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def next(self, record, curInputBookmark):
""" Return the next aggregated record, if any
Parameters:
------------------------------------------------------------------------
record: The input record (values only) from the input source, or
None if the input has reached EOF (this will cause this
method to force completion of and return any partially
aggregated time period)
curInputBookmark: The bookmark to the next input record
retval:
(outputRecord, inputBookmark)
outputRecord: the aggregated record
inputBookmark: a bookmark to the last position from the input that
contributed to this aggregated record.
If we don't have any aggregated records yet, returns (None, None)
The caller should generally do a loop like this:
while True:
inRecord = reader.getNextRecord()
bookmark = reader.getBookmark()
(aggRecord, aggBookmark) = aggregator.next(inRecord, bookmark)
# reached EOF?
if inRecord is None and aggRecord is None:
break
if aggRecord is not None:
proessRecord(aggRecord, aggBookmark)
This method makes use of the self._slice member variable to build up
the values we need to aggregate. This is a dict of lists. The keys are
the field indices and the elements of each list are the values for that
field. For example:
self._siice = { 0: [42, 53], 1: [4.0, 5.1] }
"""
# This will hold the aggregated record we return
outRecord = None
# This will hold the bookmark of the last input used within the
# aggregated record we return.
retInputBookmark = None
if record is not None:
# Increment input count
self._inIdx += 1
#print self._inIdx, record
# Apply the filter, ignore the record if any field is unacceptable
if self._filter != None and not self._filter[0](self._filter[1], record):
return (None, None)
# If no aggregation info just return as-is
if self._nullAggregation:
return (record, curInputBookmark)
# ----------------------------------------------------------------------
# Do aggregation
#
# Remember the very first record time stamp - it will be used as
# the timestamp for all first records in all sequences to align
# times for the aggregation/join of sequences.
#
# For a set of aggregated records, it will use the beginning of the time
# window as a timestamp for the set
#
t = record[self._timeFieldIdx]
if self._firstSequenceStartTime == None:
self._firstSequenceStartTime = t
# Create initial startTime and endTime if needed
if self._startTime is None:
self._startTime = t
if self._endTime is None:
self._endTime = self._getEndTime(t)
assert self._endTime > t
#print 'Processing line:', i, t, endTime
#from dbgp.client import brk; brk(port=9011)
# ----------------------------------------------------------------------
# Does this record have a reset signal or sequence Id associated with it?
# If so, see if we've reached a sequence boundary
if self._resetFieldIdx is not None:
resetSignal = record[self._resetFieldIdx]
else:
resetSignal = None
if self._sequenceIdFieldIdx is not None:
currSequenceId = record[self._sequenceIdFieldIdx]
else:
currSequenceId = None
newSequence = (resetSignal == 1 and self._inIdx > 0) \
or self._sequenceId != currSequenceId \
or self._inIdx == 0
if newSequence:
self._sequenceId = currSequenceId
# --------------------------------------------------------------------
# We end the aggregation chunk if we go past the end time
# -OR- we get an out of order record (t < startTime)
sliceEnded = (t >= self._endTime or t < self._startTime)
# -------------------------------------------------------------------
# Time to generate a new output record?
if (newSequence or sliceEnded) and len(self._slice) > 0:
# Create aggregated record
# print 'Creating aggregate record...'
# Make first record timestamp as the beginning of the time period,
# in case the first record wasn't falling on the beginning of the period
for j, f in enumerate(self._fields):
index = f[0]
if index == self._timeFieldIdx:
self._slice[j][0] = self._startTime
break
# Generate the aggregated record
outRecord = self._createAggregateRecord()
retInputBookmark = self._aggrInputBookmark
# Reset the slice
self._slice = defaultdict(list)
# --------------------------------------------------------------------
# Add current record to slice (Note keeping slices in memory). Each
# field in the slice is a list of field values from all the sliced
# records
for j, f in enumerate(self._fields):
index = f[0]
# append the parsed field value to the proper aggregated slice field.
self._slice[j].append(record[index])
self._aggrInputBookmark = curInputBookmark
# --------------------------------------------------------------------
# If we've encountered a new sequence, start aggregation over again
if newSequence:
# TODO: May use self._firstSequenceStartTime as a start for the new
# sequence (to align all sequences)
self._startTime = t
self._endTime = self._getEndTime(t)
# --------------------------------------------------------------------
# If a slice just ended, re-compute the start and end time for the
# next aggregated record
if sliceEnded:
# Did we receive an out of order record? If so, go back and iterate
# till we get to the next end time boundary.
if t < self._startTime:
self._endTime = self._firstSequenceStartTime
while t >= self._endTime:
self._startTime = self._endTime
self._endTime = self._getEndTime(self._endTime)
# If we have a record to return, do it now
if outRecord is not None:
return (outRecord, retInputBookmark)
# ---------------------------------------------------------------------
# Input reached EOF
# Aggregate one last time in the end if necessary
elif self._slice:
# Make first record timestamp as the beginning of the time period,
# in case the first record wasn't falling on the beginning of the period
for j, f in enumerate(self._fields):
index = f[0]
if index == self._timeFieldIdx:
self._slice[j][0] = self._startTime
break
outRecord = self._createAggregateRecord()
retInputBookmark = self._aggrInputBookmark
self._slice = defaultdict(list)
# Return aggregated record
return (outRecord, retInputBookmark) |
def processClubAttendance(f, clubs):
"""Process the attendance data of one club
If the club already exists in the list update its data.
If the club is new create a new Club object and add it to the dict
The next step is to iterate over all the lines and add a record for each line.
When reaching an empty line it means there are no more records for this club.
Along the way some redundant lines are skipped. When the file ends the f.next()
call raises a StopIteration exception and that's the sign to return False,
which indicates to the caller that there are no more clubs to process.
"""
try:
# Skip as many empty lines as necessary (file format inconsistent)
line = f.next()
while line == ',,,,,,,,,,,,,,,,,,,\n':
line = f.next()
# The first non-empty line should have the name as the first field
name = line.split(',')[0]
# Create a new club object if needed
if name not in clubs:
clubs[name] = Club(name)
# Get the named club
c = clubs[name]
c.processAttendance(f)
return True
except StopIteration:
return False |
def processClubConsumption(f, clubs):
"""Process the consumption a club
- Skip the header line
- Iterate over lines
- Read 4 records at a time
- Parse each line: club, date, time, consumption
- Get club object from dictionary if needed
- Aggregate consumption
- Call club.processConsumption() with data
"""
try:
# Skip header line
line = f.next()
assert line.endswith('" ","SITE_LOCATION_NAME","TIMESTAMP","TOTAL_KWH"\n')
valid_times = range(24)
t = 0 # used to track time
club = None
clubName = None
lastDate = None
while True:
assert t in valid_times
consumption = 0
for x in range(4):
# Read the line and get rid of the newline character
line = f.next()[:-1]
fields = line.split(',')
assert len(fields) == 4
for i, field in enumerate(fields):
# Strip the redundant double quotes
assert field[0] == '"' and field[-1] == '"'
fields[i] = field[1:-1]
# Ignoring field 0, which is just a running count
# Get the club name
name = fields[1]
# Hack to fix inconsistent club names like: "Melbourne CBD - Melbourne Central" vs. "Melbourne Central"
partialNames = ('Melbourne Central', 'North Sydney', 'Park St', 'Pitt St')
for pn in partialNames:
if pn in name:
name = pn
# Locate the club if needed (maybe )
if name != clubName:
clubName = name
club = clubs[name]
# Split the date (time is counted using the t variable)
tokens = fields[2].split()
# Verify that t == 0 and consumption == 0 when there is no time in the file
if len(tokens) == 1:
assert consumption == 0 and t == 0
# The first (and sometimes only) token is the date
date = tokens[0]
# Aggregate the consumption
consumption += float(fields[3])
# Update the Club object after aggregating the consumption of 4 lines
club.updateRecord(date, t, consumption)
# Increment time
t += 1
t %= 24
except StopIteration:
return |
def run(self, inputRecord):
"""
Run one iteration of this model.
:param inputRecord: (object)
A record object formatted according to
:meth:`~nupic.data.record_stream.RecordStreamIface.getNextRecord` or
:meth:`~nupic.data.record_stream.RecordStreamIface.getNextRecordDict`
result format.
:returns: (:class:`~nupic.frameworks.opf.opf_utils.ModelResult`)
An ModelResult namedtuple. The contents of ModelResult.inferences
depends on the the specific inference type of this model, which
can be queried by :meth:`.getInferenceType`.
"""
# 0-based prediction index for ModelResult
predictionNumber = self._numPredictions
self._numPredictions += 1
result = opf_utils.ModelResult(predictionNumber=predictionNumber,
rawInput=inputRecord)
return result |
def _getModelCheckpointFilePath(checkpointDir):
""" Return the absolute path of the model's checkpoint file.
:param checkpointDir: (string)
Directory of where the experiment is to be or was saved
:returns: (string) An absolute path.
"""
path = os.path.join(checkpointDir, "model.data")
path = os.path.abspath(path)
return path |
def writeToCheckpoint(self, checkpointDir):
"""Serializes model using capnproto and writes data to ``checkpointDir``"""
proto = self.getSchema().new_message()
self.write(proto)
checkpointPath = self._getModelCheckpointFilePath(checkpointDir)
# Clean up old saved state, if any
if os.path.exists(checkpointDir):
if not os.path.isdir(checkpointDir):
raise Exception(("Existing filesystem entry <%s> is not a model"
" checkpoint -- refusing to delete (not a directory)") \
% checkpointDir)
if not os.path.isfile(checkpointPath):
raise Exception(("Existing filesystem entry <%s> is not a model"
" checkpoint -- refusing to delete"\
" (%s missing or not a file)") % \
(checkpointDir, checkpointPath))
shutil.rmtree(checkpointDir)
# Create a new directory for saving state
self.__makeDirectoryFromAbsolutePath(checkpointDir)
with open(checkpointPath, 'wb') as f:
proto.write(f) |
def readFromCheckpoint(cls, checkpointDir):
"""Deserializes model from checkpointDir using capnproto"""
checkpointPath = cls._getModelCheckpointFilePath(checkpointDir)
with open(checkpointPath, 'r') as f:
proto = cls.getSchema().read(f,
traversal_limit_in_words=_TRAVERSAL_LIMIT_IN_WORDS)
model = cls.read(proto)
return model |
def writeBaseToProto(self, proto):
"""Save the state maintained by the Model base class
:param proto: capnp ModelProto message builder
"""
inferenceType = self.getInferenceType()
# lower-case first letter to be compatible with capnproto enum naming
inferenceType = inferenceType[:1].lower() + inferenceType[1:]
proto.inferenceType = inferenceType
proto.numPredictions = self._numPredictions
proto.learningEnabled = self.__learningEnabled
proto.inferenceEnabled = self.__inferenceEnabled
proto.inferenceArgs = json.dumps(self.__inferenceArgs) |
def save(self, saveModelDir):
""" Save the model in the given directory.
:param saveModelDir: (string)
Absolute directory path for saving the model. This directory should
only be used to store a saved model. If the directory does not exist,
it will be created automatically and populated with model data. A
pre-existing directory will only be accepted if it contains previously
saved model data. If such a directory is given, the full contents of
the directory will be deleted and replaced with current model data.
"""
logger = self._getLogger()
logger.debug("(%s) Creating local checkpoint in %r...",
self, saveModelDir)
modelPickleFilePath = self._getModelPickleFilePath(saveModelDir)
# Clean up old saved state, if any
if os.path.exists(saveModelDir):
if not os.path.isdir(saveModelDir):
raise Exception(("Existing filesystem entry <%s> is not a model"
" checkpoint -- refusing to delete (not a directory)") \
% saveModelDir)
if not os.path.isfile(modelPickleFilePath):
raise Exception(("Existing filesystem entry <%s> is not a model"
" checkpoint -- refusing to delete"\
" (%s missing or not a file)") % \
(saveModelDir, modelPickleFilePath))
shutil.rmtree(saveModelDir)
# Create a new directory for saving state
self.__makeDirectoryFromAbsolutePath(saveModelDir)
with open(modelPickleFilePath, 'wb') as modelPickleFile:
logger.debug("(%s) Pickling Model instance...", self)
pickle.dump(self, modelPickleFile, protocol=pickle.HIGHEST_PROTOCOL)
logger.debug("(%s) Finished pickling Model instance", self)
# Tell the model to save extra data, if any, that's too big for pickling
self._serializeExtraData(extraDataDir=self._getModelExtraDataDir(saveModelDir))
logger.debug("(%s) Finished creating local checkpoint", self)
return |
def load(cls, savedModelDir):
""" Load saved model.
