbsoluteScaledError(historyLength=historyLength)
em.initialize(tsOrg, tsFor)
# check for error calculation depending on a specific endpoint
correctResult = [1.25, 0.625, 1.527, 1.458, 1.416, 1.319, 1.309, 1.25, 1.157, 1.125, "1.060", "1.180", 1.602, 1.547]
percentage = 100.0 / len(correctResult) + 0.2
for errVal in range(14):
endPercentage = percentage * (errVal + 1)
# set maximum percentage
if endPercentage > 100.0:
endPercentage = 100.0
calcErr = str(em.get_error(endPercentage=endPercentage))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes
for errVal in range(14):
endDate = dataOrg[errVal + 6][0]
calcErr = str(em.get_error(endDate=endDate))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes, "%s != %s" % (calcErr, correctRes)
em.get_error(startDate=7.0)
try:
em.get_error(startDate=42.23)
except ValueError:
pass
else:
assert False # pragma: no cover
def error_calculation_test(self):
"""Testing for the correct MASE calculation.
History length is 5 in this test.
"""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
# 2 2 1 4 3 3 3 3 2 5 5 3 4 1 6 5 4 2 2
# Sum(History) 12 13 14 16 14 16 18 18 19 18 19 19 20 18 19
# Mean(History) # # # # # 2.4 2.6 2.8 3.2 2.8 3.2 3.6 3.6 3.8 3.6 3.8 3.8 4.0 3.6 3.8
# AD 3 0 8 3 3 2 3 2 1 2 1 6 16 2
# Sum(AD) 3 3 11 14 17 19 22 24 25 27 28 34 50 52
# MAD 3 1.5 3.666 3.5 3.4 3.166 3.142 3 2.777 2.7 2.545 2.833 3.571 3.714
# MASE (0% - 100%) 1.25 0.625 1.527 1.458 1.416 1.319 1.309 1.25 1.157 1.125 1.06 1.18 1.602 1.547
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
historyLength = 5
em = MeanAbsoluteScaledError(historyLength=historyLength)
em.initialize(tsOrg, tsFor)
# check for error calculation depending on a specific endpoint
correctResult = [1.25, 0.625, 1.527, 1.458, 1.416, 1.319, 1.309, 1.25, 1.157, 1.125, "1.060", "1.180", 1.602, 1.547]
percentage = 100.0 / len(correctResult) + 0.2
for errVal in xrange(14):
endPercentage = percentage * (errVal + 1)
# set maximum percentage
if endPercentage > 100.0:
endPercentage = 100.0
calcErr = str(em.get_error(endPercentage=endPercentage))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes
for errVal in xrange(14):
endDate = dataOrg[errVal + 6][0]
calcErr = str(em.get_error(endDate=endDate))[:5]
correctRes = str(correctResult[errVal])[:5]
assert calcErr == correctRes, "%s != %s" % (calcErr, correctRes)
em.get_error(startDate=7.0)
try:
em.get_error(startDate=42.23)
except ValueError:
pass
else:
assert False # pragma: no cover
def local_error_calculation_test(self):
"""Testing the mean absolute error calculation of the MASE."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
historyLength = 5
em = MeanAbsoluteScaledError(historyLength=historyLength)
# A history length of 5 implies that the first 6 values have to be ignored for error calculation
historyLength += 1
dataOrg = dataOrg[historyLength:]
dataFor = dataFor[historyLength:]
for orgValue, forValue in zip(dataOrg, dataFor):
difference = orgValue[1] - forValue[1]
difference = abs(difference)
assert difference == em.local_error([orgValue[1]], [forValue[1]])
def local_error_calculation_test(self):
"""Testing the mean absolute error calculation of the MASE."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
historyLength = 5
em = MeanAbsoluteScaledError(historyLength=historyLength)
# A history length of 5 implies that the first 6 values have to be ignored for error calculation
historyLength += 1
dataOrg = dataOrg[historyLength:]
dataFor = dataFor[historyLength:]
for orgValue, forValue in zip(dataOrg, dataFor):
difference = orgValue[1] - forValue[1]
difference = abs(difference)
assert difference == em.local_error([orgValue[1]], [forValue[1]])
def initialization_test(self):
"""Test for MASE initialization."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17],
[6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31],
[11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13],
[16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13],
[6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34],
[11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14],
[16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
em = MeanAbsoluteScaledError(historyLength=5)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(
em._historicMeans
), "For each error value an historic mean has to exsist."
try:
em.initialize(tsOrg, tsFor)
except StandardError:
pass
else:
assert False # pragma: no cover
em = MeanAbsoluteScaledError(historyLength=20.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(
em._historicMeans
), "For each error value an historic mean has to exsist."
assert em._historyLength == 4, "The history is %s entries long. 4 were expected." % em._historyLength
em = MeanAbsoluteScaledError(historyLength=40.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(
em._historicMeans
), "For each error value an historic mean has to exsist."
assert em._historyLength == 8, "The history is %s entries long. 8 were expected." % em._historyLength
def initialization_test(self):
"""Test for MASE initialization."""
dataOrg = [[1.0, 10], [2.0, 12], [3.0, 14], [4.0, 13], [5.0, 17], [6.0, 20], [7.0, 23], [8.0, 26], [9.0, 29], [10.0, 31], [11.0, 26], [12.0, 21], [13.0, 18], [14.0, 14], [15.0, 13], [16.0, 19], [17.0, 24], [18.0, 28], [19.0, 30], [20.0, 32]]
dataFor = [[1.0, 11], [2.0, 13], [3.0, 14], [4.0, 11], [5.0, 13], [6.0, 18], [7.0, 20], [8.0, 26], [9.0, 21], [10.0, 34], [11.0, 23], [12.0, 23], [13.0, 15], [14.0, 12], [15.0, 14], [16.0, 17], [17.0, 25], [18.0, 22], [19.0, 14], [20.0, 30]]
tsOrg = TimeSeries.from_twodim_list(dataOrg)
tsFor = TimeSeries.from_twodim_list(dataFor)
em = MeanAbsoluteScaledError(historyLength=5)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
try:
em.initialize(tsOrg, tsFor)
except StandardError:
pass
else:
assert False # pragma: no cover
em = MeanAbsoluteScaledError(historyLength=20.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
assert em._historyLength == 4, "The history is %s entries long. 4 were expected." % em._historyLength
em = MeanAbsoluteScaledError(historyLength=40.0)
em.initialize(tsOrg, tsFor)
assert len(em._errorValues) == len(em._historicMeans), "For each error value an historic mean has to exsist."
assert em._historyLength == 8, "The history is %s entries long. 8 were expected." % em._historyLength
