def test_detect(self):
eword_size = 2
ewindow_factor = 2
elead_window_factor = 2
elag_window_factor = 2
inst = AssumptionFreeAA(word_size=eword_size,
window_factor=ewindow_factor,
lead_window_factor=elead_window_factor,
lag_window_factor=elag_window_factor)
data = np.array([1, 2, 2, 2, 4, 7, 8, 9, 1, 9, 5, 6, 7, 3, 2, 6])
escore = 0.125
ebitmp1 = np.array([[0., 0., 0., 0.], [0., 0., 1., 0.],
[0., 1., 0., 0.], [0., 0., 0., 0.]])
ebitmp2 = np.array([[0., 0., 0., 1.], [0., 0., 1., 0.],
[0., 0., 0., 0.], [0., 0., 0., 0.]])
actual = inst.detect(data)[0]
self.assertEqual(escore, actual.score,
'exp: %s; act: %s' % (escore, actual.score))
self.assertItemsEqual(
ebitmp1[0], actual.bitmp1[0],
'exp: %s; act: %s' % (ebitmp1[0], actual.bitmp1[0]))
self.assertItemsEqual(
ebitmp1[1], actual.bitmp1[1],
'exp: %s; act: %s' % (ebitmp1[1], actual.bitmp1[1]))
self.assertItemsEqual(
ebitmp2[0], actual.bitmp2[0],
'exp: %s; act: %s' % (ebitmp2[0], actual.bitmp2[0]))
self.assertItemsEqual(
ebitmp2[1], actual.bitmp2[1],
'exp: %s; act: %s' % (ebitmp2[1], actual.bitmp2[1]))
def test_detect(self):
eword_size = 2
ewindow_factor = 2
elead_window_factor = 2
elag_window_factor = 2
inst = AssumptionFreeAA(word_size=eword_size,
window_factor=ewindow_factor,
lead_window_factor=elead_window_factor,
lag_window_factor=elag_window_factor)
data = np.array([1, 2, 2, 2, 4, 7, 8, 9, 1, 9, 5, 6, 7, 3, 2, 6])
escore = 0.125
ebitmp1 = np.array([[0., 0., 0., 0.],
[0., 0., 1., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 0.]])
ebitmp2 = np.array([[0., 0., 0., 1.],
[0., 0., 1., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
actual = inst.detect(data)[0]
self.assertEqual(escore, actual.score,
'exp: %s; act: %s' % (escore, actual.score))
self.assertItemsEqual(ebitmp1[0], actual.bitmp1[0],
'exp: %s; act: %s' % (ebitmp1[0], actual.bitmp1[0]))
self.assertItemsEqual(ebitmp1[1], actual.bitmp1[1],
'exp: %s; act: %s' % (ebitmp1[1], actual.bitmp1[1]))
self.assertItemsEqual(ebitmp2[0], actual.bitmp2[0],
'exp: %s; act: %s' % (ebitmp2[0], actual.bitmp2[0]))
self.assertItemsEqual(ebitmp2[1], actual.bitmp2[1],
'exp: %s; act: %s' % (ebitmp2[1], actual.bitmp2[1]))
class AssumptionFreeDataTests():
@classmethod
def setUpClass(cls):
"""
Reads a test data set into memory
"""
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'dataset.dat')
cls._df = extract_hr_acc(read_data(filename))
def setUp(self,
window_size=1000, lead_window_factor=3,
lag_window_factor=30, word_size=10,
recursion_level=2, resampling_rate='10ms',
resampling_method='mean'):
"""
Copies the data set to an test's instance variable
and instantiates an anomaly detector.
"""
self._data = self._df.resample(rule=resampling_rate,
how=resampling_method)
# fill NAs forward
self._data = self._data.fillna(method='ffill')
# fill NAs backwards (fill any NAs at the start of all series)
self._data = self._data.fillna(method='bfill')
self._instance = AssumptionFreeAA(window_size=window_size,
lead_window_factor=lead_window_factor,
lag_window_factor=lag_window_factor,
word_size=word_size,
recursion_level=recursion_level)
def tearDown(self):
"""
Deletes instance's data.
"""
del self._data
del self._instance
gc.collect()
def runTest(self):
self._scores = []
self._bitmaps = []
for i in xrange(len(self._data)):
if np.isnan(self._data.ratio[i]):
print self._data.ix[i]
sys.exit()
result = self._instance.detect([self._data.ratio[i]])
if len(result) != 0:
self._scores.append(result[0].score)
self._bitmaps.append((result[0].bitmp1, result[0].bitmp2))
else:
self._scores.append(0.)
self._bitmaps.append(([], []))
def test_detect_not_enough_elements(self):
eword_size = 2
ewindow_factor = 2
elead_window_factor = 2
elag_window_factor = 2
inst = AssumptionFreeAA(word_size=eword_size,
window_factor=ewindow_factor,
lead_window_factor=elead_window_factor,
lag_window_factor=elag_window_factor)
data = np.array([1, 2, 2, 2, 4, 7, 8, 9, 1, 9])
elen_score = 0
actual = inst.detect(data)
self.assertEqual(elen_score, len(actual),
'exp: %s; act: %s' % (elen_score, len(actual)))
def test_detect_not_enough_elements(self):
eword_size = 2
ewindow_factor = 2
elead_window_factor = 2
elag_window_factor = 2
inst = AssumptionFreeAA(word_size=eword_size,
window_factor=ewindow_factor,
lead_window_factor=elead_window_factor,
lag_window_factor=elag_window_factor)
data = np.array([1, 2, 2, 2, 4, 7, 8, 9, 1, 9])
elen_score = 0
actual = inst.detect(data)
self.assertEqual(elen_score, len(actual),
'exp: %s; act: %s' % (elen_score, len(actual)))