def compute_scores(y_test, y_pred, normalize=False):
# Errors
errors = np.array((y_test - y_pred)**2)
if normalize:
errors = errors / float(errors.max() - errors.min())
# Log likelihood.
log_likelihoods = []
anomaly_likelihood = AnomalyLikelihood()
for i in range(len(y_test)):
likelihood = anomaly_likelihood.anomalyProbability(y_test[i],
errors[i],
timestamp=None)
log_likelihood = anomaly_likelihood.computeLogLikelihood(likelihood)
log_likelihoods.append(log_likelihood)
# Anomaly thresholds:
# - HIGH: log_likelihood >= 0.5
# - MEDIUM: 0.5 > log_likelihood >= 0.4
N = len(log_likelihoods)
anomalies = {'high': np.zeros(N), 'medium': np.zeros(N)}
x = np.array(log_likelihoods)
high_idx = x >= 0.5
anomalies['high'][high_idx] = 1
# medium_idx = np.logical_and(x >= 0.4, x < 0.5)
# anomalies['medium'][medium_idx] = 1
return errors, log_likelihoods, anomalies
class buildmodel:
def __init__(self):
#self.model_params = getScalarMetricWithTimeOfDayAnomalyParams(metricData=[0],tmImplementation="cpp")
with open("model_params.json") as fp:
self.model_params = json.load(fp)
print self.model_params
self.newmodel = ModelFactory.create(self.model_params)
self.newmodel.enableLearning()
self.newmodel.enableInference({"predictedField": "value"})
self.DATE_FORMAT = "%d/%m/%Y %H:%M"
self.anomalylikelihood = AnomalyLikelihood()
def processdata(self, data):
timestamp = datetime.datetime.strptime(data[0], self.DATE_FORMAT)
ce = float(data[1])
result = self.newmodel.run({"dttm": timestamp, "value": ce})
#print result
anomalyScore = result.inferences["anomalyScore"]
anomaly = self.anomalylikelihood.anomalyProbability(
ce, anomalyScore, timestamp)
logLikelihood = self.anomalylikelihood.computeLogLikelihood(anomaly)
logLikelihood = logLikelihood * 100
print logLikelihood
'''if anomaly > 0.999:
print "Detected high level anomaly at "+str(timestamp)
elif anomaly>0.958:
print "Detected medium level anomaly at "+str(timestamp)'''
if logLikelihood > 20:
print "Detected high level anomaly at " + str(timestamp)
elif logLikelihood > 15:
print "Detected medium level anomaly at " + str(timestamp)
def __init__(self,
slidingWindowSize=None,
mode=MODE_PURE,
binaryAnomalyThreshold=None):
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if (self._mode == Anomaly.MODE_LIKELIHOOD
or self._mode == Anomaly.MODE_WEIGHTED):
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
else:
self._likelihood = None
if not self._mode in self._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" %
self._mode)
self._binaryThreshold = binaryAnomalyThreshold
if binaryAnomalyThreshold is not None and (
not isinstance(binaryAnomalyThreshold, float)
or binaryAnomalyThreshold >= 1.0
or binaryAnomalyThreshold <= 0.0):
raise ValueError(
"Anomaly: binaryAnomalyThreshold must be from (0,1) "
"or None if disabled.")
def __init__(self, slidingWindowSize=None, mode=MODE_PURE, binaryAnomalyThreshold=None):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
@param binaryAnomalyThreshold (optional) - if set [0,1] anomaly score
will be discretized to 1/0 (1 if >= binaryAnomalyThreshold)
The transformation is applied after moving average is computed and updated.
"""
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if self._mode == Anomaly.MODE_LIKELIHOOD or self._mode == Anomaly.MODE_WEIGHTED:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
self._binaryThreshold = binaryAnomalyThreshold
if binaryAnomalyThreshold is not None and (
not isinstance(binaryAnomalyThreshold, float) or
binaryAnomalyThreshold >= 1.0 or
binaryAnomalyThreshold <= 0.0 ):
raise ValueError("Anomaly: binaryAnomalyThreshold must be from (0,1) "
"or None if disabled.")
def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if self._mode == Anomaly.MODE_LIKELIHOOD or self._mode == Anomaly.MODE_WEIGHTED:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
def runAvogadroAnomaly(metric, options):
"""
Create a new HTM Model, fetch the data from the local DB, process it in NuPIC,
and save the results to a new CSV output file.
:param metric: AvogadroAgent metric class
:param options: CLI Options
"""
model = createModel(metric)
model.enableInference({"predictedField": metric.name})
fetched = metric.fetch(prefix=options.prefix, start=None)
resultFile = open(
os.path.join(options.prefix, metric.name + "-result.csv"), "wb")
csvWriter = csv.writer(resultFile)
csvWriter.writerow([
"timestamp", metric.name, "raw_anomaly_score", "anomaly_likelihood",
"color"
])
headers = ("timestamp", metric.name)
anomalyLikelihood = AnomalyLikelihood()
for (ts, value) in fetched:
try:
value = float(value)
except (ValueError, TypeError):
continue
if not math.isnan(value):
modelInput = dict(zip(headers, (ts, value)))
modelInput[metric.name] = float(value)
modelInput["timestamp"] = datetime.datetime.fromtimestamp(
float(modelInput["timestamp"]))
result = model.run(modelInput)
anomalyScore = result.inferences["anomalyScore"]
likelihood = anomalyLikelihood.anomalyProbability(
modelInput[metric.name], anomalyScore, modelInput["timestamp"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(likelihood)
if logLikelihood > .5:
color = "red"
elif logLikelihood > .4 and logLikelihood <= .5:
color = "yellow"
else:
color = "green"
csvWriter.writerow([
modelInput["timestamp"],
float(value), anomalyScore, logLikelihood, color
])
else:
resultFile.flush()
def __init__(self):
#self.model_params = getScalarMetricWithTimeOfDayAnomalyParams(metricData=[0],tmImplementation="cpp")
with open("model_params.json") as fp:
self.model_params = json.load(fp)
print self.model_params
self.newmodel = ModelFactory.create(self.model_params)
self.newmodel.enableLearning()
self.newmodel.enableInference({"predictedField": "value"})
self.DATE_FORMAT = "%d/%m/%Y %H:%M"
self.anomalylikelihood = AnomalyLikelihood()
def __init__(self,
learningPeriod=288,
estimationSamples=100,
historicWindowSize=8640,
reestimationPeriod=100):
self.anomalyLikelihood = AnomalyLikelihood(
learningPeriod=learningPeriod,
estimationSamples=estimationSamples,
historicWindowSize=historicWindowSize,
reestimationPeriod=reestimationPeriod)
def runAvogadroAnomaly(metric, options):
"""
Create a new HTM Model, fetch the data from the local DB, process it in NuPIC,
and save the results to a new CSV output file.
:param metric: AvogadroAgent metric class
:param options: CLI Options
"""
model = createModel(metric)
model.enableInference({"predictedField": metric.name})
fetched = metric.fetch(prefix=options.prefix, start=None)
resultFile = open(os.path.join(options.prefix, metric.name + "-result.csv"),
"wb")
csvWriter = csv.writer(resultFile)
csvWriter.writerow(["timestamp", metric.name, "raw_anomaly_score",
"anomaly_likelihood", "color"])
headers = ("timestamp", metric.name)
anomalyLikelihood = AnomalyLikelihood()
for (ts, value) in fetched:
try:
value = float(value)
except (ValueError, TypeError):
continue
if not math.isnan(value):
modelInput = dict(zip(headers, (ts, value)))
modelInput[metric.name] = float(value)
modelInput["timestamp"] = datetime.datetime.fromtimestamp(
float(modelInput["timestamp"]))
result = model.run(modelInput)
anomalyScore = result.inferences["anomalyScore"]
likelihood = anomalyLikelihood.anomalyProbability(
modelInput[metric.name], anomalyScore, modelInput["timestamp"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(likelihood)
if logLikelihood > .5:
color = "red"
elif logLikelihood > .4 and logLikelihood <= .5:
color = "yellow"
else:
color = "green"
csvWriter.writerow([modelInput["timestamp"], float(value),
anomalyScore, logLikelihood, color])
else:
resultFile.flush()
def __init__(self, modelId, stats, replaceParams=()):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
:param sequence replaceParams: Parameter replacement PATH REPLACEMENT pairs
"""
self._modelId = modelId
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats, replaceParams=replaceParams)
self._anomalyLikelihood = AnomalyLikelihood()
def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
self._useMovingAverage = slidingWindowSize > 0
self._buf = None
self._i = None
# Using cumulative anomaly, sliding window
if self._useMovingAverage:
self._windowSize = slidingWindowSize
# Sliding window buffer
self._buf = numpy.array([0] * self._windowSize, dtype=numpy.float)
self._i = 0 # index pointer to actual position
elif slidingWindowSize is not None:
raise TypeError(
"Anomaly: if you define slidingWindowSize, it has to be an "
"integer > 0; slidingWindowSize=%r" % slidingWindowSize)
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
def __init__(self, modelId, stats):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
"""
self._modelId = modelId
# NOTE: ModelRecordEncoder is implemented in the pull request
# https://github.com/numenta/nupic/pull/2432 that is not yet in master.
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats)
self._anomalyLikelihood = AnomalyLikelihood()
def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
def __init__(self,
slidingWindowSize=None,
mode=MODE_PURE,
binaryAnomalyThreshold=None):
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if (self._mode == Anomaly.MODE_LIKELIHOOD or
self._mode == Anomaly.MODE_WEIGHTED):
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
else:
self._likelihood = None
if not self._mode in self._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
self._binaryThreshold = binaryAnomalyThreshold
if binaryAnomalyThreshold is not None and (
not isinstance(binaryAnomalyThreshold, float) or
binaryAnomalyThreshold >= 1.0 or
binaryAnomalyThreshold <= 0.0 ):
raise ValueError("Anomaly: binaryAnomalyThreshold must be from (0,1) "
"or None if disabled.")