:param savedModelDir: (string)
Directory of where the experiment is to be or was saved
:returns: (:class:`Model`) The loaded model instance
"""
logger = opf_utils.initLogger(cls)
logger.debug("Loading model from local checkpoint at %r...", savedModelDir)
# Load the model
modelPickleFilePath = Model._getModelPickleFilePath(savedModelDir)
with open(modelPickleFilePath, 'rb') as modelPickleFile:
logger.debug("Unpickling Model instance...")
model = pickle.load(modelPickleFile)
logger.debug("Finished unpickling Model instance")
# Tell the model to load extra data, if any, that was too big for pickling
model._deSerializeExtraData(
extraDataDir=Model._getModelExtraDataDir(savedModelDir))
logger.debug("Finished Loading model from local checkpoint")
return model |
def _getModelPickleFilePath(saveModelDir):
""" Return the absolute path of the model's pickle file.
:param saveModelDir: (string)
Directory of where the experiment is to be or was saved
:returns: (string) An absolute path.
"""
path = os.path.join(saveModelDir, "model.pkl")
path = os.path.abspath(path)
return path |
def _getModelExtraDataDir(saveModelDir):
""" Return the absolute path to the directory where the model's own
"extra data" are stored (i.e., data that's too big for pickling).
:param saveModelDir: (string)
Directory of where the experiment is to be or was saved
:returns: (string) An absolute path.
"""
path = os.path.join(saveModelDir, "modelextradata")
path = os.path.abspath(path)
return path |
def runExperiment(args, model=None):
"""
Run a single OPF experiment.
.. note:: The caller is responsible for initializing python logging before
calling this function (e.g., import :mod:`nupic.support`;
:meth:`nupic.support.initLogging`)
See also: :meth:`.initExperimentPrng`.
:param args: (string) Experiment command-line args list. Too see all options,
run with ``--help``:
.. code-block:: text
Options:
-h, --help show this help message and exit
-c <CHECKPOINT> Create a model and save it under the given <CHECKPOINT>
name, but don't run it
--listCheckpoints List all available checkpoints
--listTasks List all task labels in description.py
--load=<CHECKPOINT> Load a model from the given <CHECKPOINT> and run it.
Run with --listCheckpoints flag for more details.
--newSerialization Use new capnproto serialization
--tasks Run the tasks with the given TASK LABELS in the order
they are given. Either end of arg-list, or a
standalone dot ('.') arg or the next short or long
option name (-a or --blah) terminates the list. NOTE:
FAILS TO RECOGNIZE task label names with one or more
leading dashes. [default: run all of the tasks in
description.py]
--testMode Reduce iteration count for testing
--noCheckpoint Don't checkpoint the model after running each task.
:param model: (:class:`~nupic.frameworks.opf.model.Model`) For testing, may
pass in an existing OPF Model to use instead of creating a new one.
:returns: (:class:`~nupic.frameworks.opf.model.Model`)
reference to OPF Model instance that was constructed (this
is provided to aid with debugging) or None, if none was
created.
"""
# Parse command-line options
opt = _parseCommandLineOptions(args)
#print "runExperiment: Parsed Command Options: ", opt
model = _runExperimentImpl(opt, model)
return model |
def _parseCommandLineOptions(args):
"""Parse command line options
Args:
args: command line arguments (not including sys.argv[0])
Returns:
namedtuple ParseCommandLineOptionsResult
"""
usageStr = (
"%prog [options] descriptionPyDirectory\n"
"This script runs a single OPF Model described by description.py "
"located in the given directory."
)
parser = optparse.OptionParser(usage=usageStr)
parser.add_option("-c",
help="Create a model and save it under the given "
"<CHECKPOINT> name, but don't run it",
dest="createCheckpointName",
action="store", type="string", default="",
metavar="<CHECKPOINT>")
parser.add_option("--listCheckpoints",
help="List all available checkpoints",
dest="listAvailableCheckpoints",
action="store_true", default=False)
parser.add_option("--listTasks",
help="List all task labels in description.py",
dest="listTasks",
action="store_true", default=False)
parser.add_option("--load",
help="Load a model from the given <CHECKPOINT> and run it. "
"Run with --listCheckpoints flag for more details. ",
dest="runCheckpointName",
action="store", type="string", default="",
metavar="<CHECKPOINT>")
parser.add_option("--newSerialization",
help="Use new capnproto serialization",
dest="newSerialization",
action="store_true", default=False)
#parser.add_option("--reuseDatasets",
# help="Keep existing generated/aggregated datasets",
# dest="reuseDatasets", action="store_true",
# default=False)
parser.add_option("--tasks",
help="Run the tasks with the given TASK LABELS "
"in the order they are given. Either end of "
"arg-list, or a standalone dot ('.') arg or "
"the next short or long option name (-a or "
"--blah) terminates the list. NOTE: FAILS "
"TO RECOGNIZE task label names with one or more "
"leading dashes. [default: run all of the tasks in "
"description.py]",
dest="taskLabels", default=[],
action="callback", callback=reapVarArgsCallback,
metavar="TASK_LABELS")
parser.add_option("--testMode",
help="Reduce iteration count for testing",
dest="testMode", action="store_true",
default=False)
parser.add_option("--noCheckpoint",
help="Don't checkpoint the model after running each task.",
dest="checkpointModel", action="store_false",
default=True)
options, experiments = parser.parse_args(args)
# Validate args
mutuallyExclusiveOptionCount = sum([bool(options.createCheckpointName),
options.listAvailableCheckpoints,
options.listTasks,
bool(options.runCheckpointName)])
if mutuallyExclusiveOptionCount > 1:
_reportCommandLineUsageErrorAndExit(
parser,
"Options: -c, --listCheckpoints, --listTasks, and --load are "
"mutually exclusive. Please select only one")
mutuallyExclusiveOptionCount = sum([bool(not options.checkpointModel),
bool(options.createCheckpointName)])
if mutuallyExclusiveOptionCount > 1:
_reportCommandLineUsageErrorAndExit(
parser,
"Options: -c and --noCheckpoint are "
"mutually exclusive. Please select only one")
if len(experiments) != 1:
_reportCommandLineUsageErrorAndExit(
parser,
"Exactly ONE experiment must be specified, but got %s (%s)" % (
len(experiments), experiments))
# Done with parser
parser.destroy()
# Prepare results
# Directory path of the experiment (that contain description.py)
experimentDir = os.path.abspath(experiments[0])
# RunExperiment.py's private options (g_parsedPrivateCommandLineOptionsSchema)
privateOptions = dict()
privateOptions['createCheckpointName'] = options.createCheckpointName
privateOptions['listAvailableCheckpoints'] = options.listAvailableCheckpoints
privateOptions['listTasks'] = options.listTasks
privateOptions['runCheckpointName'] = options.runCheckpointName
privateOptions['newSerialization'] = options.newSerialization
privateOptions['testMode'] = options.testMode
#privateOptions['reuseDatasets'] = options.reuseDatasets
privateOptions['taskLabels'] = options.taskLabels
privateOptions['checkpointModel'] = options.checkpointModel
result = ParseCommandLineOptionsResult(experimentDir=experimentDir,
privateOptions=privateOptions)
return result |
def reapVarArgsCallback(option, optStr, value, parser):
"""Used as optparse callback for reaping a variable number of option args.
The option may be specified multiple times, and all the args associated with
that option name will be accumulated in the order that they are encountered
"""
newValues = []
# Reap the args, taking care to stop before the next option or '.'
gotDot = False
for arg in parser.rargs:
# Stop on --longname options
if arg.startswith("--") and len(arg) > 2:
break
# Stop on -b options
if arg.startswith("-") and len(arg) > 1:
break
if arg == ".":
gotDot = True
break
newValues.append(arg)
if not newValues:
raise optparse.OptionValueError(
("Empty arg list for option %r expecting one or more args "
"(remaining tokens: %r)") % (optStr, parser.rargs))
del parser.rargs[:len(newValues) + int(gotDot)]
# Retrieve the existing arg accumulator, if any
value = getattr(parser.values, option.dest, [])
#print "Previous value: %r" % value
if value is None:
value = []
# Append the new args to the existing ones and save to the parser
value.extend(newValues)
setattr(parser.values, option.dest, value) |
def _reportCommandLineUsageErrorAndExit(parser, message):
"""Report usage error and exit program with error indication."""
print parser.get_usage()
print message
sys.exit(1) |
def _runExperimentImpl(options, model=None):
"""Creates and runs the experiment
Args:
options: namedtuple ParseCommandLineOptionsResult
model: For testing: may pass in an existing OPF Model instance
to use instead of creating a new one.
Returns: reference to OPFExperiment instance that was constructed (this
is provided to aid with debugging) or None, if none was
created.
"""
json_helpers.validate(options.privateOptions,
schemaDict=g_parsedPrivateCommandLineOptionsSchema)
# Load the experiment's description.py module
experimentDir = options.experimentDir
descriptionPyModule = helpers.loadExperimentDescriptionScriptFromDir(
experimentDir)
expIface = helpers.getExperimentDescriptionInterfaceFromModule(
descriptionPyModule)
# Handle "list checkpoints" request
if options.privateOptions['listAvailableCheckpoints']:
_printAvailableCheckpoints(experimentDir)
return None
# Load experiment tasks
experimentTasks = expIface.getModelControl().get('tasks', [])
# If the tasks list is empty, and this is a nupic environment description
# file being run from the OPF, convert it to a simple OPF description file.
if (len(experimentTasks) == 0 and
expIface.getModelControl()['environment'] == OpfEnvironment.Nupic):
expIface.convertNupicEnvToOPF()
experimentTasks = expIface.getModelControl().get('tasks', [])
# Ensures all the source locations are either absolute paths or relative to
# the nupic.datafiles package_data location.
expIface.normalizeStreamSources()
# Extract option
newSerialization = options.privateOptions['newSerialization']
# Handle listTasks
if options.privateOptions['listTasks']:
print "Available tasks:"
for label in [t['taskLabel'] for t in experimentTasks]:
print "\t", label
return None
# Construct the experiment instance
if options.privateOptions['runCheckpointName']:
assert model is None
checkpointName = options.privateOptions['runCheckpointName']
model = ModelFactory.loadFromCheckpoint(
savedModelDir=_getModelCheckpointDir(experimentDir, checkpointName),
newSerialization=newSerialization)
elif model is not None:
print "Skipping creation of OPFExperiment instance: caller provided his own"
else:
modelDescription = expIface.getModelDescription()
model = ModelFactory.create(modelDescription)
# Handle "create model" request
if options.privateOptions['createCheckpointName']:
checkpointName = options.privateOptions['createCheckpointName']
_saveModel(model=model,
experimentDir=experimentDir,
checkpointLabel=checkpointName,
newSerialization=newSerialization)
return model
# Build the task list
# Default task execution index list is in the natural list order of the tasks
taskIndexList = range(len(experimentTasks))
customTaskExecutionLabelsList = options.privateOptions['taskLabels']
if customTaskExecutionLabelsList:
taskLabelsList = [t['taskLabel'] for t in experimentTasks]
taskLabelsSet = set(taskLabelsList)
customTaskExecutionLabelsSet = set(customTaskExecutionLabelsList)
assert customTaskExecutionLabelsSet.issubset(taskLabelsSet), \
("Some custom-provided task execution labels don't correspond "
"to actual task labels: mismatched labels: %r; actual task "
"labels: %r.") % (customTaskExecutionLabelsSet - taskLabelsSet,
customTaskExecutionLabelsList)
taskIndexList = [taskLabelsList.index(label) for label in
customTaskExecutionLabelsList]
print "#### Executing custom task list: %r" % [taskLabelsList[i] for
i in taskIndexList]
# Run all experiment tasks
for taskIndex in taskIndexList:
task = experimentTasks[taskIndex]
# Create a task runner and run it!
taskRunner = _TaskRunner(model=model,
task=task,
cmdOptions=options)
taskRunner.run()
del taskRunner
if options.privateOptions['checkpointModel']:
_saveModel(model=model,
experimentDir=experimentDir,
checkpointLabel=task['taskLabel'],
newSerialization=newSerialization)
return model |
def _saveModel(model, experimentDir, checkpointLabel, newSerialization=False):
"""Save model"""
checkpointDir = _getModelCheckpointDir(experimentDir, checkpointLabel)
if newSerialization:
model.writeToCheckpoint(checkpointDir)
else:
model.save(saveModelDir=checkpointDir) |
def _getModelCheckpointDir(experimentDir, checkpointLabel):
"""Creates directory for serialization of the model
checkpointLabel:
Checkpoint label (string)
Returns:
absolute path to the serialization directory
"""
checkpointDir = os.path.join(getCheckpointParentDir(experimentDir),
checkpointLabel + g_defaultCheckpointExtension)
checkpointDir = os.path.abspath(checkpointDir)
return checkpointDir |
def getCheckpointParentDir(experimentDir):
"""Get checkpoint parent dir.
