def testInit(self, compute, configurationGet):
anomalyParams = {
'autoDetectWaitRecords': 100,
'autoDetectThreshold': 101,
'anomalyCacheRecords': 102,
'anomalyVectorType': 'tpc'
}
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc',
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), anomalyParams)
self.assertEqual(helper._autoDetectWaitRecords,
anomalyParams['autoDetectWaitRecords'])
self.assertEqual(helper._autoDetectThreshold,
anomalyParams['autoDetectThreshold'])
self.assertEqual(helper._history_length,
anomalyParams['anomalyCacheRecords'])
self.assertEqual(helper._vectorType,
anomalyParams['anomalyVectorType'])
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), None)
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertEqual(
helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertEqual(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertEqual(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
def testConfiguration(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertTrue(helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertTrue(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertTrue(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
def setUp(self): self.helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
class CLAClassifierHelperTest(unittest.TestCase):
"""CLAModelClassifierHelper unit tests."""
def setUp(self):
self.helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
@patch.object(Configuration, 'get')
@patch.object(CLAModelClassifierHelper, 'compute')
def testInit(self,compute, configurationGet):
anomalyParams = {
'autoDetectWaitRecords': 100,
'autoDetectThreshold': 101,
'anomalyCacheRecords': 102,
'anomalyVectorType': 'tpc'
}
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc',
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), anomalyParams)
self.assertEqual(helper._autoDetectWaitRecords,
anomalyParams['autoDetectWaitRecords'])
self.assertEqual(helper._autoDetectThreshold,
anomalyParams['autoDetectThreshold'])
self.assertEqual(helper._history_length,
anomalyParams['anomalyCacheRecords'])
self.assertEqual(helper._vectorType,
anomalyParams['anomalyVectorType'])
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), None)
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertEqual(helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertEqual(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertEqual(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
@patch.object(CLAModelClassifierHelper, 'compute')
def testRun(self,compute):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": [1,4,5],
"anomalyLabel": "Label"
}
compute.return_value = _CLAClassificationRecord(**state)
result = self.helper.run()
compute.assert_called_once_with()
self.assertEqual(result, state['anomalyLabel'])
def testGetLabels(self):
# No saved_states
self.helper.saved_states = []
self.assertEqual(self.helper.getLabels(), \
{'isProcessing': False, 'recordLabels': []})
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels, start=100, end=100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels, start=-100, end=-100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels, start=100, end=-100)
# Valid no threshold labels
values = {
'categoryRecencyList': [4, 5, 7],
}
self.helper.saved_categories = ['TestCategory']
categoryList = [1,1,1]
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
classifier._knn._categoryList = categoryList
results = self.helper.getLabels()
self.assertTrue('isProcessing' in results)
self.assertTrue('recordLabels' in results)
self.assertEqual(len(results['recordLabels']),
len(values['categoryRecencyList']))
for record in results['recordLabels']:
self.assertTrue(record['ROWID'] in values['categoryRecencyList'])
self.assertEqual(record['labels'], self.helper.saved_categories)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testAddLabel(self, _updateState, _getStateAnomalyVector):
self.helper.clamodel._getAnomalyClassifier().getSelf().getParameter.return_value = [1,2,3]
self.helper.saved_states = []
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel, start=100, end=100, labelName="test")
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel, start=100, end=100, labelName="test")
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel, start=-100, end=-100, labelName="test")
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel, start=100, end=-100, labelName="test")
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=[], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)]
results = self.helper.addLabel(11, 12, "Added")
# Verifies records were udpated
self.assertEqual(results, None)
self.assertTrue('Added' in self.helper.saved_states[1].anomalyLabel)
self.assertTrue(self.helper.saved_states[1].setByUser)