def __init__(self, config):
# Instantiate NuPIC model
model_params = base_model_params.MODEL_PARAMS
# Set resolution
model_params['modelParams']['sensorParams']['encoders']['value']['resolution'] = config['resolution']
# Override other Nupic parameters:
model_params['modelParams'] = update_dict(model_params['modelParams'], config['nupic_model_params'])
# Create model and enable inference on it
self.model = ModelFactory.create(model_params)
self.model.enableInference({'predictedField': 'value'})
# The shifter is used to bring the predictions to the actual time frame
self.shifter = InferenceShifter()
# The anomaly likelihood object
self.anomalyLikelihood = AnomalyLikelihood()
# Set stream source
self.stream = config['stream']
# Setup class variables
self.db = redis.Redis('localhost')
self.seconds_per_request = config['seconds_per_request']
self.webhook = config['webhook']
self.anomaly_threshold = config['anomaly_threshold']
self.likelihood_threshold = config['likelihood_threshold']
self.domain = config['domain']
self.alert = False # Toogle when we get above threshold
# Setup logging
self.logger = logger or logging.getLogger(__name__)
handler = logging.handlers.RotatingFileHandler(os.environ['LOG_DIR']+"/monitor_%s.log" % self.stream.name,
maxBytes=1024*1024,
backupCount=4,
)
handler.setFormatter(logging.Formatter('[%(levelname)s/%(processName)s][%(asctime)s] %(name)s %(message)s'))
handler.setLevel(logging.INFO)
self.logger.addHandler(handler)
self.logger.setLevel(logging.INFO)
self.logger.info("=== Settings ===")
self.logger.info("Webhook: %s", self.webhook)
self.logger.info("Domain: %s", self.domain)
self.logger.info("Seconds per request: %d", self.seconds_per_request)
# Write metadata to Redis
try:
# Save in redis with key = 'results:monitor_id' and value = 'time, status, actual, prediction, anomaly'
self.db.set('name:%s' % self.stream.id, self.stream.name)
self.db.set('value_label:%s' % self.stream.id, self.stream.value_label)
self.db.set('value_unit:%s' % self.stream.id, self.stream.value_unit)
except Exception:
self.logger.warn("Could not write results to redis.", exc_info=True)
def __init__(self, inputFileObj, inputSpec, aggSpec, modelSpec):
"""
:param inputFileObj: A file-like object that contains input metric data
:param dict inputSpec: Input data specification per input_opt_schema.json
:param dict aggSpec: Optional aggregation specification per
agg_opt_schema.json or None if no aggregation is requested
:param dict modelSpec: Model specification per model_opt_schema.json
"""
self._inputSpec = inputSpec
self._aggSpec = aggSpec
self._modelSpec = modelSpec
if "modelId" in modelSpec:
self._modelId = modelSpec["modelId"]
else:
self._modelId = "Unknown"
inputRecordSchema = (
fieldmeta.FieldMetaInfo(modelSpec["timestampFieldName"],
fieldmeta.FieldMetaType.datetime,
fieldmeta.FieldMetaSpecial.timestamp),
fieldmeta.FieldMetaInfo(modelSpec["valueFieldName"],
fieldmeta.FieldMetaType.float,
fieldmeta.FieldMetaSpecial.none),
)
self._aggregator = aggregator.Aggregator(
aggregationInfo=dict(
fields=([(modelSpec["valueFieldName"], aggSpec["func"])]
if aggSpec is not None else []),
seconds=aggSpec["windowSize"] if aggSpec is not None else 0
),
inputFields=inputRecordSchema)
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=inputRecordSchema)
self._model = self._createModel(modelSpec=modelSpec)
self._anomalyLikelihood = AnomalyLikelihood()
self._csvReader = self._createCsvReader(inputFileObj)
def create_anomaly_likelihood_calc_from_disk(self, metric):
anomaly_likelihood_calculators_path = self.__model_storage_manager.get_save_path(
metric["metric_name"],
path_element="anomaly_likelihood_calculator")
with open(
os.path.join(anomaly_likelihood_calculators_path,
self.__anomaly_likelihood_calculator_filename),
"rb") as anomaly_likelihood_calc_file:
return AnomalyLikelihood.readFromFile(anomaly_likelihood_calc_file)
def get_anomaly_likelihood_calc(self, metric,
models_number_below_configured_limit):
anomaly_likelihood_calc = None
if not self.__loaded_models.anomaly_calc_exists(metric["metric_name"]):
anomaly_likelihood_calculators_path = self.__model_storage_manager.get_save_path(
metric["metric_name"],
path_element="anomaly_likelihood_calculator")
if os.path.isfile(
os.path.join(
anomaly_likelihood_calculators_path,
self._anomaly_likelihood_calculator_filename)):
if models_number_below_configured_limit:
try:
if models_number_below_configured_limit:
self.__loaded_models.add_anomaly_calc_for_metric(
metric["metric_name"],
self.__anomaly_likelihood_calculator_factory.
create_anomaly_likelihood_calc_from_disk(
metric))
self._logger.debug(
"get_anomaly_likelihood_calc",
"LOADED ANOMALY_LIKELIHOOD_CALC FROM FILE",
metric=str(metric["metric_name"]))
except Exception as ex:
if models_number_below_configured_limit:
self.__loaded_models.add_anomaly_calc_for_metric(
metric["metric_name"], AnomalyLikelihood())
self._logger.warn(
"get_anomaly_likelihood_calc",
"Failed to create an anomaly likelihood calc from disk",
metric=str(metric["metric_name"]),
exception_type=str(type(ex).__name__),
exception_message=str(ex.message))
else:
if models_number_below_configured_limit:
self.__loaded_models.add_anomaly_calc_for_metric(
metric["metric_name"], AnomalyLikelihood())
if self.__loaded_models.anomaly_calc_exists(metric["metric_name"]):
anomaly_likelihood_calc = self.__loaded_models.get_anomaly_calc(
metric["metric_name"])
return anomaly_likelihood_calc
def __init__(self,
learningPeriod = 288,
estimationSamples = 100,
historicWindowSize = 8640,
reestimationPeriod = 100):
self.anomalyLikelihood = AnomalyLikelihood(
learningPeriod = learningPeriod,
estimationSamples = estimationSamples,
historicWindowSize = historicWindowSize,
reestimationPeriod = reestimationPeriod)
def testLikelihoodValues(self):
""" test to see if the region keeps track of state correctly and produces
the same likelihoods as the AnomalyLikelihood module """
anomalyLikelihoodRegion = AnomalyLikelihoodRegion()
anomalyLikelihood = AnomalyLikelihood()
inputs = AnomalyLikelihoodRegion.getSpec()['inputs']
outputs = AnomalyLikelihoodRegion.getSpec()['outputs']
with open(_INPUT_DATA_FILE) as f:
reader = csv.reader(f)
reader.next()
for record in reader:
consumption = float(record[1])
anomalyScore = float(record[2])
likelihood1 = anomalyLikelihood.anomalyProbability(
consumption, anomalyScore)
inputs['rawAnomalyScore'] = numpy.array([anomalyScore])
inputs['metricValue'] = numpy.array([consumption])
anomalyLikelihoodRegion.compute(inputs, outputs)
likelihood2 = outputs['anomalyLikelihood'][0]
self.assertEqual(likelihood1, likelihood2)
def testLikelihoodValues(self):
""" test to see if the region keeps track of state correctly and produces
the same likelihoods as the AnomalyLikelihood module """
anomalyLikelihoodRegion = AnomalyLikelihoodRegion()
anomalyLikelihood = AnomalyLikelihood()
inputs = AnomalyLikelihoodRegion.getSpec()['inputs']
outputs = AnomalyLikelihoodRegion.getSpec()['outputs']
with open (_INPUT_DATA_FILE) as f:
reader = csv.reader(f)
reader.next()
for record in reader:
consumption = float(record[1])
anomalyScore = float(record[2])
likelihood1 = anomalyLikelihood.anomalyProbability(
consumption, anomalyScore)
inputs['rawAnomalyScore'] = numpy.array([anomalyScore])
inputs['metricValue'] = numpy.array([consumption])
anomalyLikelihoodRegion.compute(inputs, outputs)
likelihood2 = outputs['anomalyLikelihood'][0]
self.assertEqual(likelihood1, likelihood2)
def definir_AnomDetect(N_DATA):
"""
retorna as classes de anom_score, a classe de anom_likelihood, e os arrays que guardarão a anom_score e a anom_likelihood
"""
anom_score_txt = np.zeros((N_DATA+1,))
anom_logscore_txt = np.zeros((N_DATA+1,))
anomaly_score = Anomaly(slidingWindowSize=25)
anomaly_likelihood = AnomalyLikelihood(learningPeriod=600, historicWindowSize=313)
return anomaly_score, anomaly_likelihood, anom_score_txt, anom_logscore_txt
def __init__(self, slidingWindowSize=None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
self._useMovingAverage = slidingWindowSize > 0
self._buf = None
self._i = None
# Using cumulative anomaly, sliding window
if self._useMovingAverage:
self._windowSize = slidingWindowSize
# Sliding window buffer
self._buf = numpy.array([0] * self._windowSize, dtype=numpy.float)
self._i = 0 # index pointer to actual position
elif slidingWindowSize is not None:
raise TypeError(
"Anomaly: if you define slidingWindowSize, it has to be an "
"integer > 0; slidingWindowSize=%r" % slidingWindowSize)
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" %
self._mode)
def __init__(self, modelId, stats, replaceParams):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
:param sequence replaceParams: Parameter replacement PATH REPLACEMENT pairs
"""
self._modelId = modelId
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats, replaceParams=replaceParams)
self._anomalyLikelihood = AnomalyLikelihood()
def __init__(self, modelId, stats):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
"""
