def testBadDataset(self):
filename = _getTempFileName()
print 'Creating tempfile:', filename
# Write bad dataset with records going backwards in time
fields = [
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp)
]
o = FileRecordStream(streamID=filename, write=True, fields=fields)
# Records
records = ([datetime(day=3, month=3, year=2010)],
[datetime(day=2, month=3, year=2010)])
o.appendRecord(records[0])
o.appendRecord(records[1])
o.close()
# Write bad dataset with broken sequences
fields = [
FieldMetaInfo('sid', FieldMetaType.integer,
FieldMetaSpecial.sequence)
]
o = FileRecordStream(streamID=filename, write=True, fields=fields)
# Records
records = ([1], [2], [1])
o.appendRecord(records[0])
o.appendRecord(records[1])
self.assertRaises(Exception, o.appendRecord, (records[2], ))
o.close()
def __getListMetaInfo(self, inferenceElement):
""" Get field metadata information for inferences that are of list type
TODO: Right now we assume list inferences are associated with the input field
metadata
"""
fieldMetaInfo = []
inferenceLabel = InferenceElement.getLabel(inferenceElement)
for inputFieldMeta in self.__inputFieldsMeta:
if InferenceElement.getInputElement(inferenceElement):
outputFieldMeta = FieldMetaInfo(
name=inputFieldMeta.name + ".actual",
type=inputFieldMeta.type,
special=inputFieldMeta.special
)
predictionField = FieldMetaInfo(
name=inputFieldMeta.name + "." + inferenceLabel,
type=inputFieldMeta.type,
special=inputFieldMeta.special
)
fieldMetaInfo.append(outputFieldMeta)
fieldMetaInfo.append(predictionField)
return fieldMetaInfo
def testEncoderWithoutResetAndSequenceFields(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('categories', FieldMetaType.list,
FieldMetaSpecial.category)
]
encoder = ModelRecordEncoder(fields=fields)
result = encoder.encode(
['rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5, [0, 1, 2]])
self.assertEqual(
result, {
'name': 'rec_1',
'timestamp': datetime(2010, 3, 1, 0, 0),
'integer': 5,
'real': 6.5,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 0,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 1, 0, 0),
'_timestampRecordIdx': None
})
# One more time to verify that sequence id is still 0
result = encoder.encode(
['rec_2',
datetime(day=2, month=3, year=2010), 5, 6.5, [0, 1, 2]])
self.assertEqual(
result, {
'name': 'rec_2',
'timestamp': datetime(2010, 3, 2, 0, 0),
'integer': 5,
'real': 6.5,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 0,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 2, 0, 0),
'_timestampRecordIdx': None
})
def testFieldMetaInfo(self):
# Create a single FieldMetaInfo instance from a File field"s meta-data tuple
e = ("pounds", FieldMetaType.float, FieldMetaSpecial.none)
m = FieldMetaInfo.createFromFileFieldElement(e)
self.assertEqual(e, m)
# Create a list of FieldMetaInfo instances from a list of File meta-data
# tuples
el = [("pounds", FieldMetaType.float, FieldMetaSpecial.none),
("price", FieldMetaType.float, FieldMetaSpecial.none),
("id", FieldMetaType.string, FieldMetaSpecial.sequence),
("date", FieldMetaType.datetime, FieldMetaSpecial.timestamp),
]
ml = FieldMetaInfo.createListFromFileFieldList(el)
self.assertEqual(el, ml)
def getDatasetFieldMetaData(self):
""" [virtual method override]
Returns: a tuple of dataset field metadata descriptors that are
arranged in the same order as the columns in the dataset.
Each field metadata descriptor is of type
nupic.data.fieldmeta.FieldMetaInfo
"""
return FieldMetaInfo.createListFromFileFieldList(self._reader.getFields())
def testFieldMetaInfo(self):
# Create a single FieldMetaInfo instance from a File field"s meta-data tuple
e = ("pounds", FieldMetaType.float, FieldMetaSpecial.none)
m = FieldMetaInfo.createFromFileFieldElement(e)
self.assertEqual(e, m)
# Create a list of FieldMetaInfo instances from a list of File meta-data
# tuples
el = [("pounds", FieldMetaType.float, FieldMetaSpecial.none),
("price", FieldMetaType.float, FieldMetaSpecial.none),
("id", FieldMetaType.string, FieldMetaSpecial.sequence),
("date", FieldMetaType.datetime, FieldMetaSpecial.timestamp),
]
ml = FieldMetaInfo.createListFromFileFieldList(el)
self.assertEqual(el, ml)
def getDatasetFieldMetaData(self):
""" [virtual method override]
Returns: a tuple of dataset field metadata descriptors that are
arranged in the same order as the columns in the dataset.