# Verifies record added to KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
knn.learn.assert_called_once_with(ANY, ANY, rowID=11)
# Verifies records after added label is recomputed
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testRemoveLabel(self, _updateState, _getStateAnomalyVector):
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.return_value = [10,11,12]
classifier._knn._numPatterns = 3
classifier._knn.removeIds.side_effect = self.mockRemoveIds
self.helper.saved_states = []
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels,)
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels, start=100, end=100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels, start=-100, end=-100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels, start=100, end=-100)
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)]
results = self.helper.removeLabels(11, 12, "Test")
self.assertEqual(results, {'status': 'success'})
self.assertTrue('Test' not in self.helper.saved_states[1].anomalyLabel)
# Verifies records removed from KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
self.assertEqual(knn.removeIds.mock_calls, [call([11]), call([])])
# Verifies records after removed record are updated
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testRemoveLabelNoFilter(self, _updateState, _getStateAnomalyVector):
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
values = {
'categoryRecencyList': [10, 11, 12]
}
classifier.getParameter.side_effect = values.get
classifier._knn._numPatterns = 3
classifier._knn.removeIds.side_effect = self.mockRemoveIds
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)]
results = self.helper.removeLabels(11, 12)
self.assertEqual(results, {'status': 'success'})
self.assertTrue('Test' not in self.helper.saved_states[1].anomalyLabel)
# Verifies records removed from KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
self.assertEqual(knn.removeIds.mock_calls, [call([11]), call([])])
# Verifies records after removed record are updated
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_updateState')
def testSetGetThreshold(self, updateState):
self.helper.saved_states = [Mock(), Mock(), Mock()]
self.helper.setAutoDetectThreshold(1.0)
self.assertAlmostEqual(self.helper._autoDetectThreshold, 1.0)
self.assertEqual(len(updateState.mock_calls), len(self.helper.saved_states))
self.assertAlmostEqual(self.helper.getAutoDetectThreshold(), 1.0)
self.assertRaises(Exception, self.helper.setAutoDetectThreshold, 'invalid')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testSetGetWaitRecords(self, updateState):
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)]
self.helper.setAutoDetectWaitRecords(20)
self.assertEqual(self.helper._autoDetectWaitRecords, 20)
self.assertEqual(len(updateState.mock_calls), len(self.helper.saved_states))
self.assertEqual(self.helper.getAutoDetectWaitRecords(), 20)
# Test invalid parameter type
self.assertRaises(Exception, self.helper.setAutoDetectWaitRecords,
'invalid')
# Test invalid value before first record ROWID in cache
self.assertRaises(Exception, self.helper.setAutoDetectWaitRecords, 0)
@patch.object(CLAModelClassifierHelper, '_addRecordToKNN')
@patch.object(CLAModelClassifierHelper, '_deleteRecordsFromKNN')
@patch.object(CLAModelClassifierHelper, '_recomputeRecordFromKNN')
@patch.object(CLAModelClassifierHelper, '_categoryToLabelList')
def testUpdateState(self, toLabelList, recompute, deleteRecord, addRecord):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
# Test record not labeled and not above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 0
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
deleteRecord.assert_called_once_with([state])
# Test record not labeled and above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 0.5
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL])
addRecord.assert_called_once_with(state)
# Test record not labeled and above threshold during wait period
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 10
self.helper._autoDetectThreshold = 0.5
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
self.assertTrue(not addRecord.called)
# Test record labeled and not above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 0
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = ["Label"]
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, ["Label"])
self.assertTrue(not addRecord.called)
# Test setByUser
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = ["Label 2"]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel,
[recordCopy["anomalyLabel"][0], toLabelList.return_value[0]])
addRecord.assert_called_once_with(state)
# Test removal of above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = []
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL,
CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
# Auto classified threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel,
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG])
addRecord.assert_called_once_with(state)
# Test precedence of threshold label above auto threshold label
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 0.8
toLabelList.return_value = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL,
CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel,
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL])
addRecord.assert_called_once_with(state)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testAddRecordToKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {
'categoryRecencyList': [1, 2, 3]
}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
state = {
"ROWID": 5,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
record = _CLAClassificationRecord(**state)
# Test with record not already in KNN
self.helper._addRecordToKNN(record)
classifier._knn.learn.assert_called_once_with("Vector", ANY, rowID=state['ROWID'])
self.assertTrue(not classifier._knn.prototypeSetCategory.called)
classifier._knn.learn.reset_mock()
# Test with record already in KNN
values = {
'categoryRecencyList': [1, 2, 3, 5]
}
classifier.getParameter.side_effect = values.get
self.helper._addRecordToKNN(record)
classifier._knn.prototypeSetCategory.assert_called_once_with(\
state['ROWID'], ANY)
self.assertTrue(not classifier._knn.learn.called)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testDeleteRangeFromKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {
'categoryRecencyList': [1, 2, 3]
}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
classifier._knn._numPatterns = len(values['categoryRecencyList'])
classifier._knn.removeIds.side_effect = self.mockRemoveIds
# Test with record not already in KNN
self.helper._deleteRangeFromKNN(start=1,end=3)
classifier._knn.removeIds.assert_called_once_with([1,2])
classifier._knn.removeIds.reset_mock()
# Test with record already in KNN
values = {
'categoryRecencyList': [1, 2, 3, 5]
}
classifier.getParameter.side_effect = values.get
self.helper._deleteRangeFromKNN(start=1)
classifier._knn.removeIds.assert_called_once_with([1,2,3,5])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testRecomputeRecordFromKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {
'categoryRecencyList': [1, 2, 3, 5, 6, 7, 8, 9],
'latestDists': numpy.array([0.7, 0.2, 0.5, 1, 0.3, 0.2, 0.1]),
'categories': ['A','B','C','D','E','F','G']
}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getLatestDistances.return_value = values['latestDists']
classifier.getCategoryList.return_value = values['categories']
classifier.getParameter.side_effect = values.get
state = {
"ROWID": 5,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
record = _CLAClassificationRecord(**state)
# Test finding best category before record - exists
self.helper._classificationMaxDist = 0.4
self.helper._autoDetectWaitRecords = 0
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, 'B')
# Test finding best category before record - does not exists
self.helper._classificationMaxDist = 0.1
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, None)
# Test finding best category before record - not record before
record.ROWID = 0
self.helper._classificationMaxDist = 0.1
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, None)
def testConstructClassificationVector(self):
modelParams = {
'__numRunCalls': 0
}
spVals = {
'params': {
'activeOutputCount': 5
},
'output': {
'bottomUpOut': numpy.array([1,1,0,0,1])
}
}
tpVals = {
'params': {
'cellsPerColumn': 2,
'columnCount': 2
},
'output': {
'lrnActive': numpy.array([1,0,0,1]),
'topDownOut': numpy.array([1,0,0,0,1])
}
}
self.helper.clamodel.getParameter.side_effect = modelParams.get
sp = self.helper.clamodel._getSPRegion()
tp = self.helper.clamodel._getTPRegion()
tpImp = tp.getSelf()._tfdr
sp.getParameter.side_effect = spVals['params'].get
sp.getOutputData.side_effect = spVals['output'].get
self.helper._activeColumnCount = 5
tp.getParameter.side_effect = tpVals['params'].get
tp.getOutputData.side_effect = tpVals['output'].get
tpImp.getLearnActiveStateT.return_value = tpVals['output']['lrnActive']
# Test TP Cell vector
self.helper._vectorType = 'tpc'
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector, tpImp.getLearnActiveStateT().nonzero()[0].tolist())
# Test SP and TP Column Error vector
self.helper._vectorType = 'sp_tpe'
self.helper._prevPredictedColumns = numpy.array([1,0,0,0,1]).nonzero()[0]
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector, [0, 1, 4])
self.helper._prevPredictedColumns = numpy.array([1,0,1,0,0]).nonzero()[0]
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector, [0, 1, 4, 7])
self.helper._vectorType = 'invalidType'