self._modelId = modelId
# NOTE: ModelRecordEncoder is implemented in the pull request
# https://github.com/numenta/nupic/pull/2432 that is not yet in master.
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats)
self._anomalyLikelihood = AnomalyLikelihood()
def _send_predictions(self, metric_id, metric_envelope):
if metric_id not in self._models:
self._models[metric_id] = ModelFactory.create(self.model_params)
self._models[metric_id].enableInference(
{'predictedField': 'value'})
self._shifters[metric_id] = InferenceShifter()
self._anomaly_likelihood[metric_id] = AnomalyLikelihood()
model = self._models[metric_id]
shifter = self._shifters[metric_id]
modelInput = {
# 'dttm': value['metric']['timestamp'],
'dttm': datetime.datetime.now(),
'value': metric_envelope['metric']['value']
}
result = shifter.shift(model.run(modelInput))
inferences = result.inferences
inference = inferences['multiStepBestPredictions'][5]
metric = metric_envelope['metric']
metric_name = metric['name']
if inference is not None:
metric['name'] = metric_name + '.nupic.predicted'
metric['value'] = inference
str_value = simplejson.dumps(metric_envelope)
self._producer.send_messages(self._topic, str_value)
if 'anomalyScore' in inferences:
metric['name'] = metric_name + '.nupic.anomaly_score'
metric['value'] = inferences['anomalyScore']
str_value = simplejson.dumps(metric_envelope)
self._producer.send_messages(self._topic, str_value)
anomalyLikelihood = self._anomaly_likelihood[metric_id]
likelihood = anomalyLikelihood.anomalyProbability(
modelInput['value'], inferences['anomalyScore'],
datetime.datetime.now())
metric['name'] = metric_name + '.nupic.anomaly_likelihood'
metric['value'] = likelihood
str_value = simplejson.dumps(metric_envelope)
self._producer.send_messages(self._topic, str_value)
def __init__(self, slidingWindowSize = None, anomalyMode=MODE_PURE,
shiftPredicted=False):
"""
@param (optional) slidingWindowSize -- enables moving average on final
anomaly score; how many elements are summed up,
sliding window size; int >= 0
@param (optional) anomalyMode -- (string) how to compute anomaly;
possible values are:
-- "pure" -- the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
-- "likelihood" -- uses the anomaly_likelihood code;
models probability of receiving this
value and anomalyScore; used in Grok
-- "weighted" -- "pure" anomaly weighted by "likelihood" (anomaly * likelihood)
@param shiftPredicted (optional) -- boolean [default=False];
normally active vs predicted are compared
if shiftPredicted=True: predicted(T-1) vs active(T)
are compared (eg from TP, CLAModel)
"""
# using cumulative anomaly , sliding window
if slidingWindowSize > 0:
self._windowSize = slidingWindowSize
#sliding window buffer
self._buf = numpy.array([0] * self._windowSize, dtype=numpy.float)
self._i = 0 # index pointer to actual position
elif slidingWindowSize is not None:
raise Exception("Anomaly: if you define slidingWindowSize, \
it has to be an integer > 0; \
slidingWindowSize="+str(slidingWindowSize))
# mode
self._mode = anomalyMode
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError('Invalid anomaly mode; only supported modes are: \
"Anomaly.MODE_PURE", "Anomaly.MODE_LIKELIHOOD", \
"Anomaly.MODE_WEIGHTED"; you used:' +self._mode)
if shiftPredicted:
self._prevPredictedColumns = numpy.array([])
def main():
# cluster similar inputs together in SDR space
s = SpatialPooler()
print(type(s))
# powerful sequence memory in SDR space
t = TemporalMemory()
print(type(t))
# computes rolling Gaussian based on raw anomaly scores and then their
# likelihood
a = AnomalyLikelihood()
print(type(a))
# temporally groups active cell sets from TM
u = UnionTemporalPooler()
print(type(u))
# learning pairings of Union representations and labeled classes
c = SDRClassifier()
print(type(c))
class _ModelRunner(object):
""" Use OPF Model to process metric data samples from stdin and and emit
anomaly likelihood results to stdout
"""
# Input column meta info compatible with parameters generated by
# getScalarMetricWithTimeOfDayAnomalyParams
_INPUT_RECORD_SCHEMA = (
fieldmeta.FieldMetaInfo("c0", fieldmeta.FieldMetaType.datetime,
fieldmeta.FieldMetaSpecial.timestamp),
fieldmeta.FieldMetaInfo("c1", fieldmeta.FieldMetaType.float,
fieldmeta.FieldMetaSpecial.none),
)
def __init__(self, modelId, stats):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
"""
self._modelId = modelId
# NOTE: ModelRecordEncoder is implemented in the pull request
# https://github.com/numenta/nupic/pull/2432 that is not yet in master.
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats)
self._anomalyLikelihood = AnomalyLikelihood()
@classmethod
def _createModel(cls, stats):
"""Instantiate and configure an OPF model
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
:returns: OPF Model instance
"""
# Generate swarm params
swarmParams = getScalarMetricWithTimeOfDayAnomalyParams(
metricData=[0],
minVal=stats["min"],
maxVal=stats["max"],
minResolution=stats.get("minResolution"))
model = ModelFactory.create(modelConfig=swarmParams["modelConfig"])
model.enableLearning()
model.enableInference(swarmParams["inferenceArgs"])
return model
@classmethod
def _readInputMessages(cls):
"""Create a generator that waits for and yields input messages from
stdin
yields two-tuple (<timestamp>, <scalar-value>), where <timestamp> is the
`datetime.datetime` timestamp of the metric data sample and <scalar-value>
is the floating point value of the metric data sample.
"""
while True:
message = sys.stdin.readline()
if message:
timestamp, scalarValue = json.loads(message)
yield (datetime.utcfromtimestamp(timestamp), scalarValue)
else:
# Front End closed the pipe (or died)
break
@classmethod
def _emitOutputMessage(cls, rowIndex, anomalyProbability):
"""Emit output message to stdout
:param int rowIndex: 0-based index of corresponding input sample
:param float anomalyProbability: computed anomaly probability value
"""
message = "%s\n" % (json.dumps([rowIndex, anomalyProbability]),)
sys.stdout.write(message)
sys.stdout.flush()
def _computeAnomalyProbability(self, inputRow):
""" Compute anomaly log likelihood score
:param tuple inputRow: Two-tuple input metric data row
(<datetime-timestamp>, <float-scalar>)
:returns: Log-scaled anomaly probability
:rtype: float
"""
# Generate raw anomaly score
inputRecord = self._modelRecordEncoder.encode(inputRow)
rawAnomalyScore = self._model.run(inputRecord).inferences["anomalyScore"]
# Generate anomaly likelihood score
anomalyProbability = self._anomalyLikelihood.anomalyProbability(
value=inputRow[1],
anomalyScore=rawAnomalyScore,
timestamp=inputRow[0])
return self._anomalyLikelihood.computeLogLikelihood(anomalyProbability)
def run(self):
""" Run the model: ingest and process the input metric data and emit output
messages containing anomaly scores
"""
g_log.info("Processing model=%s", self._modelId)
for rowIndex, inputRow in enumerate(self._readInputMessages()):
anomalyProbability = self._computeAnomalyProbability(inputRow)
self._emitOutputMessage(rowIndex=rowIndex,
anomalyProbability=anomalyProbability)
class AnomalyLikelihoodRegion(PyRegion):
"""Region for computing the anomaly likelihoods."""
@classmethod
def getSpec(cls):
return {
"description": ("Region that computes anomaly likelihoods for \
temporal memory."),
"singleNodeOnly": True,
"inputs": {
"rawAnomalyScore": {
"description": "The anomaly score whose \
likelihood is to be computed",
"dataType": "Real32",
"count": 1,
"required": True,
"isDefaultInput": False
},
"metricValue": {
"description": "The input metric value",
"dataType": "Real32",
"count": 1,
"required": True,
"isDefaultInput": False
},
},
"outputs": {
"anomalyLikelihood": {
"description": "The resultant anomaly likelihood",
"dataType": "Real32",
"count": 1,
"isDefaultOutput": True,
},
},
"parameters": {
"learningPeriod": {
"description": "The number of iterations required for the\
algorithm to learn the basic patterns in the dataset\
and for the anomaly score to 'settle down'.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 288,
"accessMode": "ReadWrite"
},
"estimationSamples": {
"description": "The number of reasonable anomaly scores\
required for the initial estimate of the\
Gaussian.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 100,
"accessMode": "ReadWrite"
},
"historicWindowSize": {
"description": "Size of sliding window of historical data\
points to maintain for periodic reestimation\
of the Gaussian.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 8640,
"accessMode": "ReadWrite"
},
"reestimationPeriod": {
"description": "How often we re-estimate the Gaussian\
distribution.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 100,
"accessMode": "ReadWrite"
},
},
"commands": {
},
}
def __init__(self,
learningPeriod = 288,
estimationSamples = 100,
historicWindowSize = 8640,
reestimationPeriod = 100):
self.anomalyLikelihood = AnomalyLikelihood(
learningPeriod = learningPeriod,
estimationSamples = estimationSamples,
historicWindowSize = historicWindowSize,
reestimationPeriod = reestimationPeriod)
def __eq__(self, other):
return self.anomalyLikelihood == other.anomalyLikelihood
def __ne__(self, other):
return not self == other
@classmethod
def read(cls, proto):
anomalyLikelihoodRegion = object.__new__(cls)
anomalyLikelihoodRegion.anomalyLikelihood = AnomalyLikelihood.read(proto)
return anomalyLikelihoodRegion
def write(self, proto):
self.anomalyLikelihood.write(proto)
def initialize(self):
pass
def compute(self, inputs, outputs):
anomalyScore = inputs["rawAnomalyScore"][0]
value = inputs["metricValue"][0]
anomalyProbability = self.anomalyLikelihood.anomalyProbability(
value, anomalyScore)
outputs["anomalyLikelihood"][0] = anomalyProbability
class Monitor(object):
""" A NuPIC model that saves results to Redis. """
def __init__(self, config):
# Instantiate NuPIC model
model_params = base_model_params.MODEL_PARAMS
# Set resolution
model_params['modelParams']['sensorParams']['encoders']['value']['resolution'] = config['resolution']
# Override other Nupic parameters:
model_params['modelParams'] = update_dict(model_params['modelParams'], config['nupic_model_params'])
# Create model and enable inference on it
self.model = ModelFactory.create(model_params)
self.model.enableInference({'predictedField': 'value'})
# The shifter is used to bring the predictions to the actual time frame
self.shifter = InferenceShifter()
# The anomaly likelihood object
self.anomalyLikelihood = AnomalyLikelihood()
# Set stream source
self.stream = config['stream']
# Setup class variables
self.db = redis.Redis('localhost')
self.seconds_per_request = config['seconds_per_request']
self.webhook = config['webhook']
self.anomaly_threshold = config['anomaly_threshold']
self.likelihood_threshold = config['likelihood_threshold']
self.domain = config['domain']
self.alert = False # Toogle when we get above threshold
# Setup logging
self.logger = logger or logging.getLogger(__name__)
handler = logging.handlers.RotatingFileHandler(os.environ['LOG_DIR']+"/monitor_%s.log" % self.stream.name,
maxBytes=1024*1024,
backupCount=4,
)
handler.setFormatter(logging.Formatter('[%(levelname)s/%(processName)s][%(asctime)s] %(name)s %(message)s'))
handler.setLevel(logging.INFO)
self.logger.addHandler(handler)
self.logger.setLevel(logging.INFO)
self.logger.info("=== Settings ===")
self.logger.info("Webhook: %s", self.webhook)
self.logger.info("Domain: %s", self.domain)
self.logger.info("Seconds per request: %d", self.seconds_per_request)
# Write metadata to Redis
try:
# Save in redis with key = 'results:monitor_id' and value = 'time, status, actual, prediction, anomaly'
self.db.set('name:%s' % self.stream.id, self.stream.name)
self.db.set('value_label:%s' % self.stream.id, self.stream.value_label)
self.db.set('value_unit:%s' % self.stream.id, self.stream.value_unit)
except Exception:
self.logger.warn("Could not write results to redis.", exc_info=True)
def train(self):
data = self.stream.historic_data()
for model_input in data:
self.update(model_input, False) # Don't post anomalies in training
def loop(self):
while True:
data = self.stream.new_data()
for model_input in data:
self.update(model_input, True) # Post anomalies when online
sleep(self.seconds_per_request)
def update(self, model_input, is_to_post):
# Pass the input to the model
result = self.model.run(model_input)
# Shift results
result = self.shifter.shift(result)
# Save multi step predictions
inference = result.inferences['multiStepPredictions']
# Take the anomaly_score
anomaly_score = result.inferences['anomalyScore']
# Compute the Anomaly Likelihood
likelihood = self.anomalyLikelihood.anomalyProbability(model_input['value'],
anomaly_score,
model_input['time'])
# Get the predicted value for reporting
predicted = result.inferences['multiStepBestPredictions'][1]
# Get timestamp from datetime
timestamp = calendar.timegm(model_input['time'].timetuple())
self.logger.info("Processing: %s", strftime("%Y-%m-%d %H:%M:%S", model_input['time'].timetuple()))
# Save results to Redis
if inference[1]:
try:
# Save in redis with key = 'results:monitor_id' and value = 'time, raw_value, actual, prediction, anomaly'
# * actual: is the value processed by the NuPIC model, which can be
# an average of raw_values
# * predicition: prediction based on 'actual' values.
self.db.rpush('results:%s' % self.stream.id,
'%s,%.5f,%.5f,%.5f,%.5f,%.5f' % (timestamp,
model_input['raw_value'],
result.rawInput['value'],
predicted,
anomaly_score,
likelihood))
max_items = 10000
ln = self.db.llen('results:%s' % self.stream.id)
if ln > max_items:
self.db.ltrim('results:%s' % self.stream.id, ln - max_items, ln)
except Exception:
self.logger.warn("Could not write results to redis.", exc_info=True)
# See if above threshold (in which case anomalous is True)
anomalous = False
if self.anomaly_threshold is not None:
if anomaly_score >= self.anomaly_threshold:
anomalous = True
if self.likelihood_threshold is not None:
if likelihood >= self.likelihood_threshold:
anomalous = True
# Post if webhook is not None
if is_to_post and self.webhook is not None:
# Check if it was in alert state in previous time step
was_alerted = self.alert
# Update alert state
self.alert = anomalous
# Send notification if webhook is set and if:
# was not alerted before and is alerted now (entered anomalous state)
# or
# was alerted before and is not alerted now (left anomalous state)
if not was_alerted and self.alert:
report = {'anomaly_score': anomaly_score,
'likelihood': likelihood,
'model_input': {'time': model_input['time'].isoformat(),
'value': model_input['raw_value']}}
self._send_post(report)
# Return anomalous state
return {"likelihood" : likelihood, "anomalous" : anomalous, "anomalyScore" : anomaly_score, "predicted" : predicted}
def delete(self):
""" Remove this monitor from redis """
self.db.delete("results:%s" % self.stream.id)
self.db.delete('name:%s' % self.stream.id)
self.db.delete('value_label:%s' % self.stream.id)
self.db.delete('value_unit:%s' % self.stream.id)
def _send_post(self, report):
""" Send HTTP POST notification. """
if "hooks.slack.com" not in self.webhook:
payload = {'sent_at': datetime.utcnow().isoformat(),
'report': report,
'monitor': self.stream.name,
'source': type(self.stream).__name__,
'metric': '%s (%s)' % (self.stream.value_label, self.stream.value_unit),
'chart': 'http://%s?id=%s' % (self.domain, self.stream.id)}
else:
payload = {'username': '******',
'icon_url': 'https://rawgithub.com/cloudwalkio/omg-monitor/slack-integration/docs/images/post_icon.png',
'text': 'Anomalous state in *%s* from _%s_:' % (self.stream.name, type(self.stream).__name__),
'attachments': [{'color': 'warning',
'fields': [{'title': 'Chart',
'value': 'http://%s?id=%s' % (self.domain, self.stream.id),
'short': False},
{'title': 'Metric',
'value': self.stream.value_label,
'short': True},
{'title': 'Value',
'value': str(report['model_input']['value']) + ' ' + self.stream.value_unit,
'short': True}]}]}
headers = {'Content-Type': 'application/json'}
try:
response = requests.post(self.webhook, data=json.dumps(payload), headers=headers)
except Exception:
self.logger.warn('Failed to post anomaly.', exc_info=True)
return
self.logger.info('Anomaly posted with status code %d: %s', response.status_code, response.text)
return
parser = argparse.ArgumentParser(description='Add to existing name')
parser.add_argument(
'--algo',
help='add to existing name especially if I am testing some new feature.')
args = parser.parse_args()
algo = args.algo
def get_all_files_path(root):
files = [
val for sublist in [[os.path.join(i[0], j) for j in i[2]]
for i in os.walk(root)] for val in sublist
]
return files
files = get_all_files_path('results/' + algo)
for f in files:
if (not ('_score' in f)):
print(f)
df = pd.read_csv(f)
a = []
al = AnomalyLikelihood()
for i in range(len(df)):
a.append(
al.anomalyProbability(df.value.values[i],
df.anomaly_score.values[i],
df.timestamp.values[i]))
df['anomaly_score'] = a
df.to_csv(f, index=False)
class Anomaly(object):
"""basic class that computes anomaly
Anomaly is used to detect strange patterns/behaviors (outliners)
by a trained CLA model.
"""
# anomaly modes supported
MODE_PURE = "pure"
MODE_LIKELIHOOD = "likelihood"
MODE_WEIGHTED = "weighted"
_supportedModes = [MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED]
def __init__(self, slidingWindowSize = None, anomalyMode=MODE_PURE,
shiftPredicted=False):
"""
@param (optional) slidingWindowSize -- enables moving average on final
anomaly score; how many elements are summed up,
sliding window size; int >= 0
@param (optional) anomalyMode -- (string) how to compute anomaly;
possible values are:
-- "pure" -- the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
-- "likelihood" -- uses the anomaly_likelihood code;
models probability of receiving this
value and anomalyScore; used in Grok
-- "weighted" -- "pure" anomaly weighted by "likelihood" (anomaly * likelihood)
@param shiftPredicted (optional) -- boolean [default=False];
normally active vs predicted are compared
if shiftPredicted=True: predicted(T-1) vs active(T)
are compared (eg from TP, CLAModel)
"""
# using cumulative anomaly , sliding window
if slidingWindowSize > 0:
self._windowSize = slidingWindowSize
#sliding window buffer
self._buf = numpy.array([0] * self._windowSize, dtype=numpy.float)
self._i = 0 # index pointer to actual position
elif slidingWindowSize is not None:
raise Exception("Anomaly: if you define slidingWindowSize, \
it has to be an integer > 0; \
slidingWindowSize="+str(slidingWindowSize))
# mode
self._mode = anomalyMode
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError('Invalid anomaly mode; only supported modes are: \
"Anomaly.MODE_PURE", "Anomaly.MODE_LIKELIHOOD", \
"Anomaly.MODE_WEIGHTED"; you used:' +self._mode)
if shiftPredicted:
self._prevPredictedColumns = numpy.array([])
def computeAnomalyScore(self, activeColumns, predictedColumns, value=None,
timestamp=None):
"""Compute the anomaly score as the percent of active columns not predicted
@param activeColumns: array of active column indices
@param predictedColumns: array of columns indices predicted in this step
(used for anomaly in step T+1)
@param value: (optional) input value, that is what activeColumns represent
(used in anomaly-likelihood)
@param timestamp: (optional) date timestamp when the sample occured
(used in anomaly-likelihood)
@return the computed anomaly score; float 0..1
"""
if hasattr(self, "_prevPredictedColumns"): # shiftPredicted==True
prevPredictedColumns = self._prevPredictedColumns
self._prevPredictedColumns = predictedColumns # to be used in step T+1
else:
prevPredictedColumns = predictedColumns
# 1. here is the 'classic' anomaly score
anomalyScore = computeRawAnomalyScore(activeColumns, prevPredictedColumns)
# compute final anomaly based on selected mode
if self._mode == Anomaly.MODE_PURE:
score = anomalyScore
elif self._mode == Anomaly.MODE_LIKELIHOOD:
probability = self._likelihood.anomalyProbability(value, anomalyScore, timestamp)
score = probability
elif self._mode == Anomaly.MODE_WEIGHTED:
probability = self._likelihood.anomalyProbability(value, anomalyScore, timestamp)
score = anomalyScore * probability
# last, do moving-average if windowSize is set
if hasattr(self, "_windowSize"):
score = self._movingAverage(score)
return score
def _movingAverage(self, newElement=None):
"""moving average
@param newValue (optional) add a new element before computing the avg
@return moving average of self._windowSize last elements
"""
if newElement is not None:
self._buf[self._i]= newElement
self._i = (self._i + 1) % self._windowSize
return self._buf.sum()/float(self._windowSize) # normalize to 0..1
def runAnomaly(options):
global g_ps_count_dict_unsorted
global g_abnomal_data_dict_unsorted
"""
Create and run a CLA Model on the given dataset (based on the hotgym anomaly
client in NuPIC).
