1
},
# Iteration count: maximum number of iterations. Each iteration corresponds
# to one record from the (possibly aggregated) dataset. The task is
# terminated when either number of iterations reaches iterationCount or
# all records in the (possibly aggregated) database have been processed,
# whichever occurs first.
#
# iterationCount of -1 = iterate over the entire dataset
#'iterationCount' : ITERATION_COUNT,
# Metrics: A list of MetricSpecs that instantiate the metrics that are
# computed for this experiment
'metrics': [
MetricSpec(field=u'consumption',
inferenceElement=InferenceElement.prediction,
metric='rmse'),
],
# Logged Metrics: A sequence of regular expressions that specify which of
# the metrics from the Inference Specifications section MUST be logged for
# every prediction. The regex's correspond to the automatically generated
# metric labels. This is similar to the way the optimization metric is
# specified in permutations.py.
'loggedMetrics': ['.*nupicScore.*'],
}
descriptionInterface = ExperimentDescriptionAPI(modelConfig=config,
control=control)
# Logged Metrics: A sequence of regular expressions that specify which of
# the metrics from the Inference Specifications section MUST be logged for
# every prediction. The regex's correspond to the automatically generated
# metric labels. This is similar to the way the optimization metric is
# specified in permutations.py.
'loggedMetrics': [],
# Callbacks for experimentation/research (optional)
'callbacks': {
# Callbacks to be called at the beginning of a task, before model iterations.
# Signature: callback(<reference to OPFExperiment>); returns nothing
'setup': [
htmPredictionModelControlEnableSPLearningCb,
htmPredictionModelControlEnableTPLearningCb
],
# Callbacks to be called after every learning/inference iteration
# Signature: callback(<reference to OPFExperiment>); returns nothing
'postIter': [],
# Callbacks to be called when the experiment task is finished
# Signature: callback(<reference to OPFExperiment>); returns nothing
'finish': []
}
} # End of taskControl
}, # End of task
]
descriptionInterface = ExperimentDescriptionAPI(modelConfig=config,
taskList=tasks)