self.assertRaises(TypeError, self.helper._constructClassificationRecord)
@patch.object(CLAModelClassifierHelper ,'_updateState')
@patch.object(CLAModelClassifierHelper, '_constructClassificationRecord')
def testCompute(self, createRecord, updateState):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label"
}
record = _CLAClassificationRecord(**state)
createRecord.return_value = record
# Test add first record
self.helper._history_length = 10
self.helper._autoDetectWaitRecords = 0
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
updateState.assert_called_once_with(result)
# Test add record before wait records
updateState.reset_mock()
self.helper._history_length = 10
self.helper._autoDetectWaitRecords = 10
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 2)
self.assertTrue(not updateState.called)
# Test exceeded cache length
updateState.reset_mock()
self.helper._history_length = 1
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
self.assertTrue(not updateState.called)
def testCategoryToList(self):
result = self.helper._categoryToLabelList(None)
self.assertEqual(result, [])
self.helper.saved_categories = ['A', 'B', 'C']
result = self.helper._categoryToLabelList(1)
self.assertEqual(result, ['A'])
result = self.helper._categoryToLabelList(4)
self.assertEqual(result, ['C'])
result = self.helper._categoryToLabelList(5)
self.assertEqual(result, ['A','C'])
def testGetAnomalyVector(self):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": [1,4,5],
"anomalyLabel": "Label"
}
record = _CLAClassificationRecord(**state)
self.helper._anomalyVectorLength = 10
vector = self.helper._getStateAnomalyVector(record)
self.assertEqual(len(vector), self.helper._anomalyVectorLength)
self.assertEqual(vector.nonzero()[0].tolist(), record.anomalyVector)
# Tests for configuration
#############################################################################
@patch.object(Configuration, 'get')
def testConfiguration(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertTrue(helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertTrue(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertTrue(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
@patch.object(Configuration, 'get')
def testConfigurationFail(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
self.assertRaises(TypeError, CLAModelClassifierHelper, Mock(spec=CLAModel))
@patch.object(Configuration, 'get')
def testSetState(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
state = dict(_version=1,_classificationDelay=100)
self.helper._vectorType = None
state = self.helper.__setstate__(state)
self.assertEqual(self.helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
self.assertEqual(self.helper._version, CLAModelClassifierHelper.__VERSION__)
state = dict(_version=2, _classificationDelay=100)
state = self.helper.__setstate__(state)
self.assertEqual(self.helper._version, CLAModelClassifierHelper.__VERSION__)
state = dict(_version="invalid")
self.assertRaises(Exception, self.helper.__setstate__, state)
# Tests for _CLAClassificationRecord class
#############################################################################
def testCLAClassificationRecord(self):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label"
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, False)
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": True
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, record['setByUser'])
def testCLAClassificationRecordGetState(self):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": False
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.__getstate__(), record)
def testCLAClassificationRecordSetState(self):
record = {
"ROWID": None,
"anomalyScore": None,
"anomalyVector": None,
"anomalyLabel": None,
"setByUser": None
}
state = _CLAClassificationRecord(**record)
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": False
}
state.__setstate__(record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, record['setByUser'])
def mockRemoveIds(self, ids):
self.helper.clamodel._getAnomalyClassifier().getSelf()._knn._numPatterns -= len(ids)
for idx in ids:
if idx in self.helper.clamodel._getAnomalyClassifier().getSelf().getParameter('categoryRecencyList'):
self.helper.clamodel._getAnomalyClassifier().getSelf().getParameter('categoryRecencyList').remove(idx)
def setUp(self):
self.helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
class CLAClassifierHelperTest(unittest.TestCase):
"""CLAModelClassifierHelper unit tests."""
def setUp(self):
self.helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
@patch.object(Configuration, 'get')
@patch.object(CLAModelClassifierHelper, 'compute')
def testInit(self, compute, configurationGet):
anomalyParams = {
'autoDetectWaitRecords': 100,
'autoDetectThreshold': 101,