Each field metadata descriptor is of type
nupic.data.fieldmeta.FieldMetaInfo
"""
return FieldMetaInfo.createListFromFileFieldList(self._reader.getFields())
def __getDictMetaInfo(self, inferenceElement, inferenceDict):
"""Get field metadate information for inferences that are of dict type"""
fieldMetaInfo = []
inferenceLabel = InferenceElement.getLabel(inferenceElement)
if InferenceElement.getInputElement(inferenceElement):
fieldMetaInfo.append(FieldMetaInfo(name=inferenceLabel+".actual",
type=FieldMetaType.string,
special = ''))
keys = sorted(inferenceDict.keys())
for key in keys:
fieldMetaInfo.append(FieldMetaInfo(name=inferenceLabel+"."+str(key),
type=FieldMetaType.string,
special=''))
return fieldMetaInfo
def testRewindBeforeModelRecordEncoderIsCreated(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
]
stream = self.MyRecordStream(fields)
# Check that it doesn't crash by trying to operate on an absent encoder
self.assertIsNone(stream._modelRecordEncoder)
stream.rewind()
def __init__(self, fn, iterations, historyBuffer=None):
random.seed(42)
self.nIterations = iterations
self.fn = fn
self.iterations = iter(xrange(iterations))
self.history = historyBuffer
firstRecord = fn(0)
fieldList = [(name, self.__getType(val), '')
for name, val in firstRecord.iteritems()]
self.__metaData = FieldMetaInfo.createListFromFileFieldList(fieldList)
def _testTemporalShift():
""" Test to see if the metrics manager correctly shifts records for multistep
prediction cases
"""
print "*Testing Multistep temporal shift*..."
from nupic.data.fieldmeta import (FieldMetaInfo, FieldMetaType,
FieldMetaSpecial)
from nupic.frameworks.opf.metrics import MetricSpec
from nupic.frameworks.opf.opf_utils import ModelResult, SensorInput
onlineMetrics = ()
modelFieldMetaInfo = (FieldMetaInfo(name='consumption',
type=FieldMetaType.float,
special=FieldMetaSpecial.none), )
mgr = MetricsManager(metricSpecs=onlineMetrics,
fieldInfo=modelFieldMetaInfo,
inferenceType=InferenceType.TemporalMultiStep)
groundTruths = [{'consumption': i} for i in range(10)]
oneStepInfs = reversed(range(10))
threeStepInfs = range(5, 15)
for iterNum, gt, os, ts in zip(xrange(10), groundTruths, oneStepInfs,
threeStepInfs):
inferences = {InferenceElement.multiStepPredictions: {1: os, 3: ts}}
sensorInput = SensorInput(dataDict=[gt])
result = ModelResult(sensorInput=sensorInput, inferences=inferences)
mgr.update(result)
assert mgr._getGroundTruth(
InferenceElement.multiStepPredictions)[0] == gt
if iterNum < 1:
#assert mgr._getInference(InferenceElement.multiStepPredictions) is None
assert mgr._getInference(
InferenceElement.multiStepPredictions)[1] is None
else:
prediction = mgr._getInference(
InferenceElement.multiStepPredictions)[1]
assert prediction == 10 - iterNum
if iterNum < 3:
inference = mgr._getInference(
InferenceElement.multiStepPredictions)
assert inference is None or inference[3] is None
else:
prediction = mgr._getInference(
InferenceElement.multiStepPredictions)[3]
assert prediction == iterNum + 2
def __init__(self, fn, iterations, historyBuffer = None):
random.seed(42)
self.nIterations = iterations
self.fn = fn
self.iterations = iter(xrange(iterations))
self.history = historyBuffer
firstRecord = fn(0)
fieldList = [(name, self.__getType(val), '')
for name, val in firstRecord.iteritems()]
self.__metaData = FieldMetaInfo.createListFromFileFieldList(fieldList)
def testEncoderWithSequenceAndResetFields(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('reset', FieldMetaType.integer,
FieldMetaSpecial.reset),
FieldMetaInfo('sid', FieldMetaType.string,
FieldMetaSpecial.sequence),
FieldMetaInfo('categories', FieldMetaType.list,
FieldMetaSpecial.category)
]
encoder = ModelRecordEncoder(fields=fields)
result = encoder.encode([
'rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5, 1, 99, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_1',
'timestamp': datetime(2010, 3, 1, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'sid': 99,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 99,
'_timestamp': datetime(2010, 3, 1, 0, 0),
'_timestampRecordIdx': None
})
def _testMetricsMgr():
print "*Testing Metrics Managers*..."