Returns: absolute path to the base serialization directory within which
model checkpoints for this experiment are created
"""
baseDir = os.path.join(experimentDir, "savedmodels")
baseDir = os.path.abspath(baseDir)
return baseDir |
def _checkpointLabelFromCheckpointDir(checkpointDir):
"""Returns a checkpoint label string for the given model checkpoint directory
checkpointDir: relative or absolute model checkpoint directory path
"""
assert checkpointDir.endswith(g_defaultCheckpointExtension)
lastSegment = os.path.split(checkpointDir)[1]
checkpointLabel = lastSegment[0:-len(g_defaultCheckpointExtension)]
return checkpointLabel |
def _isCheckpointDir(checkpointDir):
"""Return true iff checkpointDir appears to be a checkpoint directory."""
lastSegment = os.path.split(checkpointDir)[1]
if lastSegment[0] == '.':
return False
if not checkpointDir.endswith(g_defaultCheckpointExtension):
return False
if not os.path.isdir(checkpointDir):
return False
return True |
def _printAvailableCheckpoints(experimentDir):
"""List available checkpoints for the specified experiment."""
checkpointParentDir = getCheckpointParentDir(experimentDir)
if not os.path.exists(checkpointParentDir):
print "No available checkpoints."
return
checkpointDirs = [x for x in os.listdir(checkpointParentDir)
if _isCheckpointDir(os.path.join(checkpointParentDir, x))]
if not checkpointDirs:
print "No available checkpoints."
return
print "Available checkpoints:"
checkpointList = [_checkpointLabelFromCheckpointDir(x)
for x in checkpointDirs]
for checkpoint in sorted(checkpointList):
print "\t", checkpoint
print
print "To start from a checkpoint:"
print " python run_opf_experiment.py experiment --load <CHECKPOINT>"
print "For example, to start from the checkpoint \"MyCheckpoint\":"
print " python run_opf_experiment.py experiment --load MyCheckpoint" |
def run(self):
"""Runs a single experiment task"""
self.__logger.debug("run(): Starting task <%s>", self.__task['taskLabel'])
# Set up the task
# Create our main loop-control iterator
if self.__cmdOptions.privateOptions['testMode']:
numIters = 10
else:
numIters = self.__task['iterationCount']
if numIters >= 0:
iterTracker = iter(xrange(numIters))
else:
iterTracker = iter(itertools.count())
# Initialize periodic activities
periodic = PeriodicActivityMgr(
requestedActivities=self._createPeriodicActivities())
# Reset sequence states in the model, so it starts looking for a new
# sequence
# TODO: should this be done in OPFTaskDriver.setup(), instead? Is it always
# desired in Nupic?
self.__model.resetSequenceStates()
# Have Task Driver perform its initial setup activities, including setup
# callbacks
self.__taskDriver.setup()
# Run it!
while True:
# Check controlling iterator first
try:
next(iterTracker)
except StopIteration:
break
# Read next input record
try:
inputRecord = self.__datasetReader.next()
except StopIteration:
break
# Process input record
result = self.__taskDriver.handleInputRecord(inputRecord=inputRecord)
if InferenceElement.encodings in result.inferences:
result.inferences.pop(InferenceElement.encodings)
self.__predictionLogger.writeRecord(result)
# Run periodic activities
periodic.tick()
# Dump the experiment metrics at the end of the task
self._getAndEmitExperimentMetrics(final=True)
# Have Task Driver perform its final activities
self.__taskDriver.finalize()
# Reset sequence states in the model, so it starts looking for a new
# sequence
# TODO: should this be done in OPFTaskDriver.setup(), instead? Is it always
# desired in Nupic?
self.__model.resetSequenceStates() |
def _createPeriodicActivities(self):
"""Creates and returns a list of activites for this TaskRunner instance
Returns: a list of PeriodicActivityRequest elements
"""
# Initialize periodic activities
periodicActivities = []
# Metrics reporting
class MetricsReportCb(object):
def __init__(self, taskRunner):
self.__taskRunner = taskRunner
return
def __call__(self):
self.__taskRunner._getAndEmitExperimentMetrics()
reportMetrics = PeriodicActivityRequest(
repeating=True,
period=1000,
cb=MetricsReportCb(self))
periodicActivities.append(reportMetrics)
# Iteration progress
class IterationProgressCb(object):
PROGRESS_UPDATE_PERIOD_TICKS = 1000
def __init__(self, taskLabel, requestedIterationCount, logger):
self.__taskLabel = taskLabel
self.__requestedIterationCount = requestedIterationCount
self.__logger = logger
self.__numIterationsSoFar = 0
def __call__(self):
self.__numIterationsSoFar += self.PROGRESS_UPDATE_PERIOD_TICKS
self.__logger.debug("%s: ITERATION PROGRESS: %s of %s" % (
self.__taskLabel,
self.__numIterationsSoFar,
self.__requestedIterationCount))
iterationProgressCb = IterationProgressCb(
taskLabel=self.__task['taskLabel'],
requestedIterationCount=self.__task['iterationCount'],
logger=self.__logger)
iterationProgressReporter = PeriodicActivityRequest(
repeating=True,
period=IterationProgressCb.PROGRESS_UPDATE_PERIOD_TICKS,
cb=iterationProgressCb)
periodicActivities.append(iterationProgressReporter)
return periodicActivities |
def _generateFile(filename, data):
"""
Parameters:
----------------------------------------------------------------
filename: name of .csv file to generate
"""
# Create the file
print "Creating %s..." % (filename)
numRecords, numFields = data.shape
fields = [('field%d'%(i+1), 'float', '') for i in range(numFields)]
outFile = File(filename, fields)
for i in xrange(numRecords):
outFile.write(data[i].tolist())
outFile.close() |
def corruptVector(v1, noiseLevel, numActiveCols):
"""
Corrupts a copy of a binary vector by inverting noiseLevel percent of its bits.
@param v1 (array) binary vector whose copy will be corrupted
@param noiseLevel (float) amount of noise to be applied on the new vector
@param numActiveCols (int) number of sparse columns that represent an input
@return v2 (array) corrupted binary vector
"""
size = len(v1)
v2 = np.zeros(size, dtype="uint32")
bitsToSwap = int(noiseLevel * numActiveCols)
# Copy the contents of v1 into v2
for i in range(size):
v2[i] = v1[i]
for _ in range(bitsToSwap):
i = random.randrange(size)
if v2[i] == 1:
v2[i] = 0
else:
v2[i] = 1
return v2 |
def showPredictions():
"""
Shows predictions of the TM when presented with the characters A, B, C, D, X, and
Y without any contextual information, that is, not embedded within a sequence.
"""
for k in range(6):
tm.reset()
print "--- " + "ABCDXY"[k] + " ---"
tm.compute(set(seqT[k][:].nonzero()[0].tolist()), learn=False)
activeColumnsIndices = [tm.columnForCell(i) for i in tm.getActiveCells()]
predictedColumnIndices = [tm.columnForCell(i) for i in tm.getPredictiveCells()]
currentColumns = [1 if i in activeColumnsIndices else 0 for i in range(tm.numberOfColumns())]
predictedColumns = [1 if i in predictedColumnIndices else 0 for i in range(tm.numberOfColumns())]
print("Active cols: " + str(np.nonzero(currentColumns)[0]))
print("Predicted cols: " + str(np.nonzero(predictedColumns)[0]))
print "" |
def trainTM(sequence, timeSteps, noiseLevel):
"""
Trains the TM with given sequence for a given number of time steps and level of input
corruption
@param sequence (array) array whose rows are the input characters
@param timeSteps (int) number of time steps in which the TM will be presented with sequence
@param noiseLevel (float) amount of noise to be applied on the characters in the sequence
"""
currentColumns = np.zeros(tm.numberOfColumns(), dtype="uint32")
predictedColumns = np.zeros(tm.numberOfColumns(), dtype="uint32")
ts = 0
for t in range(timeSteps):
tm.reset()
for k in range(4):
v = corruptVector(sequence[k][:], noiseLevel, sparseCols)
tm.compute(set(v[:].nonzero()[0].tolist()), learn=True)
activeColumnsIndices = [tm.columnForCell(i) for i in tm.getActiveCells()]
predictedColumnIndices = [tm.columnForCell(i) for i in tm.getPredictiveCells()]
currentColumns = [1 if i in activeColumnsIndices else 0 for i in range(tm.numberOfColumns())]
acc = accuracy(currentColumns, predictedColumns)
x.append(ts)
y.append(acc)
ts += 1
predictedColumns = [1 if i in predictedColumnIndices else 0 for i in range(tm.numberOfColumns())] |
def encodeIntoArray(self, inputVal, outputVal):
"""See method description in base.py"""
if len(inputVal) != len(outputVal):
raise ValueError("Different input (%i) and output (%i) sizes." % (
len(inputVal), len(outputVal)))
if self.w is not None and sum(inputVal) != self.w:
raise ValueError("Input has %i bits but w was set to %i." % (
sum(inputVal), self.w))
outputVal[:] = inputVal[:]
if self.verbosity >= 2:
print "input:", inputVal, "output:", outputVal
print "decoded:", self.decodedToStr(self.decode(outputVal)) |
def decode(self, encoded, parentFieldName=""):
"""See the function description in base.py"""
if parentFieldName != "":
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: ([[0, 0]], "input")}, [fieldName]) |
def getBucketInfo(self, buckets):
"""See the function description in base.py"""
return [EncoderResult(value=0, scalar=0, encoding=numpy.zeros(self.n))] |
def topDownCompute(self, encoded):
"""See the function description in base.py"""
return EncoderResult(value=0, scalar=0,
encoding=numpy.zeros(self.n)) |
def closenessScores(self, expValues, actValues, **kwargs):
"""
Does a bitwise compare of the two bitmaps and returns a fractonal
value between 0 and 1 of how similar they are.
- ``1`` => identical
- ``0`` => no overlaping bits
``kwargs`` will have the keyword "fractional", which is assumed by this
encoder.
"""
ratio = 1.0
esum = int(expValues.sum())
asum = int(actValues.sum())
if asum > esum:
diff = asum - esum
if diff < esum:
ratio = 1 - diff/float(esum)
else:
ratio = 1/float(diff)
olap = expValues & actValues
osum = int(olap.sum())
if esum == 0:
r = 0.0
else:
r = osum/float(esum)
r = r * ratio
return numpy.array([r]) |
def getCallerInfo(depth=2):
"""Utility function to get information about function callers
The information is the tuple (function/method name, filename, class)
The class will be None if the caller is just a function and not an object
method.
:param depth: (int) how far back in the callstack to go to extract the caller
info
"""
f = sys._getframe(depth)
method_name = f.f_code.co_name
filename = f.f_code.co_filename
arg_class = None
args = inspect.getargvalues(f)
if len(args[0]) > 0:
arg_name = args[0][0] # potentially the 'self' arg if its a method
arg_class = args[3][arg_name].__class__.__name__
return (method_name, filename, arg_class) |
def title(s=None, additional='', stream=sys.stdout):
"""Utility function to display nice titles
It automatically extracts the name of the function/method it is called from
and you can add additional text. title() will then print the name
of the function/method and the additional text surrounded by tow lines
of dashes. If you don't want the name of the function, you can provide
alternative text (regardless of the additional text)
:param s: (string) text to display, uses the function name and arguments by
default
:param additional: (string) extra text to display (not needed if s is not
None)
:param stream: (stream) the stream to print to. Ny default goes to standard
output
Examples:
.. code-block:: python
def foo():
title()
will display:
.. code-block:: text
---
foo
---
.. code-block:: python
def foo():
title(additional='(), this is cool!!!')
will display:
.. code-block:: text
----------------------
foo(), this is cool!!!