'anomalyCacheRecords': 102,
'anomalyVectorType': 'tpc'
}
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc',
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), anomalyParams)
self.assertEqual(helper._autoDetectWaitRecords,
anomalyParams['autoDetectWaitRecords'])
self.assertEqual(helper._autoDetectThreshold,
anomalyParams['autoDetectThreshold'])
self.assertEqual(helper._history_length,
anomalyParams['anomalyCacheRecords'])
self.assertEqual(helper._vectorType,
anomalyParams['anomalyVectorType'])
helper = CLAModelClassifierHelper(Mock(spec=CLAModel), None)
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertEqual(
helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertEqual(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertEqual(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
@patch.object(CLAModelClassifierHelper, 'compute')
def testRun(self, compute):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": [1, 4, 5],
"anomalyLabel": "Label"
}
compute.return_value = _CLAClassificationRecord(**state)
result = self.helper.run()
compute.assert_called_once_with()
self.assertEqual(result, state['anomalyLabel'])
def testGetLabels(self):
# No saved_states
self.helper.saved_states = []
self.assertEqual(self.helper.getLabels(), \
{'isProcessing': False, 'recordLabels': []})
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels,
start=100,
end=100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels,
start=-100,
end=-100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.getLabels,
start=100,
end=-100)
# Valid no threshold labels
values = {
'categoryRecencyList': [4, 5, 7],
}
self.helper.saved_categories = ['TestCategory']
categoryList = [1, 1, 1]
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
classifier._knn._categoryList = categoryList
results = self.helper.getLabels()
self.assertTrue('isProcessing' in results)
self.assertTrue('recordLabels' in results)
self.assertEqual(len(results['recordLabels']),
len(values['categoryRecencyList']))
for record in results['recordLabels']:
self.assertTrue(record['ROWID'] in values['categoryRecencyList'])
self.assertEqual(record['labels'], self.helper.saved_categories)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testAddLabel(self, _updateState, _getStateAnomalyVector):
self.helper.clamodel._getAnomalyClassifier().getSelf(
).getParameter.return_value = [1, 2, 3]
self.helper.saved_states = []
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel,
start=100,
end=100,
labelName="test")
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel,
start=100,
end=100,
labelName="test")
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel,
start=-100,
end=-100,
labelName="test")
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.addLabel,
start=100,
end=-100,
labelName="test")
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=[], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)
]
results = self.helper.addLabel(11, 12, "Added")
# Verifies records were udpated
self.assertEqual(results, None)
self.assertTrue('Added' in self.helper.saved_states[1].anomalyLabel)
self.assertTrue(self.helper.saved_states[1].setByUser)
# Verifies record added to KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
knn.learn.assert_called_once_with(ANY, ANY, rowID=11)
# Verifies records after added label is recomputed
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testRemoveLabel(self, _updateState, _getStateAnomalyVector):
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.return_value = [10, 11, 12]
classifier._knn._numPatterns = 3
classifier._knn.removeIds.side_effect = self.mockRemoveIds
self.helper.saved_states = []
self.assertRaises(
CLAModelInvalidRangeError,
self.helper.removeLabels,
)
# Invalid ranges
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels,
start=100,
end=100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels,
start=-100,
end=-100)
self.helper.saved_states = [Mock(ROWID=10)]
self.assertRaises(CLAModelInvalidRangeError,
self.helper.removeLabels,
start=100,
end=-100)
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)
]
results = self.helper.removeLabels(11, 12, "Test")
self.assertEqual(results, {'status': 'success'})
self.assertTrue('Test' not in self.helper.saved_states[1].anomalyLabel)
# Verifies records removed from KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
self.assertEqual(knn.removeIds.mock_calls, [call([11]), call([])])
# Verifies records after removed record are updated
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testRemoveLabelNoFilter(self, _updateState, _getStateAnomalyVector):
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
values = {'categoryRecencyList': [10, 11, 12]}
classifier.getParameter.side_effect = values.get
classifier._knn._numPatterns = 3