"""
# Load the model params JSON
with open("model_params.json") as fp:
modelParams = json.load(fp)
if options.oswpsDir != "":
# Get PS dictionary
osw = OSWData(options.oswpsDir, PS)
osw.traverse_dir()
g_ps_count_dict_unsorted = osw.get_ps_dict()
options.max = ps_max_value = max(g_ps_count_dict_unsorted.values())
options.min = ps_min_value = min(g_ps_count_dict_unsorted.values())
print("Min value:" + str(ps_min_value) + ', ' + "Max value:" +
str(ps_max_value))
# Update the resolution value for the encoder
sensorParams = modelParams['modelParams']['sensorParams']
numBuckets = modelParams['modelParams']['sensorParams']['encoders'][
'value'].pop('numBuckets')
resolution = options.resolution
if resolution is None:
resolution = max(0.001, (options.max - options.min) / numBuckets)
print("Using resolution value: {0}".format(resolution))
sensorParams['encoders']['value']['resolution'] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({'predictedField': 'value'})
if options.inputFile != "":
with open(options.inputFile) as fin:
# Open file and setup headers
# Here we write the log likelihood value as the 'anomaly score'
# The actual CLA outputs are labeled 'raw anomaly score'
reader = csv.reader(fin)
csvWriter = csv.writer(open(options.outputFile, "wb"))
csvWriter.writerow([
"timestamp", "value", "_raw_score", "likelihood_score",
"log_likelihood_score"
])
headers = reader.next()
# The anomaly likelihood object
anomalyLikelihood = AnomalyLikelihood()
# Iterate through each record in the CSV file
print "Starting processing at", datetime.datetime.now()
for i, record in enumerate(reader, start=1):
# Convert input data to a dict so we can pass it into the model
inputData = dict(zip(headers, record))
inputData["value"] = float(inputData["value"])
inputData["dttm"] = dateutil.parser.parse(inputData["dttm"])
#inputData["dttm"] = datetime.datetime.now()
# Send it to the CLA and get back the raw anomaly score
result = model.run(inputData)
anomalyScore = result.inferences['anomalyScore']
# Compute the Anomaly Likelihood
likelihood = anomalyLikelihood.anomalyProbability(
inputData["value"], anomalyScore, inputData["dttm"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(
likelihood)
if likelihood > 0.9999:
print "Anomaly detected:", inputData['dttm'], inputData[
'value'], likelihood
# Write results to the output CSV file
csvWriter.writerow([
inputData["dttm"], inputData["value"], anomalyScore,
likelihood, logLikelihood
])
# Progress report
if (i % 1000) == 0:
print i, "records processed"
elif options.oswpsDir != "":
if options.use_rtm == True:
rtm_sensitivity = 2
rtm = LinearRegressionTemoporalMemory(window=10,
interval=10,
min_=options.min,
max_=options.max,
boost=rtm_sensitivity,
leak_detection=0,
critical_region="right_tail",
debug=0)
g_abnomal_data_dict_unsorted = rtm.analyze(
g_ps_count_dict_unsorted)
else:
csvWriter = csv.writer(open(options.outputFile, "wb"))
csvWriter.writerow([
"timestamp", "value", "_raw_score", "likelihood_score",
"log_likelihood_score"
])
ps_od = collections.OrderedDict(
sorted(g_ps_count_dict_unsorted.items()))
# The anomaly likelihood object
anomalyLikelihood = AnomalyLikelihood()
# Iterate through each record in the CSV file
print "Starting processing at", datetime.datetime.now()
for i, timestamp in enumerate(ps_od):
ps_count = ps_od[timestamp]
inputData = {}
inputData["value"] = float(ps_count)
inputData["dttm"] = dateutil.parser.parse(timestamp)
#inputData["dttm"] = datetime.datetime.now()
# Send it to the CLA and get back the raw anomaly score
result = model.run(inputData)
anomalyScore = result.inferences['anomalyScore']
# Compute the Anomaly Likelihood
likelihood = anomalyLikelihood.anomalyProbability(
inputData["value"], anomalyScore, inputData["dttm"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(
likelihood)
if likelihood > 0.9999:
print "Anomaly detected:", inputData['dttm'], inputData[
'value'], likelihood
g_abnomal_data_dict_unsorted[timestamp] = ps_count
# Write results to the output CSV file
csvWriter.writerow([
inputData["dttm"], inputData["value"], anomalyScore,
likelihood, logLikelihood
])
# Progress report
if (i % 1000) == 0:
print i, "records processed"
print "Completed processing", i, "records at", datetime.datetime.now(
)
print "Anomaly scores for", options.inputFile,
print "have been written to", options.outputFile
class _ModelRunner(object):
""" Use OPF Model to process metric data samples from stdin and and emit
anomaly likelihood results to stdout
"""
def __init__(self, inputFileObj, inputSpec, aggSpec, modelSpec):
"""
:param inputFileObj: A file-like object that contains input metric data
:param dict inputSpec: Input data specification per input_opt_schema.json
:param dict aggSpec: Optional aggregation specification per
agg_opt_schema.json or None if no aggregation is requested
:param dict modelSpec: Model specification per model_opt_schema.json
"""
self._inputSpec = inputSpec
self._aggSpec = aggSpec
self._modelSpec = modelSpec
if "modelId" in modelSpec:
self._modelId = modelSpec["modelId"]
else:
self._modelId = "Unknown"
inputRecordSchema = (
fieldmeta.FieldMetaInfo(modelSpec["timestampFieldName"],
fieldmeta.FieldMetaType.datetime,
fieldmeta.FieldMetaSpecial.timestamp),
fieldmeta.FieldMetaInfo(modelSpec["valueFieldName"],
fieldmeta.FieldMetaType.float,
fieldmeta.FieldMetaSpecial.none),
)
self._aggregator = aggregator.Aggregator(
aggregationInfo=dict(
fields=([(modelSpec["valueFieldName"], aggSpec["func"])]
if aggSpec is not None else []),
seconds=aggSpec["windowSize"] if aggSpec is not None else 0
),
inputFields=inputRecordSchema)
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=inputRecordSchema)
self._model = self._createModel(modelSpec=modelSpec)
self._anomalyLikelihood = AnomalyLikelihood()
self._csvReader = self._createCsvReader(inputFileObj)
@staticmethod
def _createModel(modelSpec):
"""Instantiate and configure an OPF model
:param dict modelSpec: Model specification per model_opt_schema.json
:returns: OPF Model instance
"""
model = ModelFactory.create(modelConfig=modelSpec["modelConfig"])
model.enableLearning()
model.enableInference(modelSpec["inferenceArgs"])
return model
@staticmethod
def _createCsvReader(fileObj):
# We'll be operating on csvs with arbitrarily long fields
csv.field_size_limit(2**27)
# Make sure readline() works on windows too
os.linesep = "\n"
return csv.reader(fileObj, dialect="excel")
@classmethod
def _emitOutputMessage(cls, dataRow, anomalyProbability):
"""Emit output message to stdout
:param list dataRow: the two-tuple data row on which anomalyProbability was
computed, whose first element is datetime timestamp and second element is
the float scalar value
:param float anomalyProbability: computed anomaly probability value
"""
message = "%s\n" % (json.dumps([dataRow[0].isoformat(), dataRow[1], anomalyProbability]),)
sys.stdout.write(message)
sys.stdout.flush()
def _computeAnomalyProbability(self, fields):
""" Compute anomaly log likelihood score
:param tuple fields: Two-tuple input metric data row
(<datetime-timestamp>, <float-scalar>)
:returns: Log-scaled anomaly probability
:rtype: float
"""
# Generate raw anomaly score
inputRecord = self._modelRecordEncoder.encode(fields)
rawAnomalyScore = self._model.run(inputRecord).inferences["anomalyScore"]
# Generate anomaly likelihood score
anomalyProbability = self._anomalyLikelihood.anomalyProbability(
value=fields[1],
anomalyScore=rawAnomalyScore,
timestamp=fields[0])
return self._anomalyLikelihood.computeLogLikelihood(anomalyProbability)
def run(self):
""" Run the model: ingest and process the input metric data and emit output
messages containing anomaly scores
"""
numRowsToSkip = self._inputSpec["rowOffset"]
datetimeFormat = self._inputSpec["datetimeFormat"]
inputRowTimestampIndex = self._inputSpec["timestampIndex"]
inputRowValueIndex = self._inputSpec["valueIndex"]
g_log.info("Processing model=%s", self._modelId)
for inputRow in self._csvReader:
g_log.debug("Got inputRow=%r", inputRow)
if numRowsToSkip > 0:
numRowsToSkip -= 1
g_log.debug("Skipping header row %s; %s rows left to skip",
inputRow, numRowsToSkip)
continue
# Extract timestamp and value
# NOTE: the order must match the `inputFields` that we passed to the
# Aggregator constructor
fields = [
date_time_utils.parseDatetime(inputRow[inputRowTimestampIndex],
datetimeFormat),
float(inputRow[inputRowValueIndex])
]
# Aggregate
aggRow, _ = self._aggregator.next(fields, None)
g_log.debug("Aggregator returned %s for %s", aggRow, fields)
if aggRow is not None:
self._emitOutputMessage(
dataRow=aggRow,
anomalyProbability=self._computeAnomalyProbability(aggRow))
# Reap remaining data from aggregator
aggRow, _ = self._aggregator.next(None, curInputBookmark=None)
g_log.debug("Aggregator reaped %s in final call", aggRow)
if aggRow is not None:
self._emitOutputMessage(
dataRow=aggRow,
anomalyProbability=self._computeAnomalyProbability(aggRow))
# FIFO
events = reversed(events)
if PREDICT: import PREDICTmodel_params as model_params
else: import model_params as model_params
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"})
print "Model created!\n"
# Get the Model-Classes:
anomalyLikelihood = AnomalyLikelihood()
if PREDICT:
from nupic.data.inference_shifter import InferenceShifter
shifter = InferenceShifter()
if (WINDOWSIZE != None): AnomalyScores = deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
else: AnomalyScores = deque() # numpy.ones(len(events)), maxlen=len(events) ?
LikelihoodScores = deque()
r = redis.Redis(host=os.environ.get("REDIS_HOST", "127.0.0.1"),
port=int(os.environ.get("REDIS_PORT", 6379)),
db=int(os.environ.get("REDIS_DB", 0)))
# Feed data into the model:
print "Start Data-Feed...\n"
def runAnomaly(options):
"""
Create and run a CLA Model on the given dataset (based on the hotgym anomaly
client in NuPIC).
