One Hot Gym Prediction using getScalarMetricWithTimeOfDayAnomalyParams

run.py

# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc.  Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
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"""
Groups together code used for creating a NuPIC model and dealing with IO.
(This is a component of the One Hot Gym Prediction Tutorial.)
"""
import importlib
import sys
import csv
import datetime

from nupic.data.inference_shifter import InferenceShifter
from nupic.frameworks.opf.metrics import MetricSpec
from nupic.frameworks.opf.model_factory import ModelFactory
from nupic.frameworks.opf.prediction_metrics_manager import MetricsManager
from nupic.frameworks.opf.common_models.cluster_params import (getScalarMetricWithTimeOfDayAnomalyParams)

import nupic_output


DESCRIPTION = (
  "Starts a NuPIC model from the model params returned by the swarm\n"
  "and pushes each line of input from the gym into the model. Results\n"
  "are written to an output file (default) or plotted dynamically if\n"
  "the --plot option is specified.\n"
  "NOTE: You must run ./swarm.py before this, because model parameters\n"
  "are required to run NuPIC.\n"
)
GYM_NAME = "rec-center-hourly"  # or use "rec-center-every-15m-large"
DATA_DIR = "."
MODEL_PARAMS_DIR = "./model_params"
# '7/2/10 0:00'
DATE_FORMAT = "%m/%d/%y %H:%M"

_METRIC_SPECS = (
    MetricSpec(field='kw_energy_consumption', metric='multiStep',
               inferenceElement='multiStepBestPredictions',
               params={'errorMetric': 'aae', 'window': 1000, 'steps': 1}),
    MetricSpec(field='kw_energy_consumption', metric='trivial',
               inferenceElement='prediction',
               params={'errorMetric': 'aae', 'window': 1000, 'steps': 1}),
    MetricSpec(field='kw_energy_consumption', metric='multiStep',
               inferenceElement='multiStepBestPredictions',
               params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': 1}),
    MetricSpec(field='kw_energy_consumption', metric='trivial',
               inferenceElement='prediction',
               params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': 1}),
)

def createModel(modelParams):
  model = ModelFactory.create(modelConfig=modelParams["modelConfig"])
  # model.enableInference({"predictedField": "kw_energy_consumption"})
  model.enableLearning()
  model.enableInference(modelParams["inferenceArgs"])
  return model

def runIoThroughNupic(inputData, model, gymName, plot):
  inputFile = open(inputData, "rb")
  csvReader = csv.reader(inputFile)
  # skip header rows
  csvReader.next()
  csvReader.next()
  csvReader.next()

  shifter = InferenceShifter()
  if plot:
    output = nupic_output.NuPICPlotOutput([gymName])
  else:
    output = nupic_output.NuPICFileOutput([gymName])

  metricsManager = MetricsManager(_METRIC_SPECS, model.getFieldInfo(),
                                  model.getInferenceType())

  counter = 0
  for row in csvReader:
    counter += 1
    timestamp = datetime.datetime.strptime(row[0], DATE_FORMAT)
    consumption = float(row[1])
    result = model.run({
      "timestamp": timestamp,
      "kw_energy_consumption": consumption
    })
    result.metrics = metricsManager.update(result)

    if counter % 100 == 0:
      print "Read %i lines..." % counter
      print ("After %i records, 1-step altMAPE=%f" % (counter,
              result.metrics["multiStepBestPredictions:multiStep:"
                             "errorMetric='altMAPE':steps=1:window=1000:"
                             "field=kw_energy_consumption"]))

    if plot:
      result = shifter.shift(result)

    prediction = result.inferences["multiStepBestPredictions"][1]
    output.write([timestamp], [consumption], [prediction])

    if plot and counter % 20 == 0:
        output.refreshGUI()

  inputFile.close()
  output.close()



def runModel(gymName, plot=False):
  print "Creating model from %s..." % gymName
  inputData = "%s/%s.csv" % (DATA_DIR, gymName.replace(" ", "_"))

  inputFile = open(inputData, "rb")
  csvReader = csv.reader(inputFile)
  csvReader.next()
  csvReader.next()
  csvReader.next()
  data = []
  for row in csvReader:
    consumption = float(row[1])
    data.append(consumption)
  inputFile.close()

  params = getScalarMetricWithTimeOfDayAnomalyParams(data)
  model = createModel(params)
  runIoThroughNupic(inputData, model, gymName, plot)



if __name__ == "__main__":
  print DESCRIPTION
  plot = False
  args = sys.argv[1:]
  if "--plot" in args:
    plot = True
  runModel(GYM_NAME, plot=plot)