----------------------
.. code-block:: python
def foo():
title('No function name here!')
will display:
.. code-block:: text
----------------------
No function name here!
----------------------
"""
if s is None:
callable_name, file_name, class_name = getCallerInfo(2)
s = callable_name
if class_name is not None:
s = class_name + '.' + callable_name
lines = (s + additional).split('\n')
length = max(len(line) for line in lines)
print >> stream, '-' * length
print >> stream, s + additional
print >> stream, '-' * length |
def getArgumentDescriptions(f):
"""
Get the arguments, default values, and argument descriptions for a function.
Parses the argument descriptions out of the function docstring, using a
format something lke this:
::
[junk]
argument_name: description...
description...
description...
[junk]
[more arguments]
It will find an argument as long as the exact argument name starts the line.
It will then strip a trailing colon, if present, then strip the rest of the
line and use it to start the description. It will then strip and append any
subsequent lines with a greater indent level than the original argument name.
:param f: (function) to inspect
:returns: (list of tuples) (``argName``, ``argDescription``, ``defaultValue``)
If an argument has no default value, the tuple is only two elements long (as
``None`` cannot be used, since it could be a default value itself).
"""
# Get the argument names and default values
argspec = inspect.getargspec(f)
# Scan through the docstring to extract documentation for each argument as
# follows:
# Check the first word of the line, stripping a colon if one is present.
# If it matches an argument name:
# Take the rest of the line, stripping leading whitespeace
# Take each subsequent line if its indentation level is greater than the
# initial indentation level
# Once the indentation level is back to the original level, look for
# another argument
docstring = f.__doc__
descriptions = {}
if docstring:
lines = docstring.split('\n')
i = 0
while i < len(lines):
stripped = lines[i].lstrip()
if not stripped:
i += 1
continue
# Indentation level is index of the first character
indentLevel = lines[i].index(stripped[0])
# Get the first word and remove the colon, if present
firstWord = stripped.split()[0]
if firstWord.endswith(':'):
firstWord = firstWord[:-1]
if firstWord in argspec.args:
# Found an argument
argName = firstWord
restOfLine = stripped[len(firstWord)+1:].strip()
argLines = [restOfLine]
# Take the next lines as long as they are indented more
i += 1
while i < len(lines):
stripped = lines[i].lstrip()
if not stripped:
# Empty line - stop
break
if lines[i].index(stripped[0]) <= indentLevel:
# No longer indented far enough - stop
break
# This line counts too
argLines.append(lines[i].strip())
i += 1
# Store this description
descriptions[argName] = ' '.join(argLines)
else:
# Not an argument
i += 1
# Build the list of (argName, description, defaultValue)
args = []
if argspec.defaults:
defaultCount = len(argspec.defaults)
else:
defaultCount = 0
nonDefaultArgCount = len(argspec.args) - defaultCount
for i, argName in enumerate(argspec.args):
if i >= nonDefaultArgCount:
defaultValue = argspec.defaults[i - nonDefaultArgCount]
args.append((argName, descriptions.get(argName, ""), defaultValue))
else:
args.append((argName, descriptions.get(argName, "")))
return args |
def initLogging(verbose=False, console='stdout', consoleLevel='DEBUG'):
"""
Initilize NuPic logging by reading in from the logging configuration file. The
logging configuration file is named ``nupic-logging.conf`` and is expected to
be in the format defined by the python logging module.
If the environment variable ``NTA_CONF_PATH`` is defined, then the logging
configuration file is expected to be in the ``NTA_CONF_PATH`` directory. If
``NTA_CONF_PATH`` is not defined, then it is found in the 'conf/default'
subdirectory of the NuPic installation directory (typically
~/nupic/current/conf/default)
The logging configuration file can use the environment variable
``NTA_LOG_DIR`` to set the locations of log files. If this variable is not
defined, logging to files will be disabled.
:param console: Defines console output for the default "root" logging
configuration; this may be one of 'stdout', 'stderr', or None;
Use None to suppress console logging output
:param consoleLevel:
Logging-level filter string for console output corresponding to
logging levels in the logging module; may be one of:
'DEBUG', 'INFO', 'WARNING', 'ERROR', or 'CRITICAL'.
E.g., a value of'WARNING' suppresses DEBUG and INFO level output
to console, but allows WARNING, ERROR, and CRITICAL
"""
# NOTE: If you call this twice from the same process there seems to be a
# bug - logged messages don't show up for loggers that you do another
# logging.getLogger() on.
global gLoggingInitialized
if gLoggingInitialized:
if verbose:
print >> sys.stderr, "Logging already initialized, doing nothing."
return
consoleStreamMappings = {
'stdout' : 'stdoutConsoleHandler',
'stderr' : 'stderrConsoleHandler',
}
consoleLogLevels = ['DEBUG', 'INFO', 'WARNING', 'WARN', 'ERROR', 'CRITICAL',
'FATAL']
assert console is None or console in consoleStreamMappings.keys(), (
'Unexpected console arg value: %r') % (console,)
assert consoleLevel in consoleLogLevels, (
'Unexpected consoleLevel arg value: %r') % (consoleLevel)
# -----------------------------------------------------------------------
# Setup logging. Look for the nupic-logging.conf file, first in the
# NTA_CONFIG_DIR path (if defined), then in a subdirectory of the nupic
# module
configFilename = 'nupic-logging.conf'
configFilePath = resource_filename("nupic.support", configFilename)
configLogDir = os.environ.get('NTA_LOG_DIR', None)
# Load in the logging configuration file
if verbose:
print >> sys.stderr, (
"Using logging configuration file: %s") % (configFilePath)
# This dict will hold our replacement strings for logging configuration
replacements = dict()
def makeKey(name):
""" Makes replacement key """
return "$$%s$$" % (name)
platform = sys.platform.lower()
if platform.startswith('java'):
# Jython
import java.lang
platform = java.lang.System.getProperty("os.name").lower()
if platform.startswith('mac os x'):
platform = 'darwin'
if platform.startswith('darwin'):
replacements[makeKey('SYSLOG_HANDLER_ADDRESS')] = '"/var/run/syslog"'
elif platform.startswith('linux'):
replacements[makeKey('SYSLOG_HANDLER_ADDRESS')] = '"/dev/log"'
elif platform.startswith('win'):
replacements[makeKey('SYSLOG_HANDLER_ADDRESS')] = '"log"'
else:
raise RuntimeError("This platform is neither darwin, win32, nor linux: %s" % (
sys.platform,))
# Nupic logs go to file
replacements[makeKey('PERSISTENT_LOG_HANDLER')] = 'fileHandler'
if platform.startswith('win'):
replacements[makeKey('FILE_HANDLER_LOG_FILENAME')] = '"NUL"'
else:
replacements[makeKey('FILE_HANDLER_LOG_FILENAME')] = '"/dev/null"'
# Set up log file path for the default file handler and configure handlers
handlers = list()
if configLogDir is not None:
logFilePath = _genLoggingFilePath()
makeDirectoryFromAbsolutePath(os.path.dirname(logFilePath))
replacements[makeKey('FILE_HANDLER_LOG_FILENAME')] = repr(logFilePath)
handlers.append(replacements[makeKey('PERSISTENT_LOG_HANDLER')])
if console is not None:
handlers.append(consoleStreamMappings[console])
replacements[makeKey('ROOT_LOGGER_HANDLERS')] = ", ".join(handlers)
# Set up log level for console handlers
replacements[makeKey('CONSOLE_LOG_LEVEL')] = consoleLevel
customConfig = StringIO()
# Using pkg_resources to get the logging file, which should be packaged and
# associated with this source file name.
loggingFileContents = resource_string(__name__, configFilename)
for lineNum, line in enumerate(loggingFileContents.splitlines()):
if "$$" in line:
for (key, value) in replacements.items():
line = line.replace(key, value)
# If there is still a replacement string in the line, we're missing it
# from our replacements dict
if "$$" in line and "$$<key>$$" not in line:
raise RuntimeError(("The text %r, found at line #%d of file %r, "
"contains a string not found in our replacement "
"dict.") % (line, lineNum, configFilePath))
customConfig.write("%s\n" % line)
customConfig.seek(0)
if python_version()[:3] >= '2.6':
logging.config.fileConfig(customConfig, disable_existing_loggers=False)
else:
logging.config.fileConfig(customConfig)
gLoggingInitialized = True |
def _genLoggingFilePath():
""" Generate a filepath for the calling app """
appName = os.path.splitext(os.path.basename(sys.argv[0]))[0] or 'UnknownApp'
appLogDir = os.path.abspath(os.path.join(
os.environ['NTA_LOG_DIR'],
'numenta-logs-%s' % (os.environ['USER'],),
appName))
appLogFileName = '%s-%s-%s.log' % (
appName, long(time.mktime(time.gmtime())), os.getpid())
return os.path.join(appLogDir, appLogFileName) |
def aggregationToMonthsSeconds(interval):
"""
Return the number of months and seconds from an aggregation dict that
represents a date and time.
Interval is a dict that contain one or more of the following keys: 'years',
'months', 'weeks', 'days', 'hours', 'minutes', seconds', 'milliseconds',
'microseconds'.
For example:
::
aggregationMicroseconds({'years': 1, 'hours': 4, 'microseconds':42}) ==
{'months':12, 'seconds':14400.000042}
:param interval: (dict) The aggregation interval representing a date and time
:returns: (dict) number of months and seconds in the interval:
``{months': XX, 'seconds': XX}``. The seconds is
a floating point that can represent resolutions down to a
microsecond.
"""
seconds = interval.get('microseconds', 0) * 0.000001
seconds += interval.get('milliseconds', 0) * 0.001
seconds += interval.get('seconds', 0)
seconds += interval.get('minutes', 0) * 60
seconds += interval.get('hours', 0) * 60 * 60
seconds += interval.get('days', 0) * 24 * 60 * 60
seconds += interval.get('weeks', 0) * 7 * 24 * 60 * 60
months = interval.get('months', 0)
months += 12 * interval.get('years', 0)
return {'months': months, 'seconds': seconds} |
def aggregationDivide(dividend, divisor):
"""
Return the result from dividing two dicts that represent date and time.
Both dividend and divisor are dicts that contain one or more of the following
keys: 'years', 'months', 'weeks', 'days', 'hours', 'minutes', seconds',
'milliseconds', 'microseconds'.
For example:
::
aggregationDivide({'hours': 4}, {'minutes': 15}) == 16
:param dividend: (dict) The numerator, as a dict representing a date and time
:param divisor: (dict) the denominator, as a dict representing a date and time
:returns: (float) number of times divisor goes into dividend
"""
# Convert each into microseconds
dividendMonthSec = aggregationToMonthsSeconds(dividend)
divisorMonthSec = aggregationToMonthsSeconds(divisor)
# It is a usage error to mix both months and seconds in the same operation
if (dividendMonthSec['months'] != 0 and divisorMonthSec['seconds'] != 0) \
or (dividendMonthSec['seconds'] != 0 and divisorMonthSec['months'] != 0):
raise RuntimeError("Aggregation dicts with months/years can only be "
"inter-operated with other aggregation dicts that contain "
"months/years")
if dividendMonthSec['months'] > 0:
return float(dividendMonthSec['months']) / divisor['months']
else:
return float(dividendMonthSec['seconds']) / divisorMonthSec['seconds'] |
def validateOpfJsonValue(value, opfJsonSchemaFilename):
"""
Validate a python object against an OPF json schema file
:param value: target python object to validate (typically a dictionary)
:param opfJsonSchemaFilename: (string) OPF json schema filename containing the
json schema object. (e.g., opfTaskControlSchema.json)
:raises: jsonhelpers.ValidationError when value fails json validation
"""
# Create a path by joining the filename with our local json schema root
jsonSchemaPath = os.path.join(os.path.dirname(__file__),
"jsonschema",
opfJsonSchemaFilename)
# Validate
jsonhelpers.validate(value, schemaPath=jsonSchemaPath)
return |
def initLogger(obj):
"""
Helper function to create a logger object for the current object with
the standard Numenta prefix.