classifier._knn.removeIds.side_effect = self.mockRemoveIds
# Valid no threshold labels
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)
]
results = self.helper.removeLabels(11, 12)
self.assertEqual(results, {'status': 'success'})
self.assertTrue('Test' not in self.helper.saved_states[1].anomalyLabel)
# Verifies records removed from KNN classifier
knn = self.helper.clamodel._getAnomalyClassifier().getSelf()._knn
self.assertEqual(knn.removeIds.mock_calls, [call([11]), call([])])
# Verifies records after removed record are updated
_updateState.assert_called_once_with(self.helper.saved_states[2])
@patch.object(CLAModelClassifierHelper, '_updateState')
def testSetGetThreshold(self, updateState):
self.helper.saved_states = [Mock(), Mock(), Mock()]
self.helper.setAutoDetectThreshold(1.0)
self.assertAlmostEqual(self.helper._autoDetectThreshold, 1.0)
self.assertEqual(len(updateState.mock_calls),
len(self.helper.saved_states))
self.assertAlmostEqual(self.helper.getAutoDetectThreshold(), 1.0)
self.assertRaises(Exception, self.helper.setAutoDetectThreshold,
'invalid')
@patch.object(CLAModelClassifierHelper, '_updateState')
def testSetGetWaitRecords(self, updateState):
self.helper.saved_states = [
Mock(ROWID=10, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=11, anomalyLabel=["Test"], setByUser=False),
Mock(ROWID=12, anomalyLabel=["Test"], setByUser=True)
]
self.helper.setAutoDetectWaitRecords(20)
self.assertEqual(self.helper._autoDetectWaitRecords, 20)
self.assertEqual(len(updateState.mock_calls),
len(self.helper.saved_states))
self.assertEqual(self.helper.getAutoDetectWaitRecords(), 20)
# Test invalid parameter type
self.assertRaises(Exception, self.helper.setAutoDetectWaitRecords,
'invalid')
# Test invalid value before first record ROWID in cache
self.assertRaises(Exception, self.helper.setAutoDetectWaitRecords, 0)
@patch.object(CLAModelClassifierHelper, '_addRecordToKNN')
@patch.object(CLAModelClassifierHelper, '_deleteRecordsFromKNN')
@patch.object(CLAModelClassifierHelper, '_recomputeRecordFromKNN')
@patch.object(CLAModelClassifierHelper, '_categoryToLabelList')
def testUpdateState(self, toLabelList, recompute, deleteRecord, addRecord):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
# Test record not labeled and not above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 0
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
deleteRecord.assert_called_once_with([state])
# Test record not labeled and above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 0.5
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL])
addRecord.assert_called_once_with(state)
# Test record not labeled and above threshold during wait period
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 10
self.helper._autoDetectThreshold = 0.5
toLabelList.return_value = []
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
self.assertTrue(not addRecord.called)
# Test record labeled and not above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectWaitRecords = 0
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = ["Label"]
state = _CLAClassificationRecord(**record)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, ["Label"])
self.assertTrue(not addRecord.called)
# Test setByUser
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = ["Label 2"]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(
state.anomalyLabel,
[recordCopy["anomalyLabel"][0], toLabelList.return_value[0]])
addRecord.assert_called_once_with(state)
# Test removal of above threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = []
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL,
CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel, [])
# Auto classified threshold
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 1.1
toLabelList.return_value = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(state.anomalyLabel,
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG])
addRecord.assert_called_once_with(state)
# Test precedence of threshold label above auto threshold label
deleteRecord.reset_mock()
addRecord.reset_mock()
self.helper._autoDetectThreshold = 0.8
toLabelList.return_value = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL,
CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL + \
CLAModelClassifierHelper.AUTO_TAG]
recordCopy = copy.deepcopy(record)
recordCopy['setByUser'] = True
recordCopy['anomalyLabel'] = \
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL]
state = _CLAClassificationRecord(**recordCopy)
self.helper._updateState(state)
self.assertEqual(
state.anomalyLabel,
[CLAModelClassifierHelper.AUTO_THRESHOLD_CLASSIFIED_LABEL])
addRecord.assert_called_once_with(state)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testAddRecordToKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {'categoryRecencyList': [1, 2, 3]}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