from nupic.data.fieldmeta import (FieldMetaInfo, FieldMetaType,
FieldMetaSpecial)
from nupic.frameworks.opf.metrics import MetricSpec
from nupic.frameworks.opf.opf_utils import ModelResult, SensorInput
onlineMetrics = (MetricSpec(metric="aae", inferenceElement='', \
field="consumption", params={}),)
print "TESTING METRICS MANAGER (BASIC PLUMBING TEST)..."
modelFieldMetaInfo = (FieldMetaInfo(name='temperature',
type=FieldMetaType.float,
special=FieldMetaSpecial.none),
FieldMetaInfo(name='consumption',
type=FieldMetaType.float,
special=FieldMetaSpecial.none))
# -----------------------------------------------------------------------
# Test to make sure that invalid InferenceElements are caught
try:
MetricsManager(metricSpecs=onlineMetrics,
fieldInfo=modelFieldMetaInfo,
inferenceType=InferenceType.TemporalNextStep)
except ValueError:
print "Caught bad inference element: PASS"
print
onlineMetrics = (MetricSpec(metric="aae",
inferenceElement=InferenceElement.prediction,
field="consumption",
params={}), )
temporalMetrics = MetricsManager(
metricSpecs=onlineMetrics,
fieldInfo=modelFieldMetaInfo,
inferenceType=InferenceType.TemporalNextStep)
inputs = [
{
'groundTruthRow': [9, 7],
'predictionsDict': {
InferenceType.TemporalNextStep: [12, 17]
}
},
{
'groundTruthRow': [12, 17],
'predictionsDict': {
InferenceType.TemporalNextStep: [14, 19]
}
},
{
'groundTruthRow': [14, 20],
'predictionsDict': {
InferenceType.TemporalNextStep: [16, 21]
}
},
{
'groundTruthRow': [9, 7],
'predictionsDict': {
InferenceType.TemporalNextStep: None
}
},
]
for element in inputs:
groundTruthRow = element['groundTruthRow']
tPredictionRow = element['predictionsDict'][
InferenceType.TemporalNextStep]
result = ModelResult(sensorInput=SensorInput(dataRow=groundTruthRow,
dataEncodings=None,
sequenceReset=0,
category=None),
inferences={'prediction': tPredictionRow})
temporalMetrics.update(result)
assert temporalMetrics.getMetrics().values()[0] == 15.0 / 3.0, \
"Expected %f, got %f" %(15.0/3.0,
temporalMetrics.getMetrics().values()[0])
print "ok"
return
def testModelSwapper(self):
"""Simple end-to-end test of the model swapper system."""
modelSchedulerSubprocess = self._startModelSchedulerSubprocess()
self.addCleanup(lambda: modelSchedulerSubprocess.kill() if
modelSchedulerSubprocess.returncode is None else None)
modelID = "foobar"
resultBatches = []
with ModelSwapperInterface() as swapperAPI:
possibleModels = getScalarMetricWithTimeOfDayParams(metricData=[0],
minVal=0,
maxVal=1000)
# Submit requests including a model creation command and two data rows.
args = possibleModels[0]
args["inputRecordSchema"] = (
FieldMetaInfo("c0", FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo("c1", FieldMetaType.float,
FieldMetaSpecial.none),
)
# Define the model
_LOGGER.info("Defining the model")
swapperAPI.defineModel(modelID=modelID,
args=args,
commandID="defineModelCmd1")
# Attempt to define the same model again
_LOGGER.info("Defining the model again")
swapperAPI.defineModel(modelID=modelID,
args=args,
commandID="defineModelCmd2")
# Send input rows to the model
inputRows = [
ModelInputRow(
rowID="rowfoo",
data=[datetime.datetime(2013, 5, 23, 8, 13, 00), 5.3]),
ModelInputRow(
rowID="rowbar",
data=[datetime.datetime(2013, 5, 23, 8, 13, 15), 2.4]),
]
_LOGGER.info("Submitting batch of %d input rows...",
len(inputRows))
swapperAPI.submitRequests(modelID=modelID, requests=inputRows)
_LOGGER.info("These models have pending input: %s",
swapperAPI.getModelsWithInputPending())
# Retrieve all results.
# NOTE: We collect results via background thread to avoid
# deadlocking the test runner in the event consuming blocks unexpectedly
_LOGGER.info("Reading all batches of results...")
