Created
June 27, 2013 12:36
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Scott Purdy's hotgym example driver, modified to use ipython and pylab to graph prediction and errors. A good deal of library installation and reconfiguration is needed to make this work; read the comment at the top of the file and contact me if you can't get it working.
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#!/usr/bin/env python | |
# ---------------------------------------------------------------------- | |
# Numenta Platform for Intelligent Computing (NuPIC) | |
# Copyright (C) 2013, Numenta, Inc. Unless you have purchased from | |
# Numenta, Inc. a separate commercial license for this software code, the | |
# following terms and conditions apply: | |
# | |
# This program is free software: you can redistribute it and/or modify | |
# it under the terms of the GNU General Public License version 3 as | |
# published by the Free Software Foundation. | |
# | |
# This program is distributed in the hope that it will be useful, | |
# but WITHOUT ANY WARRANTY; without even the implied warranty of | |
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. | |
# See the GNU General Public License for more details. | |
# | |
# You should have received a copy of the GNU General Public License | |
# along with this program. If not, see http://www.gnu.org/licenses. | |
# | |
# http://numenta.org/licenses/ | |
# ---------------------------------------------------------------------- | |
"""A simple client to create a CLA model for hotgym, originally by | |
Scott Purdy. Modified by Kevin Archie to graph predicted and actual | |
values.""" | |
# NOTE: this code requires ipython with pylab; I've been using the | |
# Tornado-hosted notebook but presumably the Qt console could also | |
# work. It also requires a more modern matplotlib than the one | |
# embedded in nupic; I found it was sufficient to delete the nupic | |
# site_packages/matplotlib thus exposing the system Python version | |
# of the library. | |
import csv | |
import datetime | |
import matplotlib.pyplot as plt | |
import numpy as np | |
from IPython.display import clear_output, display | |
from nupic.data.datasethelpers import findDataset | |
from nupic.frameworks.opf.modelfactory import ModelFactory | |
from nupic.data.inference_shifter import InferenceShifter | |
import model_params | |
DATA_PATH = "extra/hotgym/hotgym.csv" | |
def createModel(): | |
return ModelFactory.create(model_params.MODEL_PARAMS) | |
def should_consolidate(interval, offset, current): | |
if interval and interval * 2 <= current - offset: | |
if interval * 2 < current - offset: | |
print 'WARNING: missed consolidation; current interval', offset, 'to', current | |
return True | |
else: | |
return False | |
def consolidate(interval, offset, obs, preds): | |
distance = np.linalg.norm(np.array(preds[0:interval-1]) - | |
np.array(obs[0:interval-1])) | |
return offset+interval, obs[interval:], preds[interval:], distance | |
def runHotgym(**opts): | |
model = createModel() | |
model.enableInference({'predictionSteps': [1, 5], | |
'predictedField': 'consumption', | |
'numRecords': 4000}) | |
shifter = InferenceShifter() | |
print 'Using dataset', findDataset(DATA_PATH) | |
with open (findDataset(DATA_PATH)) as fin: | |
reader = csv.reader(fin) | |
headers = reader.next() | |
# print headers | |
# print reader.next() | |
# print reader.next() | |
# skip the additional header lines | |
reader.next() | |
reader.next() | |
ys = [] | |
zs = [] | |
errs = [[],[]] | |
consolidated_to = 0 | |
if 'consolidate_by' in opts: | |
consolidate_by = opts['consolidate_by'] | |
else: | |
consolidate_by = None | |
for record in reader: | |
# print record | |
modelInput = dict(zip(headers, record)) | |
modelInput["consumption"] = float(modelInput["consumption"]) | |
modelInput["timestamp"] = datetime.datetime.strptime( | |
modelInput["timestamp"], "%Y-%m-%d %H:%M:%S.%f") | |
# TODO: make this work so that predictions appear aligned with the | |
# timestep that they are predicting (rather than the time step where | |
# the prediction is generated). program hangs when I try it | |
# result = shifter.shift(model.run(modelInput)) | |
result = model.run(modelInput) | |
if (should_consolidate(consolidate_by, consolidated_to, | |
result.predictionNumber)): | |
consolidated_to, ys, zs, distance = consolidate(consolidate_by, | |
consolidated_to, | |
ys, zs) | |
errs[0].append(consolidated_to) | |
errs[1].append(distance) | |
# print result | |
if 'plot' in opts: | |
ys.append(modelInput['consumption']) | |
zs.append(result.inferences['prediction'][0] or 0) | |
if 'max_predictions' in opts and opts['max_predictions'] < result.predictionNumber: | |
break | |
if 'plot' in opts and (not 'plot_interval' in opts or | |
result.predictionNumber and | |
(10 > result.predictionNumber or | |
0 == result.predictionNumber % opts['plot_interval'])): | |
xs = range(consolidated_to,result.predictionNumber+1) | |
opts['plot']([xs,xs,errs[0]], [ys,zs,errs[1]]) | |
def update_plot(fig, axes, xs, ys): | |
clear_output() | |
axes[0].clear() | |
axes[0].plot(xs[0], ys[0], label='consumption') | |
axes[0].plot(xs[1], ys[1], 'm', label='prediction') | |
axes[1].clear() | |
axes[1].plot(xs[1], np.subtract(ys[1],ys[0]), label='error') | |
axes[2].vlines(xs[2],0,ys[2], label='RMS error') | |
display(fig) | |
def doplot(fig, axes): | |
return lambda xs,ys: update_plot(fig, axes, xs, ys) | |
if __name__ == "__main__": | |
predictions=80000 | |
fig,axes = plt.subplots(3, 1, figsize=(15,10)) | |
runHotgym(plot=doplot(fig,axes), | |
max_predictions=predictions, | |
plot_interval=100, | |
consolidate_by=500) |
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