def test_multivariate_gaussian(self):
po_normal = norm(1.1, 5)
po_anomaly = norm(1.5, 7)
po_normal2 = norm(2.2, 10)
po_anomaly2 = norm(3, 12)
gs_normal = norm(1, 12)
gs_anomaly = norm(2, 30)
normal_len = 100
anomaly_len = 15
data = np.column_stack(
[
list(po_normal.rvs(normal_len)) + list(po_anomaly.rvs(anomaly_len)),
list(po_normal2.rvs(normal_len)) + list(po_anomaly2.rvs(anomaly_len)),
list(gs_normal.rvs(normal_len)) + list(gs_anomaly.rvs(anomaly_len)),
]
)
anomaly_detector = pyisc.AnomalyDetector(
component_models=[
pyisc.P_Gaussian(0), # columns 1 and 0
pyisc.P_Gaussian(1), # columns 2 and 0
pyisc.P_Gaussian(2) # column 3
],
output_combination_rule=pyisc.cr_max
)
anomaly_detector.fit(data);
json = anomaly_detector.exportJSon()
print(json)
anomaly_detector2 = pyisc.AnomalyDetector(
component_models=[
pyisc.P_Gaussian(0), # columns 1 and 0
pyisc.P_Gaussian(1), # columns 2 and 0
pyisc.P_Gaussian(2) # column 3
],
output_combination_rule=pyisc.cr_max
)
anomaly_detector2.importJSon(json)
json2 = anomaly_detector2.exportJSon()
print(json2)
assert_allclose(anomaly_detector.anomaly_score(data), anomaly_detector2.anomaly_score(data))
self.assertEqual(json, json2)
def test_conditional_gaussian(self):
po_normal = norm(1.1, 5)
po_anomaly = norm(1.5, 7)
po_normal2 = norm(2.2, 10)
po_anomaly2 = norm(3, 12)
gs_normal = norm(1, 12)
gs_anomaly = norm(2, 30)
normal_len = 100
anomaly_len = 15
data = np.column_stack(
[
list(po_normal.rvs(normal_len)) + list(po_anomaly.rvs(anomaly_len)),
list(po_normal2.rvs(normal_len)) + list(po_anomaly2.rvs(anomaly_len)),
list(gs_normal.rvs(normal_len)) + list(gs_anomaly.rvs(anomaly_len)),
]
)
anomaly_detector = pyisc.AnomalyDetector(
component_models=[
pyisc.P_ConditionalGaussianCombiner([pyisc.P_ConditionalGaussian([0], [1]), pyisc.P_ConditionalGaussian([1], [2])])
],
output_combination_rule=pyisc.cr_max
)
anomaly_detector.fit(data);
json = anomaly_detector.exportJSon()
print(json)
anomaly_detector2 = pyisc.AnomalyDetector(
component_models=[
pyisc.P_ConditionalGaussianCombiner([pyisc.P_ConditionalGaussian([0], [1]), pyisc.P_ConditionalGaussian([1], [2])])
],
output_combination_rule=pyisc.cr_max
)
anomaly_detector2.importJSon(json)
json2 = anomaly_detector2.exportJSon()
print(json2)
self.assertEqual(json, json2)
assert_allclose(anomaly_detector.anomaly_score(data), anomaly_detector2.anomaly_score(data))
def test_something(self):
X = np.array([0.1, -0.1, 0.05, -0.01, 0.0, 0.11]).reshape((-1,1))
try:
ad = pyisc.AnomalyDetector(pyisc.P_Gaussian(1)).fit(X)
self.assertFalse(True, "the probability model use column index that is larger than the data's max column index")
except AssertionError:
pass # OK
ad = pyisc.AnomalyDetector(pyisc.P_Gaussian(0)).fit(X)
self.assertTrue(np.array_equal(ad.anomaly_score(X), ad.anomaly_score(X)))
def test_multivariate_gaussian(self):
from scipy.stats import poisson, norm
po_normal = poisson(10)
po_anomaly = poisson(25)
po_normal2 = poisson(2)
po_anomaly2 = poisson(3)
gs_normal = norm(1, 12)
gs_anomaly = norm(2, 30)
normal_len = 10000
anomaly_len = 15
data = np.column_stack([
[1] * (normal_len + anomaly_len),
list(po_normal.rvs(normal_len)) +
list(po_anomaly.rvs(anomaly_len)),
list(po_normal2.rvs(normal_len)) +
list(po_anomaly2.rvs(anomaly_len)),
list(gs_normal.rvs(normal_len)) +
list(gs_anomaly.rvs(anomaly_len)),
])
anomaly_detector = pyisc.AnomalyDetector(
component_models=[
pyisc.P_PoissonOnesided(1, 0), # columns 1 and 0
pyisc.P_Poisson(2, 0), # columns 2 and 0
pyisc.P_Gaussian(3) # column 3
],
output_combination_rule=pyisc.cr_max)
anomaly_detector.fit(data)
# This above should fail this test if the problem still occurs:
'''
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-5-ecd0c0a2a8d4> in <module>()
----> 1 anomaly_detector.fit(data);
C:\ProgramData\Anaconda3\envs\pyISC_py27\lib\site-packages\_pyisc_modules\BaseISC.pyc in fit(self, X, y)
313
314
--> 315 return self._fit(X,y)
316
317 def _fit(self,X,y=None):
C:\ProgramData\Anaconda3\envs\pyISC_py27\lib\site-packages\_pyisc_modules\BaseISC.pyc in _fit(self, X, y)
352
353 if data_object is not None:
--> 354 assert self._max_index < data_object.length() # ensure that data distribution has not to large index into the data
355
356 return self._fit(data_object)
AssertionError:
'''
assert True