def convertNupicEnvToOPF(self):
"""
TODO: document
"""
# We need to create a task structure, most of which is taken verbatim
# from the Nupic control dict
task = dict(self.__control)
task.pop('environment')
inferenceArgs = task.pop('inferenceArgs')
task['taskLabel'] = 'DefaultTask'
# Create the iterationCycle element that will be placed inside the
# taskControl.
iterationCount = task.get('iterationCount', -1)
iterationCountInferOnly = task.pop('iterationCountInferOnly', 0)
if iterationCountInferOnly == -1:
iterationCycle = [
IterationPhaseSpecInferOnly(1000, inferenceArgs=inferenceArgs)
]
elif iterationCountInferOnly > 0:
assert iterationCount > 0, "When iterationCountInferOnly is specified, "\
"iterationCount must also be specified and not be -1"
iterationCycle = [
IterationPhaseSpecLearnAndInfer(iterationCount -
iterationCountInferOnly,
inferenceArgs=inferenceArgs),
IterationPhaseSpecInferOnly(iterationCountInferOnly,
inferenceArgs=inferenceArgs)
]
else:
iterationCycle = [
IterationPhaseSpecLearnAndInfer(1000,
inferenceArgs=inferenceArgs)
]
taskControl = dict(metrics=task.pop('metrics'),
loggedMetrics=task.pop('loggedMetrics'),
iterationCycle=iterationCycle)
task['taskControl'] = taskControl
# Create the new control
self.__control = dict(environment=OpfEnvironment.Nupic, tasks=[task])
# 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' : -1,
# Task Control parameters for OPFTaskDriver (per opfTaskControlSchema.json)
'taskControl' : {
# Iteration cycle list consisting of opf_task_driver.IterationPhaseSpecXXXXX
# instances.
'iterationCycle' : [
#IterationPhaseSpecLearnOnly(1000),
IterationPhaseSpecLearnAndInfer(1000),
#IterationPhaseSpecInferOnly(10),
],
# Metrics: A list of MetricSpecs that instantiate the metrics that are
# computed for this experiment
'metrics':[
MetricSpec(metric='avg_err', inferenceElement='classification',
params={'window': 200}),
MetricSpec(metric='neg_auc', inferenceElement='classConfidences',
params={'window': 200, 'computeEvery': 10}),
],
# 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
# 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' : -1,
# Task Control parameters for OPFTaskDriver (per opfTaskControlSchema.json)
'taskControl' : {
# Iteration cycle list consisting of opf_task_driver.IterationPhaseSpecXXXXX
# instances.
'iterationCycle' : [
#IterationPhaseSpecLearnOnly(1000),
IterationPhaseSpecLearnAndInfer(1000, inferenceArgs=None),
#IterationPhaseSpecInferOnly(10, inferenceArgs=None),
],
'metrics' : [
],
# 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.*'],
# Callbacks for experimentation/research (optional)
# 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' : -1,
# Task Control parameters for OPFTaskDriver (per opfTaskControlSchema.json)
'taskControl' : {
# Iteration cycle list consisting of opf_task_driver.IterationPhaseSpecXXXXX
# instances.
'iterationCycle' : [
#IterationPhaseSpecLearnOnly(1000),
IterationPhaseSpecLearnAndInfer(1000, dict(predictedField="consumption")),
#IterationPhaseSpecInferOnly(10),
],
'metrics' :[
MetricSpec(metric='rmse',
field="consumption",
inferenceElement=InferenceElement.prediction),
],
# 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' : [],