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)