"""
# Load the model params JSON
with open("model_params.json") as fp:
modelParams = json.load(fp)
# Update the resolution value for the encoder
sensorParams = modelParams['modelParams']['sensorParams']
numBuckets = modelParams['modelParams']['sensorParams']['encoders']['value'].pop('numBuckets')
resolution = options.resolution
if resolution is None:
resolution = max(0.001,
(options.max - options.min) / numBuckets)
print "Using resolution value: {0}".format(resolution)
sensorParams['encoders']['value']['resolution'] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({'predictedField': 'value'})
with open (options.inputFile) as fin:
# Open file and setup headers
# Here we write the log likelihood value as the 'anomaly score'
# The actual CLA outputs are labeled 'raw anomaly score'
reader = csv.reader(fin)
csvWriter = csv.writer(open(options.outputFile,"wb"))
csvWriter.writerow(["timestamp", "value",
"_raw_score", "likelihood_score", "log_likelihood_score"])
headers = reader.next()
# The anomaly likelihood object
anomalyLikelihood = AnomalyLikelihood()
# Iterate through each record in the CSV file
print "Starting processing at",datetime.datetime.now()
for i, record in enumerate(reader, start=1):
# Convert input data to a dict so we can pass it into the model
inputData = dict(zip(headers, record))
inputData["value"] = float(inputData["value"])
inputData["dttm"] = dateutil.parser.parse(inputData["dttm"])
#inputData["dttm"] = datetime.datetime.now()
# Send it to the CLA and get back the raw anomaly score
result = model.run(inputData)
anomalyScore = result.inferences['anomalyScore']
# Compute the Anomaly Likelihood
likelihood = anomalyLikelihood.anomalyProbability(
inputData["value"], anomalyScore, inputData["dttm"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(likelihood)
if likelihood > 0.9999:
print "Anomaly detected:",inputData['dttm'],inputData['value'],likelihood
# Write results to the output CSV file
csvWriter.writerow([inputData["dttm"], inputData["value"],
anomalyScore, likelihood, logLikelihood])
# Progress report
if (i%1000) == 0: print i,"records processed"
print "Completed processing",i,"records at",datetime.datetime.now()
print "Anomaly scores for",options.inputFile,
print "have been written to",options.outputFile
def foreach_batch_function(df, epoch_id):
# Transform and write batchDF
row = df.collect()
print "Size of Batch"
print(len(row))
if len(row) != 0:
for x in range(len(row)):
nb = nb + 1
record = {}
level = row[x]['level']
#print(type(level))
timestamp = row[x]['@timestamp']
#print(type(timestamp))
#print(timestamp)
#timestamp = timestamp.encode("utf-8")
level = level.encode("utf-8")
if level == 'INFO' or level == 'info':
level = 'info'
elif level == 'ERROR' or level == 'error':
level = 'error'
else:
level = 'warning'
#print 'step 2'
record = {"timestamp": timestamp, "level": level}
print(record)
result = model.run(record)
anom = result.inferences['anomalyScore']
#print(anom)
record_anomalies.append(anom)
#print "Lengths of record anomalies"
#print(len(record_anomalies))
mean_anomalies = np.mean(record_anomalies)
std_anomalies = np.std(record_anomalies)
if std_anomalies == 0:
std_anomalies = 0.00001
var_anomalies = np.var(record_anomalies)
mean_anomalies_short_window = np.mean(
record_anomalies[-int(history):])
likelihood = 1 - (
(norm.cdf(anom, mean_anomalies_short_window - mean_anomalies,
std_anomalies)) -
(norm.cdf(0, mean_anomalies_short_window - mean_anomalies,
std_anomalies)))
likelihood_test = 1 - (
anom -
(mean_anomalies_short_window - mean_anomalies)) / std_anomalies
likelihood_test_test = 1 - qfunction(
(mean_anomalies_short_window - mean_anomalies) / std_anomalies)
print "Likelihood"
print(likelihood_test_test)
anomalyLikelihood = AnomalyLikelihood()
anomalyProbability = anomalyLikelihood.anomalyProbability(
record['level'], anom, record['timestamp'])
ani = animation.FuncAnimation(fig,
animate,
interval=1000,
x=nb,
y=likelihood_test_test)
plt.show()
if likelihood_test_test >= 0.85:
print "Anomaly detected!"
print "Probability od being abnormal", likelihood_test_test
#ibefore = i
#if ibefore - iafter == 1:
# region = region + 1
# if region == 20:
# print i-20
# print 'Anomaly detcted!'
# print 'Probability of being abnormal', likelihood_test_test
# print 'Probability of being abnormal (nupic)', anomalyProbability
# region_anomaly = region_anomaly + 1
#else :
# region = 0
#iafter = ibefore
pass
# FIFO
events = reversed(events)
if PREDICT: import PREDICTmodel_params as model_params
else: import model_params as model_params
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"})
print "Model created!\n"
# Get the Model-Classes:
anomalyLikelihood = AnomalyLikelihood()
if PREDICT:
from nupic.data.inference_shifter import InferenceShifter
shifter = InferenceShifter()
if (WINDOWSIZE != None):
AnomalyScores = deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
else:
AnomalyScores = deque() # numpy.ones(len(events)), maxlen=len(events) ?
LikelihoodScores = deque()
r = redis.Redis(host=os.environ.get("REDIS_HOST", "127.0.0.1"),
port=int(os.environ.get("REDIS_PORT", 6379)),
db=int(os.environ.get("REDIS_DB", 0)))
# Feed data into the model:
class Anomaly(object):
"""Utility class for generating anomaly scores in different ways.
:param slidingWindowSize: [optional] - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
:param mode: (string) [optional] how to compute anomaly, one of:
- :const:`nupic.algorithms.anomaly.Anomaly.MODE_PURE`
- :const:`nupic.algorithms.anomaly.Anomaly.MODE_LIKELIHOOD`
- :const:`nupic.algorithms.anomaly.Anomaly.MODE_WEIGHTED`
:param binaryAnomalyThreshold: [optional] if set [0,1] anomaly score
will be discretized to 1/0 (1 if >= binaryAnomalyThreshold)
The transformation is applied after moving average is computed.
"""
# anomaly modes supported
MODE_PURE = "pure"
"""
Default mode. The raw anomaly score as computed by
:func:`~.anomaly_likelihood.computeRawAnomalyScore`
"""
MODE_LIKELIHOOD = "likelihood"
"""
Uses the :class:`~.anomaly_likelihood.AnomalyLikelihood` class, which models
probability of receiving this value and anomalyScore
"""
MODE_WEIGHTED = "weighted"
"""
Multiplies the likelihood result with the raw anomaly score that was used to
generate the likelihood (anomaly * likelihood)
"""
_supportedModes = (MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED)
def __init__(self,
slidingWindowSize=None,
mode=MODE_PURE,
binaryAnomalyThreshold=None):
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if (self._mode == Anomaly.MODE_LIKELIHOOD or
self._mode == Anomaly.MODE_WEIGHTED):
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
else:
self._likelihood = None
if not self._mode in self._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
self._binaryThreshold = binaryAnomalyThreshold
if binaryAnomalyThreshold is not None and (
not isinstance(binaryAnomalyThreshold, float) or
binaryAnomalyThreshold >= 1.0 or
binaryAnomalyThreshold <= 0.0 ):
raise ValueError("Anomaly: binaryAnomalyThreshold must be from (0,1) "
"or None if disabled.")
def compute(self, activeColumns, predictedColumns,
inputValue=None, timestamp=None):
"""Compute the anomaly score as the percent of active columns not predicted.
:param activeColumns: array of active column indices
:param predictedColumns: array of columns indices predicted in this step
(used for anomaly in step T+1)
:param inputValue: (optional) value of current input to encoders
(eg "cat" for category encoder)
(used in anomaly-likelihood)
:param timestamp: (optional) date timestamp when the sample occured
(used in anomaly-likelihood)
:returns: the computed anomaly score; float 0..1
"""
# Start by computing the raw anomaly score.
anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)
# Compute final anomaly based on selected mode.
if self._mode == Anomaly.MODE_PURE:
score = anomalyScore
elif self._mode == Anomaly.MODE_LIKELIHOOD:
if inputValue is None:
raise ValueError("Selected anomaly mode 'Anomaly.MODE_LIKELIHOOD' "
"requires 'inputValue' as parameter to compute() method. ")
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
# low likelihood -> hi anomaly
score = 1 - probability
elif self._mode == Anomaly.MODE_WEIGHTED:
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
score = anomalyScore * (1 - probability)
# Last, do moving-average if windowSize was specified.
if self._movingAverage is not None:
score = self._movingAverage.next(score)
# apply binary discretization if required
if self._binaryThreshold is not None:
if score >= self._binaryThreshold:
score = 1.0
else:
score = 0.0
return score
def __str__(self):
windowSize = 0
if self._movingAverage is not None:
windowSize = self._movingAverage.windowSize
return "Anomaly:\tmode=%s\twindowSize=%r" % (self._mode, windowSize)
def __eq__(self, other):
return (isinstance(other, Anomaly) and
other._mode == self._mode and
other._binaryThreshold == self._binaryThreshold and
other._movingAverage == self._movingAverage and
other._likelihood == self._likelihood)
def __setstate__(self, state):
"""deserialization"""
self.__dict__.update(state)
if not hasattr(self, '_mode'):
self._mode = Anomaly.MODE_PURE
if not hasattr(self, '_movingAverage'):
self._movingAverage = None
if not hasattr(self, '_binaryThreshold'):
self._binaryThreshold = None
minLatitude = lat
if float(earthquake.longitude) > maxLongitude:
maxLongitude = lng
if float(earthquake.longitude) < minLongitude:
minLongitude = lng
inp.seek(0)
next(inp)
# Create Model
model = ModelFactory.create(model_params.MODEL_PARAMS)
model.enableInference({"predictedField": "event"})
anomalyLikelihood = AnomalyLikelihood()
scores=deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
likelihoodScores=deque(maxlen=100)
r = redis.Redis(host=os.environ.get("REDIS_HOST", "127.0.0.1"),
port=int(os.environ.get("REDIS_PORT", 6379)),
db=int(os.environ.get("REDIS_DB", 0)))
for n, earthquake in enumerate(reversed(earthquakes)):
x = int(10000 * abs(float(earthquake.longitude) - minLongitude))
y = int(10000 * abs(float(earthquake.latitude) - minLatitude))
try:
event = (numpy.array([x, y]), int(10*float(earthquake.mag)))
class _ModelRunner(object):
""" Use OPF Model to process metric data samples from stdin and and emit
anomaly likelihood results to stdout
"""
# Input column meta info compatible with parameters generated by
# getScalarMetricWithTimeOfDayAnomalyParams
_INPUT_RECORD_SCHEMA = (
fieldmeta.FieldMetaInfo("c0", fieldmeta.FieldMetaType.datetime,
fieldmeta.FieldMetaSpecial.timestamp),
fieldmeta.FieldMetaInfo("c1", fieldmeta.FieldMetaType.float,
fieldmeta.FieldMetaSpecial.none),
)
def __init__(self, modelId, stats):
"""
:param str modelId: model identifier
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
"""
self._modelId = modelId
# NOTE: ModelRecordEncoder is implemented in the pull request
# https://github.com/numenta/nupic/pull/2432 that is not yet in master.
self._modelRecordEncoder = record_stream.ModelRecordEncoder(
fields=self._INPUT_RECORD_SCHEMA)
self._model = self._createModel(stats=stats)
self._anomalyLikelihood = AnomalyLikelihood()
@classmethod
def _createModel(cls, stats):
"""Instantiate and configure an OPF model
:param dict stats: Metric data stats per stats_schema.json in the
unicorn_backend package.