:param obj: (object) to add a logger to
"""
if inspect.isclass(obj):
myClass = obj
else:
myClass = obj.__class__
logger = logging.getLogger(".".join(
['com.numenta', myClass.__module__, myClass.__name__]))
return logger |
def matchPatterns(patterns, keys):
"""
Returns a subset of the keys that match any of the given patterns
:param patterns: (list) regular expressions to match
:param keys: (list) keys to search for matches
"""
results = []
if patterns:
for pattern in patterns:
prog = re.compile(pattern)
for key in keys:
if prog.match(key):
results.append(key)
else:
return None
return results |
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal |
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal) |
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output)) |
def decode(self, encoded, parentFieldName=''):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV),
math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges-1:
desc += ", "
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName]) |
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues |
def getBucketInfo(self, buckets):
"""
See the function description in base.py
"""
scaledResult = self.encoder.getBucketInfo(buckets)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return [EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding)] |
def topDownCompute(self, encoded):
"""
See the function description in base.py
"""
scaledResult = self.encoder.topDownCompute(encoded)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding) |
def closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues [0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
#print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
#import pdb; pdb.set_trace()
return numpy.array([closeness]) |
def export(self):
"""
Exports a network as a networkx MultiDiGraph intermediate representation
suitable for visualization.
:return: networkx MultiDiGraph
"""
graph = nx.MultiDiGraph()
# Add regions to graph as nodes, annotated by name
regions = self.network.getRegions()
for idx in xrange(regions.getCount()):
regionPair = regions.getByIndex(idx)
regionName = regionPair[0]
graph.add_node(regionName, label=regionName)
# Add links between regions to graph as edges, annotate by input-output
# name pairs
for linkName, link in self.network.getLinks():
graph.add_edge(link.getSrcRegionName(),
link.getDestRegionName(),
src=link.getSrcOutputName(),
dest=link.getDestInputName())
return graph |
def bitsToString(arr):
"""Returns a string representing a numpy array of 0's and 1's"""
s = array('c','.'*len(arr))
for i in xrange(len(arr)):
if arr[i] == 1:
s[i]='*'
return s |
def percentOverlap(x1, x2, size):
"""
Computes the percentage of overlap between vectors x1 and x2.
@param x1 (array) binary vector
@param x2 (array) binary vector
@param size (int) length of binary vectors
@return percentOverlap (float) percentage overlap between x1 and x2
"""
nonZeroX1 = np.count_nonzero(x1)
nonZeroX2 = np.count_nonzero(x2)
minX1X2 = min(nonZeroX1, nonZeroX2)
percentOverlap = 0
if minX1X2 > 0:
percentOverlap = float(np.dot(x1, x2))/float(minX1X2)
return percentOverlap |
def resetVector(x1, x2):
"""
Copies the contents of vector x1 into vector x2.
@param x1 (array) binary vector to be copied
@param x2 (array) binary vector where x1 is copied
"""
size = len(x1)
for i in range(size):
x2[i] = x1[i] |
def runCPU():
"""Poll CPU usage, make predictions, and plot the results. Runs forever."""
# Create the model for predicting CPU usage.
model = ModelFactory.create(model_params.MODEL_PARAMS)
model.enableInference({'predictedField': 'cpu'})
# The shifter will align prediction and actual values.
shifter = InferenceShifter()
# Keep the last WINDOW predicted and actual values for plotting.
actHistory = deque([0.0] * WINDOW, maxlen=60)
predHistory = deque([0.0] * WINDOW, maxlen=60)
# Initialize the plot lines that we will update with each new record.
actline, = plt.plot(range(WINDOW), actHistory)
predline, = plt.plot(range(WINDOW), predHistory)
# Set the y-axis range.
actline.axes.set_ylim(0, 100)
predline.axes.set_ylim(0, 100)
while True:
s = time.time()
# Get the CPU usage.
cpu = psutil.cpu_percent()
# Run the input through the model and shift the resulting prediction.
modelInput = {'cpu': cpu}
result = shifter.shift(model.run(modelInput))
# Update the trailing predicted and actual value deques.
inference = result.inferences['multiStepBestPredictions'][5]
if inference is not None:
actHistory.append(result.rawInput['cpu'])
predHistory.append(inference)
# Redraw the chart with the new data.
actline.set_ydata(actHistory) # update the data
predline.set_ydata(predHistory) # update the data
plt.draw()
plt.legend( ('actual','predicted') )
# Make sure we wait a total of 2 seconds per iteration.
try:
plt.pause(SECONDS_PER_STEP)
except:
pass |
def _extractCallingMethodArgs():
"""
Returns args dictionary from the calling method
"""
import inspect
import copy
callingFrame = inspect.stack()[1][0]
argNames, _, _, frameLocalVarDict = inspect.getargvalues(callingFrame)
argNames.remove("self")
args = copy.copy(frameLocalVarDict)
for varName in frameLocalVarDict:
if varName not in argNames:
args.pop(varName)
return args |
def write(self, proto):
"""Populate serialization proto instance.
:param proto: (BacktrackingTMCppProto) the proto instance to populate
"""
# Write base class to proto.baseTM (BacktrackingTMProto)
super(BacktrackingTMCPP, self).write(proto.baseTM)
self.cells4.write(proto.cells4)
proto.makeCells4Ephemeral = self.makeCells4Ephemeral
proto.seed = self.seed
proto.checkSynapseConsistency = self.checkSynapseConsistency
proto.initArgs = json.dumps(self._initArgsDict) |
def read(cls, proto):
"""Deserialize from proto instance.
:param proto: (BacktrackingTMCppProto) the proto instance to read from
"""
# Use base class to create initial class from proto.baseTM
# (BacktrackingTMProto)
obj = BacktrackingTM.read(proto.baseTM)
obj.__class__ = cls
# Additional CPP-specific deserialization
newCells4 = Cells4.read(proto.cells4)
print newCells4
obj.cells4 = newCells4
obj.makeCells4Ephemeral = proto.makeCells4Ephemeral
obj.seed = proto.seed
obj.checkSynapseConsistency = proto.checkSynapseConsistency
obj._initArgsDict = json.loads(proto.initArgs)
# Convert unicode to str
obj._initArgsDict["outputType"] = str(obj._initArgsDict["outputType"])
# Initialize ephemeral attributes
obj.allocateStatesInCPP = False
obj.retrieveLearningStates = False
obj._setStatePointers()
return obj |
def _getEphemeralMembers(self):
"""
List of our member variables that we don't need to be saved
"""
e = BacktrackingTM._getEphemeralMembers(self)
if self.makeCells4Ephemeral:
e.extend(['cells4'])
return e |
def _initEphemerals(self):
"""
Initialize all ephemeral members after being restored to a pickled state.
"""
BacktrackingTM._initEphemerals(self)
#---------------------------------------------------------------------------------
# cells4 specific initialization
# If True, let C++ allocate memory for activeState, predictedState, and
# learnState. In this case we can retrieve copies of these states but can't
# set them directly from Python. If False, Python can allocate them as
# numpy arrays and we can pass pointers to the C++ using setStatePointers
self.allocateStatesInCPP = False
# Set this to true for debugging or accessing learning states
self.retrieveLearningStates = False
if self.makeCells4Ephemeral:
self._initCells4() |
def compute(self, bottomUpInput, enableLearn, enableInference=None):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.compute`.
"""
# The C++ TM takes 32 bit floats as input. uint32 works as well since the
# code only checks whether elements are non-zero
assert (bottomUpInput.dtype == numpy.dtype('float32')) or \
(bottomUpInput.dtype == numpy.dtype('uint32')) or \
(bottomUpInput.dtype == numpy.dtype('int32'))
self.iterationIdx = self.iterationIdx + 1
# As a speed optimization for now (until we need online learning), skip
# computing the inference output while learning
if enableInference is None:
if enableLearn:
enableInference = False
else:
enableInference = True
# ====================================================================
# Run compute and retrieve selected state and member variables
self._setStatePointers()
y = self.cells4.compute(bottomUpInput, enableInference, enableLearn)
self.currentOutput = y.reshape((self.numberOfCols, self.cellsPerColumn))
self.avgLearnedSeqLength = self.cells4.getAvgLearnedSeqLength()
self._copyAllocatedStates()
# ========================================================================
# Update the prediction score stats
# Learning always includes inference
if self.collectStats:
activeColumns = bottomUpInput.nonzero()[0]
if enableInference:
predictedState = self.infPredictedState['t-1']
else:
predictedState = self.lrnPredictedState['t-1']
self._updateStatsInferEnd(self._internalStats,
activeColumns,
predictedState,
self.colConfidence['t-1'])
# Finally return the TM output
output = self._computeOutput()
# Print diagnostic information based on the current verbosity level
self.printComputeEnd(output, learn=enableLearn)
self.resetCalled = False
return output |
def _copyAllocatedStates(self):
"""If state is allocated in CPP, copy over the data into our numpy arrays."""
# Get learn states if we need to print them out
if self.verbosity > 1 or self.retrieveLearningStates:
(activeT, activeT1, predT, predT1) = self.cells4.getLearnStates()
self.lrnActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
self.lrnActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
self.lrnPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
self.lrnPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn))
if self.allocateStatesInCPP:
assert False
(activeT, activeT1, predT, predT1, colConfidenceT, colConfidenceT1, confidenceT,
confidenceT1) = self.cells4.getStates()
self.cellConfidence['t'] = confidenceT.reshape((self.numberOfCols, self.cellsPerColumn))
self.cellConfidence['t-1'] = confidenceT1.reshape((self.numberOfCols, self.cellsPerColumn))
self.colConfidence['t'] = colConfidenceT.reshape(self.numberOfCols)
self.colConfidence['t-1'] = colConfidenceT1.reshape(self.numberOfCols)
self.infActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
self.infActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
self.infPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
self.infPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn)) |
def _setStatePointers(self):
"""If we are having CPP use numpy-allocated buffers, set these buffer
pointers. This is a relatively fast operation and, for safety, should be
done before every call to the cells4 compute methods. This protects us
in situations where code can cause Python or numpy to create copies."""
if not self.allocateStatesInCPP:
self.cells4.setStatePointers(
self.infActiveState["t"], self.infActiveState["t-1"],
self.infPredictedState["t"], self.infPredictedState["t-1"],
self.colConfidence["t"], self.colConfidence["t-1"],
self.cellConfidence["t"], self.cellConfidence["t-1"]) |
def reset(self):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.reset`.
"""
if self.verbosity >= 3:
print "TM Reset"
self._setStatePointers()
self.cells4.reset()
BacktrackingTM.reset(self) |
def trimSegments(self, minPermanence=None, minNumSyns=None):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.trimSegments`.
"""
# Fill in defaults
if minPermanence is None:
minPermanence = 0.0
if minNumSyns is None:
minNumSyns = 0
# Print all cells if verbosity says to
if self.verbosity >= 5:
print "Cells, all segments:"
self.printCells(predictedOnly=False)
return self.cells4.trimSegments(minPermanence=minPermanence, minNumSyns=minNumSyns) |
def printSegmentUpdates(self):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.printSegmentUpdates`.
"""
# TODO: need to add C++ accessors to implement this method
assert False
print "=== SEGMENT UPDATES ===, Num = ", len(self.segmentUpdates)
for key, updateList in self.segmentUpdates.iteritems():
c,i = key[0],key[1]
print c,i,updateList |
def _slowIsSegmentActive(self, seg, timeStep):
"""
A segment is active if it has >= activationThreshold connected
synapses that are active due to infActiveState.
"""
numSyn = seg.size()
numActiveSyns = 0
for synIdx in xrange(numSyn):
if seg.getPermanence(synIdx) < self.connectedPerm:
continue
sc, si = self.getColCellIdx(seg.getSrcCellIdx(synIdx))
if self.infActiveState[timeStep][sc, si]:
numActiveSyns += 1
if numActiveSyns >= self.activationThreshold:
return True
return numActiveSyns >= self.activationThreshold |
def printCell(self, c, i, onlyActiveSegments=False):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.printCell`.
