def __init__(self, streamDefDict):
""" Constructor
streamDefDict: stream definition, as defined in
py/nupic/frameworks/opf/jsonschema/stream_def.json
"""
# Create the object to read from
self._reader = StreamReader(streamDefDict, saveOutput=True)
return
def run(self):
""" Runs the OPF Model
Parameters:
-------------------------------------------------------------------------
retval: (completionReason, completionMsg)
where completionReason is one of the ClientJobsDAO.CMPL_REASON_XXX
equates.
"""
# -----------------------------------------------------------------------
# Load the experiment's description.py module
descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir(
self._experimentDir)
expIface = helpers.getExperimentDescriptionInterfaceFromModule(
descriptionPyModule)
expIface.normalizeStreamSources()
modelDescription = expIface.getModelDescription()
self._modelControl = expIface.getModelControl()
# -----------------------------------------------------------------------
# Create the input data stream for this task
streamDef = self._modelControl['dataset']
from nupic.data.stream_reader import StreamReader
readTimeout = 0
self._inputSource = StreamReader(streamDef,
isBlocking=False,
maxTimeout=readTimeout)
# -----------------------------------------------------------------------
#Get field statistics from the input source
fieldStats = self._getFieldStats()
# -----------------------------------------------------------------------
# Construct the model instance
self._model = ModelFactory.create(modelDescription)
self._model.setFieldStatistics(fieldStats)
self._model.enableLearning()
self._model.enableInference(
self._modelControl.get("inferenceArgs", None))
# -----------------------------------------------------------------------
# Instantiate the metrics
self.__metricMgr = MetricsManager(
self._modelControl.get('metrics', None),
self._model.getFieldInfo(), self._model.getInferenceType())
self.__loggedMetricPatterns = self._modelControl.get(
"loggedMetrics", [])
self._optimizedMetricLabel = self.__getOptimizedMetricLabel()
self._reportMetricLabels = matchPatterns(self._reportKeyPatterns,
self._getMetricLabels())
# -----------------------------------------------------------------------
# Initialize periodic activities (e.g., for model result updates)
self._periodic = self._initPeriodicActivities()
# -----------------------------------------------------------------------
# Create our top-level loop-control iterator
numIters = self._modelControl.get('iterationCount', -1)
# Are we asked to turn off learning for a certain # of iterations near the
# end?
learningOffAt = None
iterationCountInferOnly = self._modelControl.get(
'iterationCountInferOnly', 0)
if iterationCountInferOnly == -1:
self._model.disableLearning()
elif iterationCountInferOnly > 0:
assert numIters > iterationCountInferOnly, "when iterationCountInferOnly " \
"is specified, iterationCount must be greater than " \
"iterationCountInferOnly."
learningOffAt = numIters - iterationCountInferOnly
self.__runTaskMainLoop(numIters, learningOffAt=learningOffAt)
# -----------------------------------------------------------------------
# Perform final operations for model
self._finalize()
return (self._cmpReason, None)
def __init__(self, streamDefDict):
# Create the object to read from
self._reader = StreamReader(streamDefDict, saveOutput=True)
return