state = {
"ROWID": 5,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
record = _CLAClassificationRecord(**state)
# Test with record not already in KNN
self.helper._addRecordToKNN(record)
classifier._knn.learn.assert_called_once_with("Vector",
ANY,
rowID=state['ROWID'])
self.assertTrue(not classifier._knn.prototypeSetCategory.called)
classifier._knn.learn.reset_mock()
# Test with record already in KNN
values = {'categoryRecencyList': [1, 2, 3, 5]}
classifier.getParameter.side_effect = values.get
self.helper._addRecordToKNN(record)
classifier._knn.prototypeSetCategory.assert_called_once_with(\
state['ROWID'], ANY)
self.assertTrue(not classifier._knn.learn.called)
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testDeleteRangeFromKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {'categoryRecencyList': [1, 2, 3]}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getParameter.side_effect = values.get
classifier._knn._numPatterns = len(values['categoryRecencyList'])
classifier._knn.removeIds.side_effect = self.mockRemoveIds
# Test with record not already in KNN
self.helper._deleteRangeFromKNN(start=1, end=3)
classifier._knn.removeIds.assert_called_once_with([1, 2])
classifier._knn.removeIds.reset_mock()
# Test with record already in KNN
values = {'categoryRecencyList': [1, 2, 3, 5]}
classifier.getParameter.side_effect = values.get
self.helper._deleteRangeFromKNN(start=1)
classifier._knn.removeIds.assert_called_once_with([1, 2, 3, 5])
@patch.object(CLAModelClassifierHelper, '_getStateAnomalyVector')
def testRecomputeRecordFromKNN(self, getAnomalyVector):
getAnomalyVector.return_value = "Vector"
values = {
'categoryRecencyList': [1, 2, 3, 5, 6, 7, 8, 9],
'latestDists': numpy.array([0.7, 0.2, 0.5, 1, 0.3, 0.2, 0.1]),
'categories': ['A', 'B', 'C', 'D', 'E', 'F', 'G']
}
classifier = self.helper.clamodel._getAnomalyClassifier().getSelf()
classifier.getLatestDistances.return_value = values['latestDists']
classifier.getCategoryList.return_value = values['categories']
classifier.getParameter.side_effect = values.get
state = {
"ROWID": 5,
"anomalyScore": 1.0,
"anomalyVector": "",
"anomalyLabel": ["Label"],
"setByUser": False
}
record = _CLAClassificationRecord(**state)
# Test finding best category before record - exists
self.helper._classificationMaxDist = 0.4
self.helper._autoDetectWaitRecords = 0
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, 'B')
# Test finding best category before record - does not exists
self.helper._classificationMaxDist = 0.1
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, None)
# Test finding best category before record - not record before
record.ROWID = 0
self.helper._classificationMaxDist = 0.1
result = self.helper._recomputeRecordFromKNN(record)
self.assertEqual(result, None)
def testConstructClassificationVector(self):
modelParams = {'__numRunCalls': 0}
spVals = {
'params': {
'activeOutputCount': 5
},
'output': {
'bottomUpOut': numpy.array([1, 1, 0, 0, 1])
}
}
tpVals = {
'params': {
'cellsPerColumn': 2,
'columnCount': 2
},
'output': {
'lrnActive': numpy.array([1, 0, 0, 1]),
'topDownOut': numpy.array([1, 0, 0, 0, 1])
}
}
self.helper.clamodel.getParameter.side_effect = modelParams.get
sp = self.helper.clamodel._getSPRegion()
tp = self.helper.clamodel._getTPRegion()
tpImp = tp.getSelf()._tfdr
sp.getParameter.side_effect = spVals['params'].get
sp.getOutputData.side_effect = spVals['output'].get
self.helper._activeColumnCount = 5
tp.getParameter.side_effect = tpVals['params'].get
tp.getOutputData.side_effect = tpVals['output'].get
tpImp.getLearnActiveStateT.return_value = tpVals['output']['lrnActive']
# Test TP Cell vector
self.helper._vectorType = 'tpc'
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector,
tpImp.getLearnActiveStateT().nonzero()[0].tolist())
# Test SP and TP Column Error vector
self.helper._vectorType = 'sp_tpe'
self.helper._prevPredictedColumns = numpy.array([1, 0, 0, 0,
1]).nonzero()[0]
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector, [0, 1, 4])
self.helper._prevPredictedColumns = numpy.array([1, 0, 1, 0,
0]).nonzero()[0]
vector = self.helper._constructClassificationRecord()
self.assertEqual(vector.anomalyVector, [0, 1, 4, 7])
self.helper._vectorType = 'invalidType'
self.assertRaises(TypeError,
self.helper._constructClassificationRecord)
@patch.object(CLAModelClassifierHelper, '_updateState')
@patch.object(CLAModelClassifierHelper, '_constructClassificationRecord')
def testCompute(self, createRecord, updateState):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label"
}
record = _CLAClassificationRecord(**state)
createRecord.return_value = record
# Test add first record
self.helper._history_length = 10
self.helper._autoDetectWaitRecords = 0