"""
nSegs = self.cells4.nSegmentsOnCell(c,i)
if nSegs > 0:
segList = self.cells4.getNonEmptySegList(c,i)
gidx = c * self.cellsPerColumn + i
print "Column", c, "Cell", i, "(%d)"%(gidx),":", nSegs, "segment(s)"
for k,segIdx in enumerate(segList):
seg = self.cells4.getSegment(c, i, segIdx)
isActive = self._slowIsSegmentActive(seg, 't')
if onlyActiveSegments and not isActive:
continue
isActiveStr = "*" if isActive else " "
print " %sSeg #%-3d" % (isActiveStr, segIdx),
print seg.size(),
print seg.isSequenceSegment(), "%9.7f" % (seg.dutyCycle(
self.cells4.getNLrnIterations(), False, True)),
# numPositive/totalActivations
print "(%4d/%-4d)" % (seg.getPositiveActivations(),
seg.getTotalActivations()),
# Age
print "%4d" % (self.cells4.getNLrnIterations()
- seg.getLastActiveIteration()),
numSyn = seg.size()
for s in xrange(numSyn):
sc, si = self.getColCellIdx(seg.getSrcCellIdx(s))
print "[%d,%d]%4.2f"%(sc, si, seg.getPermanence(s)),
print |
def getColCellIdx(self, idx):
"""
Get column and cell within column from a global cell index.
The global index is ``idx = colIdx * nCellsPerCol() + cellIdxInCol``
:param idx: (int) global cell index
:returns: (tuple) (colIdx, cellIdxInCol)
"""
c = idx//self.cellsPerColumn
i = idx - c*self.cellsPerColumn
return c,i |
def getSegmentOnCell(self, c, i, segIdx):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.getSegmentOnCell`.
"""
segList = self.cells4.getNonEmptySegList(c,i)
seg = self.cells4.getSegment(c, i, segList[segIdx])
numSyn = seg.size()
assert numSyn != 0
# Accumulate segment information
result = []
result.append([int(segIdx), bool(seg.isSequenceSegment()),
seg.getPositiveActivations(),
seg.getTotalActivations(), seg.getLastActiveIteration(),
seg.getLastPosDutyCycle(),
seg.getLastPosDutyCycleIteration()])
for s in xrange(numSyn):
sc, si = self.getColCellIdx(seg.getSrcCellIdx(s))
result.append([int(sc), int(si), seg.getPermanence(s)])
return result |
def getSegmentInfo(self, collectActiveData = False):
"""
Overrides :meth:`nupic.algorithms.backtracking_tm.BacktrackingTM.getSegmentInfo`.
"""
# Requires appropriate accessors in C++ cells4 (currently unimplemented)
assert collectActiveData == False
nSegments, nSynapses = self.getNumSegments(), self.cells4.nSynapses()
distSegSizes, distNSegsPerCell = {}, {}
nActiveSegs, nActiveSynapses = 0, 0
distPermValues = {} # Num synapses with given permanence values
numAgeBuckets = 20
distAges = []
ageBucketSize = int((self.iterationIdx+20) / 20)
for i in range(numAgeBuckets):
distAges.append(['%d-%d' % (i*ageBucketSize, (i+1)*ageBucketSize-1), 0])
for c in xrange(self.numberOfCols):
for i in xrange(self.cellsPerColumn):
# Update histogram counting cell sizes
nSegmentsThisCell = self.getNumSegmentsInCell(c,i)
if nSegmentsThisCell > 0:
if distNSegsPerCell.has_key(nSegmentsThisCell):
distNSegsPerCell[nSegmentsThisCell] += 1
else:
distNSegsPerCell[nSegmentsThisCell] = 1
# Update histogram counting segment sizes.
segList = self.cells4.getNonEmptySegList(c,i)
for segIdx in xrange(nSegmentsThisCell):
seg = self.getSegmentOnCell(c, i, segIdx)
nSynapsesThisSeg = len(seg) - 1
if nSynapsesThisSeg > 0:
if distSegSizes.has_key(nSynapsesThisSeg):
distSegSizes[nSynapsesThisSeg] += 1
else:
distSegSizes[nSynapsesThisSeg] = 1
# Accumulate permanence value histogram
for syn in seg[1:]:
p = int(syn[2]*10)
if distPermValues.has_key(p):
distPermValues[p] += 1
else:
distPermValues[p] = 1
segObj = self.cells4.getSegment(c, i, segList[segIdx])
age = self.iterationIdx - segObj.getLastActiveIteration()
ageBucket = int(age/ageBucketSize)
distAges[ageBucket][1] += 1
return (nSegments, nSynapses, nActiveSegs, nActiveSynapses, \
distSegSizes, distNSegsPerCell, distPermValues, distAges) |
def main(argv):
"""
The main function of the HypersearchWorker script. This parses the command
line arguments, instantiates a HypersearchWorker instance, and then
runs it.
Parameters:
----------------------------------------------------------------------
retval: jobID of the job we ran. This is used by unit test code
when calling this working using the --params command
line option (which tells this worker to insert the job
itself).
"""
parser = OptionParser(helpString)
parser.add_option("--jobID", action="store", type="int", default=None,
help="jobID of the job within the dbTable [default: %default].")
parser.add_option("--modelID", action="store", type="str", default=None,
help=("Tell worker to re-run this model ID. When specified, jobID "
"must also be specified [default: %default]."))
parser.add_option("--workerID", action="store", type="str", default=None,
help=("workerID of the scheduler's SlotAgent (GenericWorker) that "
"hosts this SpecializedWorker [default: %default]."))
parser.add_option("--params", action="store", default=None,
help="Create and execute a new hypersearch request using this JSON " \
"format params string. This is helpful for unit tests and debugging. " \
"When specified jobID must NOT be specified. [default: %default].")
parser.add_option("--clearModels", action="store_true", default=False,
help="clear out the models table before starting [default: %default].")
parser.add_option("--resetJobStatus", action="store_true", default=False,
help="Reset the job status before starting [default: %default].")
parser.add_option("--logLevel", action="store", type="int", default=None,
help="override default log level. Pass in an integer value that "
"represents the desired logging level (10=logging.DEBUG, "
"20=logging.INFO, etc.) [default: %default].")
# Evaluate command line arguments
(options, args) = parser.parse_args(argv[1:])
if len(args) != 0:
raise RuntimeError("Expected no command line arguments, but got: %s" % \
(args))
if (options.jobID and options.params):
raise RuntimeError("--jobID and --params can not be used at the same time")
if (options.jobID is None and options.params is None):
raise RuntimeError("Either --jobID or --params must be specified.")
initLogging(verbose=True)
# Instantiate the HypersearchWorker and run it
hst = HypersearchWorker(options, argv[1:])
# Normal use. This is one of among a number of workers. If we encounter
# an exception at the outer loop here, we fail the entire job.
if options.params is None:
try:
jobID = hst.run()
except Exception, e:
jobID = options.jobID
msg = StringIO.StringIO()
print >>msg, "%s: Exception occurred in Hypersearch Worker: %r" % \
(ErrorCodes.hypersearchLogicErr, e)
traceback.print_exc(None, msg)
completionReason = ClientJobsDAO.CMPL_REASON_ERROR
completionMsg = msg.getvalue()
hst.logger.error(completionMsg)
# If no other worker already marked the job as failed, do so now.
jobsDAO = ClientJobsDAO.get()
workerCmpReason = jobsDAO.jobGetFields(options.jobID,
['workerCompletionReason'])[0]
if workerCmpReason == ClientJobsDAO.CMPL_REASON_SUCCESS:
jobsDAO.jobSetFields(options.jobID, fields=dict(
cancel=True,
workerCompletionReason = ClientJobsDAO.CMPL_REASON_ERROR,
workerCompletionMsg = completionMsg),
useConnectionID=False,
ignoreUnchanged=True)
# Run just 1 worker for the entire job. Used for unit tests that run in
# 1 process
else:
jobID = None
completionReason = ClientJobsDAO.CMPL_REASON_SUCCESS
completionMsg = "Success"
try:
jobID = hst.run()
except Exception, e:
jobID = hst._options.jobID
completionReason = ClientJobsDAO.CMPL_REASON_ERROR
completionMsg = "ERROR: %s" % (e,)
raise
finally:
if jobID is not None:
cjDAO = ClientJobsDAO.get()
cjDAO.jobSetCompleted(jobID=jobID,
completionReason=completionReason,
completionMsg=completionMsg)
return jobID |
def _processUpdatedModels(self, cjDAO):
""" For all models that modified their results since last time this method
was called, send their latest results to the Hypersearch implementation.
"""
# Get the latest update counters. This returns a list of tuples:
# (modelID, updateCounter)
curModelIDCtrList = cjDAO.modelsGetUpdateCounters(self._options.jobID)
if len(curModelIDCtrList) == 0:
return
self.logger.debug("current modelID/updateCounters: %s" \
% (str(curModelIDCtrList)))
self.logger.debug("last modelID/updateCounters: %s" \
% (str(self._modelIDCtrList)))
# --------------------------------------------------------------------
# Find out which ones have changed update counters. Since these are models
# that the Hypersearch implementation already knows about, we don't need to
# send params or paramsHash
curModelIDCtrList = sorted(curModelIDCtrList)
numItems = len(curModelIDCtrList)
# Each item in the list we are filtering contains:
# (idxIntoModelIDCtrList, (modelID, curCtr), (modelID, oldCtr))
# We only want to keep the ones where the oldCtr != curCtr
changedEntries = filter(lambda x:x[1][1] != x[2][1],
itertools.izip(xrange(numItems), curModelIDCtrList,
self._modelIDCtrList))
if len(changedEntries) > 0:
# Update values in our cache
self.logger.debug("changedEntries: %s", str(changedEntries))
for entry in changedEntries:
(idx, (modelID, curCtr), (_, oldCtr)) = entry
self._modelIDCtrDict[modelID] = curCtr
assert (self._modelIDCtrList[idx][0] == modelID)
assert (curCtr != oldCtr)
self._modelIDCtrList[idx][1] = curCtr
# Tell Hypersearch implementation of the updated results for each model
changedModelIDs = [x[1][0] for x in changedEntries]
modelResults = cjDAO.modelsGetResultAndStatus(changedModelIDs)
for mResult in modelResults:
results = mResult.results
if results is not None:
results = json.loads(results)
self._hs.recordModelProgress(modelID=mResult.modelId,
modelParams = None,
modelParamsHash = mResult.engParamsHash,
results = results,
completed = (mResult.status == cjDAO.STATUS_COMPLETED),
completionReason = mResult.completionReason,
matured = mResult.engMatured,
numRecords = mResult.numRecords)
# --------------------------------------------------------------------
# Figure out which ones are newly arrived and add them to our
# cache
curModelIDSet = set([x[0] for x in curModelIDCtrList])
newModelIDs = curModelIDSet.difference(self._modelIDSet)
if len(newModelIDs) > 0:
# Add new modelID and counters to our cache
self._modelIDSet.update(newModelIDs)
curModelIDCtrDict = dict(curModelIDCtrList)
# Get the results for each of these models and send them to the
# Hypersearch implementation.
modelInfos = cjDAO.modelsGetResultAndStatus(newModelIDs)
modelInfos.sort()
modelParamsAndHashs = cjDAO.modelsGetParams(newModelIDs)
modelParamsAndHashs.sort()
for (mResult, mParamsAndHash) in itertools.izip(modelInfos,
modelParamsAndHashs):
modelID = mResult.modelId
assert (modelID == mParamsAndHash.modelId)
# Update our cache of IDs and update counters
self._modelIDCtrDict[modelID] = curModelIDCtrDict[modelID]
self._modelIDCtrList.append([modelID, curModelIDCtrDict[modelID]])
# Tell the Hypersearch implementation of the new model
results = mResult.results
if results is not None:
results = json.loads(mResult.results)
self._hs.recordModelProgress(modelID = modelID,
modelParams = json.loads(mParamsAndHash.params),
modelParamsHash = mParamsAndHash.engParamsHash,
results = results,
completed = (mResult.status == cjDAO.STATUS_COMPLETED),
completionReason = (mResult.completionReason),
matured = mResult.engMatured,
numRecords = mResult.numRecords)
# Keep our list sorted
self._modelIDCtrList.sort() |
def run(self):
""" Run this worker.
Parameters:
----------------------------------------------------------------------
retval: jobID of the job we ran. This is used by unit test code
when calling this working using the --params command
line option (which tells this worker to insert the job
itself).