:returns: OPF Model instance
"""
# Generate swarm params
swarmParams = getScalarMetricWithTimeOfDayAnomalyParams(
metricData=[0],
minVal=stats["min"],
maxVal=stats["max"],
minResolution=stats.get("minResolution"))
model = ModelFactory.create(modelConfig=swarmParams["modelConfig"])
model.enableLearning()
model.enableInference(swarmParams["inferenceArgs"])
return model
@classmethod
def _readInputMessages(cls):
"""Create a generator that waits for and yields input messages from
stdin
yields two-tuple (<timestamp>, <scalar-value>), where <timestamp> is the
`datetime.datetime` timestamp of the metric data sample and <scalar-value>
is the floating point value of the metric data sample.
"""
while True:
message = sys.stdin.readline()
if message:
timestamp, scalarValue = json.loads(message)
yield (datetime.utcfromtimestamp(timestamp), scalarValue)
else:
# Front End closed the pipe (or died)
break
@classmethod
def _emitOutputMessage(cls, rowIndex, anomalyProbability):
"""Emit output message to stdout
:param int rowIndex: 0-based index of corresponding input sample
:param float anomalyProbability: computed anomaly probability value
"""
message = "%s\n" % (json.dumps([rowIndex, anomalyProbability]),)
sys.stdout.write(message)
sys.stdout.flush()
def _computeAnomalyProbability(self, inputRow):
""" Compute anomaly log likelihood score
:param tuple inputRow: Two-tuple input metric data row
(<datetime-timestamp>, <float-scalar>)
:returns: Log-scaled anomaly probability
:rtype: float
"""
# Generate raw anomaly score
inputRecord = self._modelRecordEncoder.encode(inputRow)
rawAnomalyScore = self._model.run(inputRecord).inferences["anomalyScore"]
# Generate anomaly likelihood score
anomalyProbability = self._anomalyLikelihood.anomalyProbability(
value=inputRow[1],
anomalyScore=rawAnomalyScore,
timestamp=inputRow[0])
return self._anomalyLikelihood.computeLogLikelihood(anomalyProbability)
def run(self):
""" Run the model: ingest and process the input metric data and emit output
messages containing anomaly scores
"""
g_log.info("Processing model=%s", self._modelId)
for rowIndex, inputRow in enumerate(self._readInputMessages()):
anomalyProbability = self._computeAnomalyProbability(inputRow)
self._emitOutputMessage(rowIndex=rowIndex,
anomalyProbability=anomalyProbability)
def read(cls, proto):
anomalyLikelihoodRegion = object.__new__(cls)
anomalyLikelihoodRegion.anomalyLikelihood = AnomalyLikelihood.read(proto)
return anomalyLikelihoodRegion
def runAnomaly(options):
"""
Create and run a CLA Model on the given dataset (based on the hotgym anomaly
client in NuPIC).
"""
# Load the model params JSON
with open("model_params.json") as fp:
modelParams = json.load(fp)
# Update the resolution value for the encoder
sensorParams = modelParams['modelParams']['sensorParams']
numBuckets = modelParams['modelParams']['sensorParams']['encoders'][
'value'].pop('numBuckets')
resolution = options.resolution
if resolution is None:
resolution = max(0.001, (options.max - options.min) / numBuckets)
print "Using resolution value: {0}".format(resolution)
sensorParams['encoders']['value']['resolution'] = resolution
model = ModelFactory.create(modelParams)
model.enableInference({'predictedField': 'value'})
with open(options.inputFile) as fin:
# Open file and setup headers
# Here we write the log likelihood value as the 'anomaly score'
# The actual CLA outputs are labeled 'raw anomaly score'
reader = csv.reader(fin)
csvWriter = csv.writer(open(options.outputFile, "wb"))
csvWriter.writerow([
"timestamp", "value", "_raw_score", "likelihood_score",
"log_likelihood_score"
])
headers = reader.next()
# The anomaly likelihood object
anomalyLikelihood = AnomalyLikelihood()
# Iterate through each record in the CSV file
print "Starting processing at", datetime.datetime.now()
for i, record in enumerate(reader, start=1):
# Convert input data to a dict so we can pass it into the model
inputData = dict(zip(headers, record))
inputData["value"] = float(inputData["value"])
inputData["dttm"] = dateutil.parser.parse(inputData["dttm"])
#inputData["dttm"] = datetime.datetime.now()
# Send it to the CLA and get back the raw anomaly score
result = model.run(inputData)
anomalyScore = result.inferences['anomalyScore']
# Compute the Anomaly Likelihood
likelihood = anomalyLikelihood.anomalyProbability(
inputData["value"], anomalyScore, inputData["dttm"])
logLikelihood = anomalyLikelihood.computeLogLikelihood(likelihood)
if likelihood > 0.9999:
print "Anomaly detected:", inputData['dttm'], inputData[
'value'], likelihood
# Write results to the output CSV file
csvWriter.writerow([
inputData["dttm"], inputData["value"], anomalyScore,
likelihood, logLikelihood
])
# Progress report
if (i % 1000) == 0: print i, "records processed"
print "Completed processing", i, "records at", datetime.datetime.now()
print "Anomaly scores for", options.inputFile,
print "have been written to", options.outputFile
class Anomaly(object):
"""Utility class for generating anomaly scores in different ways.
Supported modes:
MODE_PURE - the raw anomaly score as computed by computeRawAnomalyScore
MODE_LIKELIHOOD - uses the AnomalyLikelihood class on top of the raw
anomaly scores
MODE_WEIGHTED - multiplies the likelihood result with the raw anomaly score
that was used to generate the likelihood
"""
# anomaly modes supported
MODE_PURE = "pure"
MODE_LIKELIHOOD = "likelihood"
MODE_WEIGHTED = "weighted"
_supportedModes = (MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED)
def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
def compute(self, activeColumns, predictedColumns,
inputValue=None, timestamp=None):
"""Compute the anomaly score as the percent of active columns not predicted.
@param activeColumns: array of active column indices
@param predictedColumns: array of columns indices predicted in this step
(used for anomaly in step T+1)
@param inputValue: (optional) value of current input to encoders
(eg "cat" for category encoder)
(used in anomaly-likelihood)
@param timestamp: (optional) date timestamp when the sample occured
(used in anomaly-likelihood)
@return the computed anomaly score; float 0..1
"""
# Start by computing the raw anomaly score.
anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)
# Compute final anomaly based on selected mode.
if self._mode == Anomaly.MODE_PURE:
score = anomalyScore
elif self._mode == Anomaly.MODE_LIKELIHOOD:
if inputValue is None:
raise ValueError("Selected anomaly mode 'Anomaly.MODE_LIKELIHOOD' "
"requires 'inputValue' as parameter to compute() method. ")
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
# low likelihood -> hi anomaly
score = 1 - probability
elif self._mode == Anomaly.MODE_WEIGHTED:
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
score = anomalyScore * (1 - probability)
# Last, do moving-average if windowSize was specified.
if self._movingAverage is not None:
score = self._movingAverage.next(score)
return score
class Anomaly(object):
"""Utility class for generating anomaly scores in different ways.
Supported modes:
MODE_PURE - the raw anomaly score as computed by computeRawAnomalyScore
MODE_LIKELIHOOD - uses the AnomalyLikelihood class on top of the raw
anomaly scores
MODE_WEIGHTED - multiplies the likelihood result with the raw anomaly score
that was used to generate the likelihood
"""
# anomaly modes supported
MODE_PURE = "pure"
MODE_LIKELIHOOD = "likelihood"
MODE_WEIGHTED = "weighted"
_supportedModes = (MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED)
def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
"""
self._mode = mode
self._useMovingAverage = slidingWindowSize > 0
self._buf = None
self._i = None
# Using cumulative anomaly, sliding window
if self._useMovingAverage:
self._windowSize = slidingWindowSize
# Sliding window buffer
self._buf = numpy.array([0] * self._windowSize, dtype=numpy.float)
self._i = 0 # index pointer to actual position
elif slidingWindowSize is not None:
raise TypeError(
"Anomaly: if you define slidingWindowSize, it has to be an "
"integer > 0; slidingWindowSize=%r" % slidingWindowSize)
if self._mode == Anomaly.MODE_LIKELIHOOD:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)
def computeAnomalyScore(self, activeColumns, predictedColumns, value=None,
timestamp=None):
"""Compute the anomaly score as the percent of active columns not predicted.
