def __init__(self, model, task, cmdOptions):
""" Constructor
Args:
model: The OPF Model instance against which to run the task
task: A dictionary conforming to opfTaskSchema.json
cmdOptions: ParseCommandLineOptionsResult namedtuple
"""
validateOpfJsonValue(task, "opfTaskSchema.json")
# Set up our logger
self.__logger = logging.getLogger(".".join(
['com.numenta', self.__class__.__module__, self.__class__.__name__]))
#self.__logger.setLevel(logging.DEBUG)
self.__logger.debug(("Instantiated %s(" + \
"model=%r, " + \
"task=%r, " + \
"cmdOptions=%r)") % \
(self.__class__.__name__,
model,
task,
cmdOptions))
# Generate a new dataset from streamDef and create the dataset reader
streamDef = task['dataset']
datasetReader = opfbasicenvironment.BasicDatasetReader(streamDef)
self.__model = model
self.__datasetReader = datasetReader
self.__task = task
self.__cmdOptions = cmdOptions
self.__predictionLogger = opfbasicenvironment.BasicPredictionLogger(
fields=model.getFieldInfo(),
experimentDir=cmdOptions.experimentDir,
label=task['taskLabel'],
inferenceType=self.__model.getInferenceType())
taskControl = task['taskControl']
# Create Task Driver
self.__taskDriver = OPFTaskDriver(
taskControl=taskControl,
model=model)
loggedMetricPatterns = taskControl.get('loggedMetrics', None)
loggedMetricLabels = matchPatterns(loggedMetricPatterns,
self.__taskDriver.getMetricLabels())
self.__predictionLogger.setLoggedMetrics(loggedMetricLabels)
# Create a prediction metrics logger
self.__metricsLogger = opfbasicenvironment.BasicPredictionMetricsLogger(
experimentDir=cmdOptions.experimentDir,
label=task['taskLabel'])