"""
# Easier access to options
options = self._options
# ---------------------------------------------------------------------
# Connect to the jobs database
self.logger.info("Connecting to the jobs database")
cjDAO = ClientJobsDAO.get()
# Get our worker ID
self._workerID = cjDAO.getConnectionID()
if options.clearModels:
cjDAO.modelsClearAll()
# -------------------------------------------------------------------------
# if params were specified on the command line, insert a new job using
# them.
if options.params is not None:
options.jobID = cjDAO.jobInsert(client='hwTest', cmdLine="echo 'test mode'",
params=options.params, alreadyRunning=True,
minimumWorkers=1, maximumWorkers=1,
jobType = cjDAO.JOB_TYPE_HS)
if options.workerID is not None:
wID = options.workerID
else:
wID = self._workerID
buildID = Configuration.get('nupic.software.buildNumber', 'N/A')
logPrefix = '<BUILDID=%s, WORKER=HW, WRKID=%s, JOBID=%s> ' % \
(buildID, wID, options.jobID)
ExtendedLogger.setLogPrefix(logPrefix)
# ---------------------------------------------------------------------
# Get the search parameters
# If asked to reset the job status, do that now
if options.resetJobStatus:
cjDAO.jobSetFields(options.jobID,
fields={'workerCompletionReason': ClientJobsDAO.CMPL_REASON_SUCCESS,
'cancel': False,
#'engWorkerState': None
},
useConnectionID=False,
ignoreUnchanged=True)
jobInfo = cjDAO.jobInfo(options.jobID)
self.logger.info("Job info retrieved: %s" % (str(clippedObj(jobInfo))))
# ---------------------------------------------------------------------
# Instantiate the Hypersearch object, which will handle the logic of
# which models to create when we need more to evaluate.
jobParams = json.loads(jobInfo.params)
# Validate job params
jsonSchemaPath = os.path.join(os.path.dirname(__file__),
"jsonschema",
"jobParamsSchema.json")
validate(jobParams, schemaPath=jsonSchemaPath)
hsVersion = jobParams.get('hsVersion', None)
if hsVersion == 'v2':
self._hs = HypersearchV2(searchParams=jobParams, workerID=self._workerID,
cjDAO=cjDAO, jobID=options.jobID, logLevel=options.logLevel)
else:
raise RuntimeError("Invalid Hypersearch implementation (%s) specified" \
% (hsVersion))
# =====================================================================
# The main loop.
try:
exit = False
numModelsTotal = 0
print >>sys.stderr, "reporter:status:Evaluating first model..."
while not exit:
# ------------------------------------------------------------------
# Choose a model to evaluate
batchSize = 10 # How many to try at a time.
modelIDToRun = None
while modelIDToRun is None:
if options.modelID is None:
# -----------------------------------------------------------------
# Get the latest results on all running models and send them to
# the Hypersearch implementation
# This calls cjDAO.modelsGetUpdateCounters(), compares the
# updateCounters with what we have cached, fetches the results for the
# changed and new models, and sends those to the Hypersearch
# implementation's self._hs.recordModelProgress() method.
self._processUpdatedModels(cjDAO)
# --------------------------------------------------------------------
# Create a new batch of models
(exit, newModels) = self._hs.createModels(numModels = batchSize)
if exit:
break
# No more models left to create, just loop. The _hs is waiting for
# all remaining running models to complete, and may pick up on an
# orphan if it detects one.
if len(newModels) == 0:
continue
# Try and insert one that we will run
for (modelParams, modelParamsHash, particleHash) in newModels:
jsonModelParams = json.dumps(modelParams)
(modelID, ours) = cjDAO.modelInsertAndStart(options.jobID,
jsonModelParams, modelParamsHash, particleHash)
# Some other worker is already running it, tell the Hypersearch object
# so that it doesn't try and insert it again
if not ours:
mParamsAndHash = cjDAO.modelsGetParams([modelID])[0]
mResult = cjDAO.modelsGetResultAndStatus([modelID])[0]
results = mResult.results
if results is not None:
results = json.loads(results)
modelParams = json.loads(mParamsAndHash.params)
particleHash = cjDAO.modelsGetFields(modelID,
['engParticleHash'])[0]
particleInst = "%s.%s" % (
modelParams['particleState']['id'],
modelParams['particleState']['genIdx'])
self.logger.info("Adding model %d to our internal DB " \
"because modelInsertAndStart() failed to insert it: " \
"paramsHash=%s, particleHash=%s, particleId='%s'", modelID,
mParamsAndHash.engParamsHash.encode('hex'),
particleHash.encode('hex'), particleInst)
self._hs.recordModelProgress(modelID = modelID,
modelParams = modelParams,
modelParamsHash = mParamsAndHash.engParamsHash,
results = results,
completed = (mResult.status == cjDAO.STATUS_COMPLETED),
completionReason = mResult.completionReason,
matured = mResult.engMatured,
numRecords = mResult.numRecords)
else:
modelIDToRun = modelID
break
else:
# A specific modelID was passed on the command line
modelIDToRun = int(options.modelID)
mParamsAndHash = cjDAO.modelsGetParams([modelIDToRun])[0]
modelParams = json.loads(mParamsAndHash.params)
modelParamsHash = mParamsAndHash.engParamsHash
# Make us the worker
cjDAO.modelSetFields(modelIDToRun,
dict(engWorkerConnId=self._workerID))
if False:
# Change the hash and params of the old entry so that we can
# create a new model with the same params
for attempt in range(1000):
paramsHash = hashlib.md5("OrphanParams.%d.%d" % (modelIDToRun,
attempt)).digest()
particleHash = hashlib.md5("OrphanParticle.%d.%d" % (modelIDToRun,
attempt)).digest()
try:
cjDAO.modelSetFields(modelIDToRun,
dict(engParamsHash=paramsHash,
engParticleHash=particleHash))
success = True
except:
success = False
if success:
break
if not success:
raise RuntimeError("Unexpected failure to change paramsHash and "
"particleHash of orphaned model")
(modelIDToRun, ours) = cjDAO.modelInsertAndStart(options.jobID,
mParamsAndHash.params, modelParamsHash)
# ^^^ end while modelIDToRun ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# ---------------------------------------------------------------
# We have a model, evaluate it now
# All done?
if exit:
break
# Run the model now
self.logger.info("RUNNING MODEL GID=%d, paramsHash=%s, params=%s",
modelIDToRun, modelParamsHash.encode('hex'), modelParams)
# ---------------------------------------------------------------------
# Construct model checkpoint GUID for this model:
# jobParams['persistentJobGUID'] contains the client's (e.g., API Server)
# persistent, globally-unique model identifier, which is what we need;
persistentJobGUID = jobParams['persistentJobGUID']
assert persistentJobGUID, "persistentJobGUID: %r" % (persistentJobGUID,)
modelCheckpointGUID = jobInfo.client + "_" + persistentJobGUID + (
'_' + str(modelIDToRun))
self._hs.runModel(modelID=modelIDToRun, jobID = options.jobID,
modelParams=modelParams, modelParamsHash=modelParamsHash,
jobsDAO=cjDAO, modelCheckpointGUID=modelCheckpointGUID)
# TODO: don't increment for orphaned models
numModelsTotal += 1
self.logger.info("COMPLETED MODEL GID=%d; EVALUATED %d MODELs",
modelIDToRun, numModelsTotal)
print >>sys.stderr, "reporter:status:Evaluated %d models..." % \
(numModelsTotal)
print >>sys.stderr, "reporter:counter:HypersearchWorker,numModels,1"
if options.modelID is not None:
exit = True
# ^^^ end while not exit
finally:
# Provide Hypersearch instance an opportunity to clean up temporary files
self._hs.close()
self.logger.info("FINISHED. Evaluated %d models." % (numModelsTotal))
print >>sys.stderr, "reporter:status:Finished, evaluated %d models" % (numModelsTotal)
return options.jobID |
def getBucketIndices(self, x):
""" See method description in base.py """
if ((isinstance(x, float) and math.isnan(x)) or
x == SENTINEL_VALUE_FOR_MISSING_DATA):
return [None]
if self._offset is None:
self._offset = x
bucketIdx = (
(self._maxBuckets/2) + int(round((x - self._offset) / self.resolution))
)
if bucketIdx < 0:
bucketIdx = 0
elif bucketIdx >= self._maxBuckets:
bucketIdx = self._maxBuckets-1
return [bucketIdx] |
def mapBucketIndexToNonZeroBits(self, index):
"""
Given a bucket index, return the list of non-zero bits. If the bucket
index does not exist, it is created. If the index falls outside our range
we clip it.
:param index The bucket index to get non-zero bits for.
@returns numpy array of indices of non-zero bits for specified index.
"""
if index < 0:
index = 0
if index >= self._maxBuckets:
index = self._maxBuckets-1
if not self.bucketMap.has_key(index):
if self.verbosity >= 2:
print "Adding additional buckets to handle index=", index
self._createBucket(index)
return self.bucketMap[index] |
def encodeIntoArray(self, x, output):
""" See method description in base.py """
if x is not None and not isinstance(x, numbers.Number):
raise TypeError(
"Expected a scalar input but got input of type %s" % type(x))
# Get the bucket index to use
bucketIdx = self.getBucketIndices(x)[0]
# None is returned for missing value in which case we return all 0's.
output[0:self.n] = 0
if bucketIdx is not None:
output[self.mapBucketIndexToNonZeroBits(bucketIdx)] = 1 |
def _createBucket(self, index):
"""
Create the given bucket index. Recursively create as many in-between
bucket indices as necessary.
"""
if index < self.minIndex:
if index == self.minIndex - 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the min representation
self.bucketMap[index] = self._newRepresentation(self.minIndex,
index)
self.minIndex = index
else:
# Recursively create all the indices above and then this index
self._createBucket(index+1)
self._createBucket(index)
else:
if index == self.maxIndex + 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the max representation
self.bucketMap[index] = self._newRepresentation(self.maxIndex,
index)
self.maxIndex = index
else:
# Recursively create all the indices below and then this index
self._createBucket(index-1)
self._createBucket(index) |
def _newRepresentation(self, index, newIndex):
"""
Return a new representation for newIndex that overlaps with the
representation at index by exactly w-1 bits
"""
newRepresentation = self.bucketMap[index].copy()
# Choose the bit we will replace in this representation. We need to shift
# this bit deterministically. If this is always chosen randomly then there
# is a 1 in w chance of the same bit being replaced in neighboring
# representations, which is fairly high
ri = newIndex % self.w
# Now we choose a bit such that the overlap rules are satisfied.
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
while newBit in self.bucketMap[index] or \
not self._newRepresentationOK(newRepresentation, newIndex):
self.numTries += 1
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
return newRepresentation |
def _newRepresentationOK(self, newRep, newIndex):
"""
Return True if this new candidate representation satisfies all our overlap
rules. Since we know that neighboring representations differ by at most
one bit, we compute running overlaps.
"""
if newRep.size != self.w:
return False
if (newIndex < self.minIndex-1) or (newIndex > self.maxIndex+1):
raise ValueError("newIndex must be within one of existing indices")
# A binary representation of newRep. We will use this to test containment
newRepBinary = numpy.array([False]*self.n)
newRepBinary[newRep] = True
# Midpoint
midIdx = self._maxBuckets/2
# Start by checking the overlap at minIndex
runningOverlap = self._countOverlap(self.bucketMap[self.minIndex], newRep)
if not self._overlapOK(self.minIndex, newIndex, overlap=runningOverlap):
return False
# Compute running overlaps all the way to the midpoint
for i in range(self.minIndex+1, midIdx+1):
# This is the bit that is going to change
newBit = (i-1)%self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i-1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
# At this point, runningOverlap contains the overlap for midIdx
# Compute running overlaps all the way to maxIndex
for i in range(midIdx+1, self.maxIndex+1):
# This is the bit that is going to change
newBit = i%self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i-1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
return True |
def _countOverlapIndices(self, i, j):
"""
Return the overlap between bucket indices i and j
"""
if self.bucketMap.has_key(i) and self.bucketMap.has_key(j):
iRep = self.bucketMap[i]
jRep = self.bucketMap[j]
return self._countOverlap(iRep, jRep)
else:
raise ValueError("Either i or j don't exist") |
def _countOverlap(rep1, rep2):
"""
Return the overlap between two representations. rep1 and rep2 are lists of
non-zero indices.