numBatchesExpected = 3
resultBatches.extend(
self._consumeResults(numBatchesExpected, timeout=20))
self.assertEqual(len(resultBatches), numBatchesExpected)
with MessageBusConnector() as bus:
# The results message queue should be empty now
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
# Delete the model
_LOGGER.info("Deleting the model")
swapperAPI.deleteModel(modelID=modelID,
commandID="deleteModelCmd1")
_LOGGER.info("Waiting for model deletion result")
resultBatches.extend(self._consumeResults(1, timeout=20))
self.assertEqual(len(resultBatches), 4)
with MessageBusConnector() as bus:
# The results message queue should be empty now
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
# The model input queue should be deleted now
self.assertFalse(
bus.isMessageQeueuePresent(
swapperAPI._getModelInputQName(modelID=modelID)))
# Try deleting the model again, to make sure there are no exceptions
_LOGGER.info("Attempting to delete the model again")
swapperAPI.deleteModel(modelID=modelID,
commandID="deleteModelCmd1")
# Verify results
# First result batch should be the first defineModel result
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "defineModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "defineModelCmd1")
# The second result batch should for the second defineModel result for the
# same model
batch = resultBatches[1]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "defineModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "defineModelCmd2")
# The third batch should be for the two input rows
batch = resultBatches[2]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), len(inputRows))
for inputRow, result in zip(inputRows, batch.objects):
self.assertIsInstance(result, ModelInferenceResult)
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.rowID, inputRow.rowID)
self.assertIsInstance(result.anomalyScore, float)
# The fourth batch should be for the "deleteModel"
batch = resultBatches[3]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "deleteModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "deleteModelCmd1")
# Signal Model Scheduler Service subprocess to shut down and wait for it
waitResult = dict()
def runWaiterThread():
try:
waitResult["returnCode"] = modelSchedulerSubprocess.wait()
except:
_LOGGER.exception(
"Waiting for modelSchedulerSubprocess failed")
waitResult["exceptionInfo"] = traceback.format_exc()
raise
return
modelSchedulerSubprocess.terminate()
waiterThread = threading.Thread(target=runWaiterThread)
waiterThread.setDaemon(True)
waiterThread.start()
waiterThread.join(timeout=30)
self.assertFalse(waiterThread.isAlive())
self.assertEqual(waitResult["returnCode"], 0, msg=repr(waitResult))
def testFieldMetaInfoRaisesValueErrorOnInvalidFieldType(self):
with self.assertRaises(ValueError):
FieldMetaInfo("fieldName", "bogus-type", FieldMetaSpecial.none)
def testBasic(self):
"""Runs basic FileRecordStream tests."""
filename = _getTempFileName()
# Write a standard file
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('reset', FieldMetaType.integer,
FieldMetaSpecial.reset),
FieldMetaInfo('sid', FieldMetaType.string,
FieldMetaSpecial.sequence),
FieldMetaInfo('categoryField', FieldMetaType.integer,
FieldMetaSpecial.category),
]
fieldNames = [
'name', 'timestamp', 'integer', 'real', 'reset', 'sid',
'categoryField'
]
print 'Creating temp file:', filename
with FileRecordStream(streamID=filename, write=True,
fields=fields) as s:
self.assertEqual(0, s.getDataRowCount())
# Records
records = ([
'rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5, 1, 'seq-1', 10
], [
'rec_2',
datetime(day=2, month=3, year=2010), 8, 7.5, 0, 'seq-1', 11
], [
'rec_3',
datetime(day=3, month=3, year=2010), 12, 8.5, 0, 'seq-1', 12
])
self.assertEqual(fields, s.getFields())
self.assertEqual(0, s.getNextRecordIdx())
print 'Writing records ...'