@param activeColumns: array of active column indices
@param predictedColumns: array of columns indices predicted in this step
(used for anomaly in step T+1)
@param value: (optional) metric value of current input
(used in anomaly-likelihood)
@param timestamp: (optional) date timestamp when the sample occured
(used in anomaly-likelihood)
@return the computed anomaly score; float 0..1
"""
# Start by computing the raw anomaly score.
anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)
# Compute final anomaly based on selected mode.
if self._mode == Anomaly.MODE_PURE:
score = anomalyScore
elif self._mode == Anomaly.MODE_LIKELIHOOD:
# TODO add tests for likelihood modes
probability = self._likelihood.anomalyProbability(
value, anomalyScore, timestamp)
score = probability
elif self._mode == Anomaly.MODE_WEIGHTED:
probability = self._likelihood.anomalyProbability(
value, anomalyScore, timestamp)
score = anomalyScore * probability
# Last, do moving-average if windowSize was specified.
if self._useMovingAverage:
score = self._movingAverage(score)
return score
def _movingAverage(self, newElement=None):
"""moving average
@param newValue (optional) add a new element before computing the avg
@return moving average of self._windowSize last elements
"""
if newElement is not None:
self._buf[self._i]= newElement
self._i = (self._i + 1) % self._windowSize
return self._buf.sum() / float(self._windowSize) # normalize to 0..1
result = model.run(record)
#print "prediction: ", result.inferences["multiStepBestPredictions"][1]
anom = result.inferences['anomalyScore']
#print "anomaly score: ", anom
record_anomalies.append(anom)
mean_anomalies = np.mean(record_anomalies)
std_anomalies = np.std(record_anomalies)
var_anomalies = np.var(record_anomalies)
mean_anomalies_short_window = np.mean(record_anomalies[-int(0.05 * i):])
#likelihood = 1-((norm.cdf(anom, mean_anomalies_short_window-mean_anomalies, std_anomalies))-(norm.cdf(0, mean_anomalies_short_window-mean_anomalies, std_anomalies)))
likelihood = 1 - ((norm.cdf(
anom, mean_anomalies_short_window - mean_anomalies, std_anomalies)) -
(norm.cdf(0, mean_anomalies_short_window -
mean_anomalies, std_anomalies)))
#print "likelihood score: ", likelihood
anomalyLikelihood = AnomalyLikelihood()
anomalyProbability = anomalyLikelihood.anomalyProbability(
record['level'], anom, record['timestamp'])
#print "anomalyProbability: ", anomalyProbability
if likelihood >= 1:
cpt = cpt + 1
print i
print "Anomaly detected!"
print "Total nb of anomalies", cpt
"""
data = getData()
for _ in xrange(5):
print data.next()
with open('export_dataframe_df2.csv') as inputFile:
import TESTmodel_params as model_params
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"}) # Predict not only event but also scalar! TODO
# model.enableInference({"predictedField": "scalar"})
# model.enableInference({"predictedField": "timestamp"})
print "Model created!\n"
# Get the Model-Classes:
anomalyLikelihood = AnomalyLikelihood()
if (WINDOWSIZE != None): AnomalyScores = deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
else: AnomalyScores = deque()
LikelihoodScores = deque()
if REDIS:
r = redis.Redis(host=os.environ.get("REDIS_HOST", "127.0.0.1"),
port=int(os.environ.get("REDIS_PORT", 6379)),
db=int(os.environ.get("REDIS_DB", 0)))
print "Start Data-Feed...\n"
for n, event in enumerate(events):
# Cluster or not? // Convert all Coords. to non-neg Ints (better handeled by the model):
if CLUSTERING:
class Anomaly(object):
"""Utility class for generating anomaly scores in different ways.
Supported modes:
MODE_PURE - the raw anomaly score as computed by computeRawAnomalyScore
MODE_LIKELIHOOD - uses the AnomalyLikelihood class on top of the raw
anomaly scores
MODE_WEIGHTED - multiplies the likelihood result with the raw anomaly score
that was used to generate the likelihood
"""
# anomaly modes supported
MODE_PURE = "pure"
MODE_LIKELIHOOD = "likelihood"
MODE_WEIGHTED = "weighted"
_supportedModes = (MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED)
def __init__(self,
slidingWindowSize=None,
mode=MODE_PURE,
binaryAnomalyThreshold=None):
"""
@param slidingWindowSize (optional) - how many elements are summed up;
enables moving average on final anomaly score; int >= 0
@param mode (optional) - (string) how to compute anomaly;
possible values are:
- "pure" - the default, how much anomal the value is;
float 0..1 where 1=totally unexpected
- "likelihood" - uses the anomaly_likelihood code;
models probability of receiving this value and anomalyScore
- "weighted" - "pure" anomaly weighted by "likelihood"
(anomaly * likelihood)
@param binaryAnomalyThreshold (optional) - if set [0,1] anomaly score
will be discretized to 1/0 (1 if >= binaryAnomalyThreshold)
The transformation is applied after moving average is computed and updated.
"""
self._mode = mode
if slidingWindowSize is not None:
self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
else:
self._movingAverage = None
if self._mode == Anomaly.MODE_LIKELIHOOD or self._mode == Anomaly.MODE_WEIGHTED:
self._likelihood = AnomalyLikelihood() # probabilistic anomaly
if not self._mode in Anomaly._supportedModes:
raise ValueError("Invalid anomaly mode; only supported modes are: "
"Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
"Anomaly.MODE_WEIGHTED; you used: %r" %
self._mode)
self._binaryThreshold = binaryAnomalyThreshold
if binaryAnomalyThreshold is not None and (
not isinstance(binaryAnomalyThreshold, float)
or binaryAnomalyThreshold >= 1.0
or binaryAnomalyThreshold <= 0.0):
raise ValueError(
"Anomaly: binaryAnomalyThreshold must be from (0,1) "
"or None if disabled.")
def compute(self,
activeColumns,
predictedColumns,
inputValue=None,
timestamp=None):
"""Compute the anomaly score as the percent of active columns not predicted.
@param activeColumns: array of active column indices
@param predictedColumns: array of columns indices predicted in this step
(used for anomaly in step T+1)
@param inputValue: (optional) value of current input to encoders
(eg "cat" for category encoder)
(used in anomaly-likelihood)
@param timestamp: (optional) date timestamp when the sample occured
(used in anomaly-likelihood)
@return the computed anomaly score; float 0..1
"""
# Start by computing the raw anomaly score.
anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)
# Compute final anomaly based on selected mode.
if self._mode == Anomaly.MODE_PURE:
score = anomalyScore
elif self._mode == Anomaly.MODE_LIKELIHOOD:
if inputValue is None:
raise ValueError(
"Selected anomaly mode 'Anomaly.MODE_LIKELIHOOD' "
"requires 'inputValue' as parameter to compute() method. ")
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
# low likelihood -> hi anomaly
score = 1 - probability
elif self._mode == Anomaly.MODE_WEIGHTED:
probability = self._likelihood.anomalyProbability(
inputValue, anomalyScore, timestamp)
score = anomalyScore * (1 - probability)
# Last, do moving-average if windowSize was specified.
if self._movingAverage is not None:
score = self._movingAverage.next(score)
# apply binary discretization if required
if self._binaryThreshold is not None:
if score >= self._binaryThreshold:
score = 1.0
else:
score = 0.0
return score
def __str__(self):
windowSize = 0
if self._movingAverage is not None:
windowSize = self._movingAverage.windowSize
return "Anomaly:\tmode=%s\twindowSize=%r" % (self._mode, windowSize)
def __setstate__(self, state):
"""deserialization"""
self.__dict__.update(state)
if not hasattr(self, '_mode'):
self._mode = Anomaly.MODE_PURE
if not hasattr(self, '_movingAverage'):
self._movingAverage = None
if not hasattr(self, '_binaryThreshold'):
self._binaryThreshold = None
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"}) # Predict not only event but also scalar! TODO
# model.enableInference({"predictedField": "scalar"})
# model.enableInference({"predictedField": "timestamp"})
print "Model created!\n"
# Get the Model-Classes:
if PREDICT:
from nupic.data.inference_shifter import InferenceShifter
shifter = InferenceShifter()
anomalyLikelihood = AnomalyLikelihood()
if (WINDOWSIZE != None): AnomalyScores = deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
else: AnomalyScores = deque()
LikelihoodScores = deque()
if REDIS:
r = redis.Redis(host=os.environ.get("REDIS_HOST", "127.0.0.1"),
port=int(os.environ.get("REDIS_PORT", 6379)),
db=int(os.environ.get("REDIS_DB", 0)))
print "Start Data-Feed...\n"
for n, event in enumerate(events):
# Cluster or not? // Convert all Coords. to non-neg Ints (better handeled by the model):
if CLUSTERING:
class AnomalyLikelihoodRegion(PyRegion):
"""Region for computing the anomaly likelihoods."""
@classmethod
def getSpec(cls):
return {
"description": ("Region that computes anomaly likelihoods for \
temporal memory."),
"singleNodeOnly":
True,
"inputs": {
"rawAnomalyScore": {
"description": "The anomaly score whose \
likelihood is to be computed",
"dataType": "Real32",
"count": 1,
"required": True,
"isDefaultInput": False
},
"metricValue": {
"description": "The input metric value",
"dataType": "Real32",
"count": 1,
"required": True,
"isDefaultInput": False
},
},
"outputs": {
"anomalyLikelihood": {
"description": "The resultant anomaly likelihood",
"dataType": "Real32",
"count": 1,
"isDefaultOutput": True,
},
},
"parameters": {
"learningPeriod": {
"description": "The number of iterations required for the\
algorithm to learn the basic patterns in the dataset\
and for the anomaly score to 'settle down'.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 288,
"accessMode": "ReadWrite"
},
"estimationSamples": {
"description": "The number of reasonable anomaly scores\
required for the initial estimate of the\
Gaussian.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 100,
"accessMode": "ReadWrite"
},
"historicWindowSize": {
"description": "Size of sliding window of historical data\
points to maintain for periodic reestimation\
of the Gaussian.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 8640,
"accessMode": "ReadWrite"
},
"reestimationPeriod": {
"description": "How often we re-estimate the Gaussian\
distribution.",
"dataType": "UInt32",
"count": 1,
"constraints": "",
"defaultValue": 100,
"accessMode": "ReadWrite"
},
},
"commands": {},
}
def __init__(self,
learningPeriod=288,
estimationSamples=100,
historicWindowSize=8640,
reestimationPeriod=100):
self.anomalyLikelihood = AnomalyLikelihood(
learningPeriod=learningPeriod,
estimationSamples=estimationSamples,
historicWindowSize=historicWindowSize,
reestimationPeriod=reestimationPeriod)
def __eq__(self, other):
return self.anomalyLikelihood == other.anomalyLikelihood
def __ne__(self, other):
return not self == other
@classmethod
def read(cls, proto):
anomalyLikelihoodRegion = object.__new__(cls)
anomalyLikelihoodRegion.anomalyLikelihood = AnomalyLikelihood.read(
proto)
return anomalyLikelihoodRegion
def write(self, proto):
self.anomalyLikelihood.write(proto)
def initialize(self):
pass
def compute(self, inputs, outputs):
anomalyScore = inputs["rawAnomalyScore"][0]
value = inputs["metricValue"][0]
anomalyProbability = self.anomalyLikelihood.anomalyProbability(
value, anomalyScore)
outputs["anomalyLikelihood"][0] = anomalyProbability
def read(cls, proto):
anomalyLikelihoodRegion = object.__new__(cls)
anomalyLikelihoodRegion.anomalyLikelihood = AnomalyLikelihood.read(
proto)
return anomalyLikelihoodRegion
def getSchema(cls): return AnomalyLikelihood.getSchema()