def initializeAggregator(aggSpec, modelSpec):
inputRecordSchema = (
fieldmeta.FieldMetaInfo(modelSpec["timestampFieldName"],
fieldmeta.FieldMetaType.datetime,
fieldmeta.FieldMetaSpecial.timestamp),
fieldmeta.FieldMetaInfo(modelSpec["valueFieldName"],
fieldmeta.FieldMetaType.float,
fieldmeta.FieldMetaSpecial.none),
)
dataAggregator = 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)
return dataAggregator
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)