def runModelGivenBaseAndParams(modelID, jobID, baseDescription, params,
predictedField, reportKeys, optimizeKey, jobsDAO,
modelCheckpointGUID, logLevel=None, predictionCacheMaxRecords=None):
""" This creates an experiment directory with a base.py description file
created from 'baseDescription' and a description.py generated from the
given params dict and then runs the experiment.
Parameters:
-------------------------------------------------------------------------
modelID: ID for this model in the models table
jobID: ID for this hypersearch job in the jobs table
baseDescription: Contents of a description.py with the base experiment
description
params: Dictionary of specific parameters to override within
the baseDescriptionFile.
predictedField: Name of the input field for which this model is being
optimized
reportKeys: Which metrics of the experiment to store into the
results dict of the model's database entry
optimizeKey: Which metric we are optimizing for
jobsDAO Jobs data access object - the interface to the
jobs database which has the model's table.
modelCheckpointGUID: A persistent, globally-unique identifier for
constructing the model checkpoint key
logLevel: override logging level to this value, if not None
retval: (completionReason, completionMsg)
"""
from nupic.swarming.ModelRunner import OPFModelRunner
# The logger for this method
logger = logging.getLogger('com.numenta.nupic.hypersearch.utils')
# --------------------------------------------------------------------------
# Create a temp directory for the experiment and the description files
experimentDir = tempfile.mkdtemp()
try:
logger.info("Using experiment directory: {0!s}".format((experimentDir)))
# Create the decription.py from the overrides in params
paramsFilePath = os.path.join(experimentDir, 'description.py')
paramsFile = open(paramsFilePath, 'wb')
paramsFile.write(_paramsFileHead())
items = params.items()
items.sort()
for (key,value) in items:
quotedKey = _quoteAndEscape(key)
if isinstance(value, basestring):
paramsFile.write(" {0!s} : '{1!s}',\n".format(quotedKey , value))
else:
paramsFile.write(" {0!s} : {1!s},\n".format(quotedKey , value))
paramsFile.write(_paramsFileTail())
paramsFile.close()
# Write out the base description
baseParamsFile = open(os.path.join(experimentDir, 'base.py'), 'wb')
baseParamsFile.write(baseDescription)
baseParamsFile.close()
# Store the experiment's sub-description file into the model table
# for reference
fd = open(paramsFilePath)
expDescription = fd.read()
fd.close()
jobsDAO.modelSetFields(modelID, {'genDescription': expDescription})
# Run the experiment now
try:
runner = OPFModelRunner(
modelID=modelID,
jobID=jobID,
predictedField=predictedField,
experimentDir=experimentDir,
reportKeyPatterns=reportKeys,
optimizeKeyPattern=optimizeKey,
jobsDAO=jobsDAO,
modelCheckpointGUID=modelCheckpointGUID,
logLevel=logLevel,
predictionCacheMaxRecords=predictionCacheMaxRecords)
signal.signal(signal.SIGINT, runner.handleWarningSignal)
(completionReason, completionMsg) = runner.run()
except InvalidConnectionException:
raise
except Exception, e:
(completionReason, completionMsg) = _handleModelRunnerException(jobID,
modelID, jobsDAO, experimentDir, logger, e)
finally:
# delete our temporary directory tree
shutil.rmtree(experimentDir)
signal.signal(signal.SIGINT, signal.default_int_handler)
# Return completion reason and msg
return (completionReason, completionMsg)
def runModelGivenBaseAndParams(modelID,
jobID,
baseDescription,
params,
predictedField,
reportKeys,
optimizeKey,
jobsDAO,
modelCheckpointGUID,
logLevel=None,
predictionCacheMaxRecords=None):
""" This creates an experiment directory with a base.py description file
created from 'baseDescription' and a description.py generated from the
given params dict and then runs the experiment.
Parameters:
-------------------------------------------------------------------------
modelID: ID for this model in the models table
jobID: ID for this hypersearch job in the jobs table
baseDescription: Contents of a description.py with the base experiment
description
params: Dictionary of specific parameters to override within
the baseDescriptionFile.
predictedField: Name of the input field for which this model is being
optimized
reportKeys: Which metrics of the experiment to store into the
results dict of the model's database entry
optimizeKey: Which metric we are optimizing for
jobsDAO Jobs data access object - the interface to the
jobs database which has the model's table.
modelCheckpointGUID: A persistent, globally-unique identifier for
constructing the model checkpoint key
logLevel: override logging level to this value, if not None
retval: (completionReason, completionMsg)
"""
from nupic.swarming.ModelRunner import OPFModelRunner
# The logger for this method
logger = logging.getLogger('com.numenta.nupic.hypersearch.utils')
# --------------------------------------------------------------------------
# Create a temp directory for the experiment and the description files
experimentDir = tempfile.mkdtemp()
try:
logger.info("Using experiment directory: %s" % (experimentDir))
# Create the decription.py from the overrides in params
paramsFilePath = os.path.join(experimentDir, 'description.py')
paramsFile = open(paramsFilePath, 'wb')
paramsFile.write(_paramsFileHead())
items = params.items()
items.sort()
for (key, value) in items:
quotedKey = _quoteAndEscape(key)
if isinstance(value, basestring):
paramsFile.write(" %s : '%s',\n" % (quotedKey, value))
else:
paramsFile.write(" %s : %s,\n" % (quotedKey, value))
paramsFile.write(_paramsFileTail())
paramsFile.close()
# Write out the base description
baseParamsFile = open(os.path.join(experimentDir, 'base.py'), 'wb')
baseParamsFile.write(baseDescription)
baseParamsFile.close()
# Store the experiment's sub-description file into the model table
# for reference
fd = open(paramsFilePath)
expDescription = fd.read()
fd.close()
jobsDAO.modelSetFields(modelID, {'genDescription': expDescription})
# -------------------------------------------------------------------------
# if NTA_ROOTDIR is not set in the environment, set it for the user so that
# we can find data files expected to be in the share/prediction/data directory
if not "NTA_ROOTDIR" in os.environ:
os.environ['NTA_ROOTDIR'] = nupic.rootDir
# Run the experiment now
try:
runner = OPFModelRunner(
modelID=modelID,
jobID=jobID,
predictedField=predictedField,
experimentDir=experimentDir,
reportKeyPatterns=reportKeys,
optimizeKeyPattern=optimizeKey,
jobsDAO=jobsDAO,
modelCheckpointGUID=modelCheckpointGUID,
logLevel=logLevel,
predictionCacheMaxRecords=predictionCacheMaxRecords)
signal.signal(signal.SIGINT, runner.handleWarningSignal)
(completionReason, completionMsg) = runner.run()
except InvalidConnectionException:
raise
except Exception, e:
(completionReason, completionMsg) = _handleModelRunnerException(
jobID, modelID, jobsDAO, experimentDir, logger, e)
finally:
# delete our temporary directory tree
shutil.rmtree(experimentDir)
signal.signal(signal.SIGINT, signal.default_int_handler)
# Return completion reason and msg
return (completionReason, completionMsg)