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
updateState.assert_called_once_with(result)
# Test add record before wait records
updateState.reset_mock()
self.helper._history_length = 10
self.helper._autoDetectWaitRecords = 10
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 2)
self.assertTrue(not updateState.called)
# Test exceeded cache length
updateState.reset_mock()
self.helper._history_length = 1
self.helper.saved_states = []
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
result = self.helper.compute()
self.assertEqual(result, record)
self.assertEqual(len(self.helper.saved_states), 1)
self.assertTrue(not updateState.called)
def testCategoryToList(self):
result = self.helper._categoryToLabelList(None)
self.assertEqual(result, [])
self.helper.saved_categories = ['A', 'B', 'C']
result = self.helper._categoryToLabelList(1)
self.assertEqual(result, ['A'])
result = self.helper._categoryToLabelList(4)
self.assertEqual(result, ['C'])
result = self.helper._categoryToLabelList(5)
self.assertEqual(result, ['A', 'C'])
def testGetAnomalyVector(self):
state = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": [1, 4, 5],
"anomalyLabel": "Label"
}
record = _CLAClassificationRecord(**state)
self.helper._anomalyVectorLength = 10
vector = self.helper._getStateAnomalyVector(record)
self.assertEqual(len(vector), self.helper._anomalyVectorLength)
self.assertEqual(vector.nonzero()[0].tolist(), record.anomalyVector)
# Tests for configuration
#############################################################################
@patch.object(Configuration, 'get')
def testConfiguration(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.auto_detect_threshold': 2.0,
'nupic.model.temporalAnomaly.window_length': 1111,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
helper = CLAModelClassifierHelper(Mock(spec=CLAModel))
self.assertEqual(helper._autoDetectWaitRecords,
conf['nupic.model.temporalAnomaly.wait_records'])
self.assertTrue(
helper._autoDetectThreshold,
conf['nupic.model.temporalAnomaly.auto_detect_threshold'])
self.assertTrue(helper._history_length,
conf['nupic.model.temporalAnomaly.window_length'])
self.assertTrue(helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
@patch.object(Configuration, 'get')
def testConfigurationFail(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
self.assertRaises(TypeError, CLAModelClassifierHelper,
Mock(spec=CLAModel))
@patch.object(Configuration, 'get')
def testSetState(self, configurationGet):
conf = {
'nupic.model.temporalAnomaly.wait_records': 160,
'nupic.model.temporalAnomaly.anomaly_vector': 'tpc'
}
configurationGet.side_effect = conf.get
state = dict(_version=1, _classificationDelay=100)
self.helper._vectorType = None
state = self.helper.__setstate__(state)
self.assertEqual(self.helper._vectorType,
conf['nupic.model.temporalAnomaly.anomaly_vector'])
self.assertEqual(self.helper._version,
CLAModelClassifierHelper.__VERSION__)
state = dict(_version=2, _classificationDelay=100)
state = self.helper.__setstate__(state)
self.assertEqual(self.helper._version,
CLAModelClassifierHelper.__VERSION__)
state = dict(_version="invalid")
self.assertRaises(Exception, self.helper.__setstate__, state)
# Tests for _CLAClassificationRecord class
#############################################################################
def testCLAClassificationRecord(self):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label"
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, False)
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": True
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, record['setByUser'])
def testCLAClassificationRecordGetState(self):
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": False
}
state = _CLAClassificationRecord(**record)
self.assertEqual(state.__getstate__(), record)
def testCLAClassificationRecordSetState(self):
record = {
"ROWID": None,
"anomalyScore": None,
"anomalyVector": None,
"anomalyLabel": None,
"setByUser": None
}
state = _CLAClassificationRecord(**record)
record = {
"ROWID": 0,
"anomalyScore": 1.0,
"anomalyVector": "Vector",
"anomalyLabel": "Label",
"setByUser": False
}
state.__setstate__(record)
self.assertEqual(state.ROWID, record['ROWID'])
self.assertEqual(state.anomalyScore, record['anomalyScore'])
self.assertEqual(state.anomalyVector, record['anomalyVector'])
self.assertEqual(state.anomalyLabel, record['anomalyLabel'])
self.assertEqual(state.setByUser, record['setByUser'])
def mockRemoveIds(self, ids):
self.helper.clamodel._getAnomalyClassifier().getSelf(
)._knn._numPatterns -= len(ids)
for idx in ids:
if idx in self.helper.clamodel._getAnomalyClassifier().getSelf(
).getParameter('categoryRecencyList'):
self.helper.clamodel._getAnomalyClassifier().getSelf(
).getParameter('categoryRecencyList').remove(idx)