"""
overlap = 0
for e in rep1:
if e in rep2:
overlap += 1
return overlap |
def _overlapOK(self, i, j, overlap=None):
"""
Return True if the given overlap between bucket indices i and j are
acceptable. If overlap is not specified, calculate it from the bucketMap
"""
if overlap is None:
overlap = self._countOverlapIndices(i, j)
if abs(i-j) < self.w:
if overlap == (self.w - abs(i-j)):
return True
else:
return False
else:
if overlap <= self._maxOverlap:
return True
else:
return False |
def _initializeBucketMap(self, maxBuckets, offset):
"""
Initialize the bucket map assuming the given number of maxBuckets.
"""
# The first bucket index will be _maxBuckets / 2 and bucket indices will be
# allowed to grow lower or higher as long as they don't become negative.
# _maxBuckets is required because the current SDR Classifier assumes bucket
# indices must be non-negative. This normally does not need to be changed
# but if altered, should be set to an even number.
self._maxBuckets = maxBuckets
self.minIndex = self._maxBuckets / 2
self.maxIndex = self._maxBuckets / 2
# The scalar offset used to map scalar values to bucket indices. The middle
# bucket will correspond to numbers in the range
# [offset-resolution/2, offset+resolution/2).
# The bucket index for a number x will be:
# maxBuckets/2 + int( round( (x-offset)/resolution ) )
self._offset = offset
# This dictionary maps a bucket index into its bit representation
# We initialize the class with a single bucket with index 0
self.bucketMap = {}
def _permutation(n):
r = numpy.arange(n, dtype=numpy.uint32)
self.random.shuffle(r)
return r
self.bucketMap[self.minIndex] = _permutation(self.n)[0:self.w]
# How often we need to retry when generating valid encodings
self.numTries = 0 |
def retrySQL(timeoutSec=60*5, logger=None):
""" Return a closure suitable for use as a decorator for
retrying a pymysql DAO function on certain failures that warrant retries (
e.g., RDS/MySQL server down temporarily, transaction deadlock, etc.).
We share this function across multiple scripts (e.g., ClientJobsDAO,
StreamMgr) for consitent behavior.
.. note:: Please ensure that the operation being retried is idempotent.
.. note:: logging must be initialized *before* any loggers are created, else
there will be no output; see nupic.support.initLogging()
Usage Example:
.. code-block:: python
@retrySQL()
def jobInfo(self, jobID):
...
:param timeoutSec: How many seconds from time of initial call to stop retrying
(floating point)
:param logger: User-supplied logger instance.
"""
if logger is None:
logger = logging.getLogger(__name__)
def retryFilter(e, args, kwargs):
if isinstance(e, (pymysql.InternalError, pymysql.OperationalError)):
if e.args and e.args[0] in _ALL_RETRIABLE_ERROR_CODES:
return True
elif isinstance(e, pymysql.Error):
if (e.args and
inspect.isclass(e.args[0]) and issubclass(e.args[0], socket_error)):
return True
return False
retryExceptions = tuple([
pymysql.InternalError,
pymysql.OperationalError,
pymysql.Error,
])
return make_retry_decorator(
timeoutSec=timeoutSec, initialRetryDelaySec=0.1, maxRetryDelaySec=10,
retryExceptions=retryExceptions, retryFilter=retryFilter,
logger=logger) |
def create(*args, **kwargs):
"""
Create a SDR classifier factory.
The implementation of the SDR Classifier can be specified with
the "implementation" keyword argument.
The SDRClassifierFactory uses the implementation as specified in
`Default NuPIC Configuration <default-config.html>`_.
"""
impl = kwargs.pop('implementation', None)
if impl is None:
impl = Configuration.get('nupic.opf.sdrClassifier.implementation')
if impl == 'py':
return SDRClassifier(*args, **kwargs)
elif impl == 'cpp':
return FastSDRClassifier(*args, **kwargs)
elif impl == 'diff':
return SDRClassifierDiff(*args, **kwargs)
else:
raise ValueError('Invalid classifier implementation (%r). Value must be '
'"py", "cpp" or "diff".' % impl) |
def read(proto):
"""
:param proto: SDRClassifierRegionProto capnproto object
"""
impl = proto.implementation
if impl == 'py':
return SDRClassifier.read(proto.sdrClassifier)
elif impl == 'cpp':
return FastSDRClassifier.read(proto.sdrClassifier)
elif impl == 'diff':
return SDRClassifierDiff.read(proto.sdrClassifier)
else:
raise ValueError('Invalid classifier implementation (%r). Value must be '
'"py", "cpp" or "diff".' % impl) |
def cross_list(*sequences):
"""
From: http://book.opensourceproject.org.cn/lamp/python/pythoncook2/opensource/0596007973/pythoncook2-chp-19-sect-9.html
"""
result = [[ ]]
for seq in sequences:
result = [sublist+[item] for sublist in result for item in seq]
return result |
def cross(*sequences):
"""
From: http://book.opensourceproject.org.cn/lamp/python/pythoncook2/opensource/0596007973/pythoncook2-chp-19-sect-9.html
"""
# visualize an odometer, with "wheels" displaying "digits"...:
wheels = map(iter, sequences)
digits = [it.next( ) for it in wheels]
while True:
yield tuple(digits)
for i in range(len(digits)-1, -1, -1):
try:
digits[i] = wheels[i].next( )
break
except StopIteration:
wheels[i] = iter(sequences[i])
digits[i] = wheels[i].next( )
else:
break |
def dcross(**keywords):
"""
Similar to cross(), but generates output dictionaries instead of tuples.
"""
keys = keywords.keys()
# Could use keywords.values(), but unsure whether the order
# the values come out in is guaranteed to be the same as that of keys
# (appears to be anecdotally true).
sequences = [keywords[key] for key in keys]
wheels = map(iter, sequences)
digits = [it.next( ) for it in wheels]
while True:
yield dict(zip(keys, digits))
for i in range(len(digits)-1, -1, -1):
try:
digits[i] = wheels[i].next( )
break
except StopIteration:
wheels[i] = iter(sequences[i])
digits[i] = wheels[i].next( )
else:
break |
def mmGetMetricFromTrace(self, trace):
"""
Convenience method to compute a metric over an indices trace, excluding
resets.
@param (IndicesTrace) Trace of indices
@return (Metric) Metric over trace excluding resets
"""
return Metric.createFromTrace(trace.makeCountsTrace(),
excludeResets=self.mmGetTraceResets()) |
def mmGetMetricSequencesPredictedActiveCellsPerColumn(self):
"""
Metric for number of predicted => active cells per column for each sequence
@return (Metric) metric
"""
self._mmComputeTransitionTraces()
numCellsPerColumn = []
for predictedActiveCells in (
self._mmData["predictedActiveCellsForSequence"].values()):
cellsForColumn = self.mapCellsToColumns(predictedActiveCells)
numCellsPerColumn += [len(x) for x in cellsForColumn.values()]
return Metric(self,
"# predicted => active cells per column for each sequence",
numCellsPerColumn) |
def mmGetMetricSequencesPredictedActiveCellsShared(self):
"""
Metric for number of sequences each predicted => active cell appears in
Note: This metric is flawed when it comes to high-order sequences.
@return (Metric) metric
"""
self._mmComputeTransitionTraces()
numSequencesForCell = defaultdict(lambda: 0)
for predictedActiveCells in (
self._mmData["predictedActiveCellsForSequence"].values()):
for cell in predictedActiveCells:
numSequencesForCell[cell] += 1
return Metric(self,
"# sequences each predicted => active cells appears in",
numSequencesForCell.values()) |
def mmPrettyPrintConnections(self):
"""
Pretty print the connections in the temporal memory.
TODO: Use PrettyTable.
@return (string) Pretty-printed text
"""
text = ""
text += ("Segments: (format => "
"(#) [(source cell=permanence ...), ...]\n")
text += "------------------------------------\n"
columns = range(self.numberOfColumns())
for column in columns:
cells = self.cellsForColumn(column)
for cell in cells:
segmentDict = dict()
for seg in self.connections.segmentsForCell(cell):
synapseList = []
for synapse in self.connections.synapsesForSegment(seg):
synapseData = self.connections.dataForSynapse(synapse)
synapseList.append(
(synapseData.presynapticCell, synapseData.permanence))
synapseList.sort()
synapseStringList = ["{0:3}={1:.2f}".format(sourceCell, permanence) for
sourceCell, permanence in synapseList]
segmentDict[seg] = "({0})".format(" ".join(synapseStringList))
text += ("Column {0:3} / Cell {1:3}:\t({2}) {3}\n".format(
column, cell,
len(segmentDict.values()),
"[{0}]".format(", ".join(segmentDict.values()))))
if column < len(columns) - 1: # not last
text += "\n"
text += "------------------------------------\n"
return text |
def mmPrettyPrintSequenceCellRepresentations(self, sortby="Column"):
"""
Pretty print the cell representations for sequences in the history.
@param sortby (string) Column of table to sort by
@return (string) Pretty-printed text
"""
self._mmComputeTransitionTraces()
table = PrettyTable(["Pattern", "Column", "predicted=>active cells"])
for sequenceLabel, predictedActiveCells in (
self._mmData["predictedActiveCellsForSequence"].iteritems()):
cellsForColumn = self.mapCellsToColumns(predictedActiveCells)
for column, cells in cellsForColumn.iteritems():
table.add_row([sequenceLabel, column, list(cells)])
return table.get_string(sortby=sortby).encode("utf-8") |
def createTemporalAnomaly(recordParams, spatialParams=_SP_PARAMS,
temporalParams=_TM_PARAMS,
verbosity=_VERBOSITY):
"""Generates a Network with connected RecordSensor, SP, TM.
This function takes care of generating regions and the canonical links.
The network has a sensor region reading data from a specified input and
passing the encoded representation to an SPRegion.
The SPRegion output is passed to a TMRegion.
Note: this function returns a network that needs to be initialized. This
allows the user to extend the network by adding further regions and
connections.
:param recordParams: a dict with parameters for creating RecordSensor region.
:param spatialParams: a dict with parameters for creating SPRegion.
:param temporalParams: a dict with parameters for creating TMRegion.
:param verbosity: an integer representing how chatty the network will be.
"""
inputFilePath = recordParams["inputFilePath"]
scalarEncoderArgs = recordParams["scalarEncoderArgs"]
dateEncoderArgs = recordParams["dateEncoderArgs"]
scalarEncoder = ScalarEncoder(**scalarEncoderArgs)
dateEncoder = DateEncoder(**dateEncoderArgs)
encoder = MultiEncoder()
encoder.addEncoder(scalarEncoderArgs["name"], scalarEncoder)
encoder.addEncoder(dateEncoderArgs["name"], dateEncoder)
network = Network()
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": verbosity}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = encoder
sensor.dataSource = FileRecordStream(streamID=inputFilePath)
# Create the spatial pooler region
spatialParams["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(spatialParams))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor", "spatialPoolerRegion", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="spatialTopDownOut", destInput="spatialTopDownIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="temporalTopDownOut", destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TMRegion",
json.dumps(temporalParams))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink", "")
network.link("temporalPoolerRegion", "spatialPoolerRegion", "UniformLink", "",
srcOutput="topDownOut", destInput="topDownIn")
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score.
temporalPoolerRegion.setParameter("anomalyMode", True)
return network |
def runNetwork(network, writer):
"""Run the network and write output to writer.
:param network: a Network instance to run
:param writer: a csv.writer instance to write output to
"""
sensorRegion = network.regions["sensor"]
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
for i in xrange(_NUM_RECORDS):
# Run the network for a single iteration
network.run(1)
# Write out the anomaly score along with the record number and consumption
# value.
anomalyScore = temporalPoolerRegion.getOutputData("anomalyScore")[0]
consumption = sensorRegion.getOutputData("sourceOut")[0]
writer.writerow((i, consumption, anomalyScore)) |
def __appendActivities(self, periodicActivities):
"""
periodicActivities: A sequence of PeriodicActivityRequest elements
"""
for req in periodicActivities:
act = self.Activity(repeating=req.repeating,
period=req.period,
cb=req.cb,
iteratorHolder=[iter(xrange(req.period-1))])
self.__activities.append(act)
return |
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