for r in records:
print list(r)
s.appendRecord(list(r))
self.assertEqual(3, s.getDataRowCount())
recordsBatch = ([
'rec_4',
datetime(day=4, month=3, year=2010), 2, 9.5, 1, 'seq-1', 13
], [
'rec_5',
datetime(day=5, month=3, year=2010), 6, 10.5, 0, 'seq-1', 14
], [
'rec_6',
datetime(day=6, month=3, year=2010), 11, 11.5, 0, 'seq-1', 15
])
print 'Adding batch of records...'
for rec in recordsBatch:
print rec
s.appendRecords(recordsBatch)
self.assertEqual(6, s.getDataRowCount())
with FileRecordStream(filename) as s:
# Read the standard file
self.assertEqual(6, s.getDataRowCount())
self.assertEqual(fieldNames, s.getFieldNames())
# Note! this is the number of records read so far
self.assertEqual(0, s.getNextRecordIdx())
readStats = s.getStats()
print 'Got stats:', readStats
expectedStats = {
'max': [None, None, 12, 11.5, 1, None, 15],
'min': [None, None, 2, 6.5, 0, None, 10]
}
self.assertEqual(expectedStats, readStats)
readRecords = []
print 'Reading records ...'
while True:
r = s.getNextRecord()
print r
if r is None:
break
readRecords.append(r)
allRecords = records + recordsBatch
for r1, r2 in zip(allRecords, readRecords):
self.assertEqual(r1, r2)
def __init__(self,
streamID,
write=False,
fields=None,
missingValues=None,
bookmark=None,
includeMS=True,
firstRecord=None):
""" Constructor
streamID:
CSV file name, input or output
write:
True or False, open for writing if True
fields:
a list of nupic.data.fieldmeta.FieldMetaInfo field descriptors, only
applicable when write==True
missingValues:
what missing values should be replaced with?
bookmark:
a reference to the previous reader, if passed in, the records will be
returned starting from the point where bookmark was requested. Either
bookmark or firstRecord can be specified, not both. If bookmark is used,
then firstRecord MUST be None.
includeMS:
If false, the microseconds portion is not included in the
generated output file timestamp fields. This makes it compatible
with reading in from Excel.
firstRecord:
0-based index of the first record to start reading from. Either bookmark
or firstRecord can be specified, not both. If bookmark is used, then
firstRecord MUST be None.
Each field is a 3-tuple (name, type, special or '')
The name is the name of the field. The type is one of: 'string', 'datetime',
'int', 'float', 'bool' The special is either empty or one of S, R, T, C that
designate their field as the sequenceId, reset, timestamp, or category.
There can be at most one of each. There may be multiple fields of type
datetime, but no more than one of them may be the timestamp field (T). The
sequence id field must be either a string or an int. The reset field must be
an int (and must contain 0 or 1). The category field must be an int.
The FileRecordStream iterates over the field names, types and specials and
stores the information.
"""
# Call superclass constructor
super(FileRecordStream, self).__init__()
# Only bookmark or firstRow can be specified, not both
if bookmark is not None and firstRecord is not None:
raise RuntimeError(
"Only bookmark or firstRecord can be specified, not both")
if fields is None:
fields = []
if missingValues is None:
missingValues = ['']
# We'll be operating on csvs with arbitrarily long fields
size = 2**27
csv.field_size_limit(size)
self._filename = streamID
# We can't guarantee what system files are coming from, use universal
# newlines
self._write = write
self._mode = self._FILE_WRITE_MODE if write else self._FILE_READ_MODE
self._file = open(self._filename, self._mode)
self._sequences = set()
self.rewindAtEOF = False
if write:
assert fields is not None
assert isinstance(fields, (tuple, list))
# Verify all fields are 3-tuple
assert all(
isinstance(f, (tuple, FieldMetaInfo)) and len(f) == 3
for f in fields)
names, types, specials = zip(*fields)
self._writer = csv.writer(self._file)
else:
os.linesep = '\n' # make sure readline() works on windows too.
# Read header lines
self._reader = csv.reader(self._file,
dialect='excel',
quoting=csv.QUOTE_NONE)
try:
names = [n.strip() for n in self._reader.next()]
except:
raise Exception('The header line of the file %s contained a NULL byte' \
% self._filename)
types = [t.strip() for t in self._reader.next()]
specials = [s.strip() for s in self._reader.next()]
# If there are no specials, this means there was a blank line
if len(specials) == 0:
specials = [""]
if not (len(names) == len(types) == len(specials)):
raise Exception('Invalid file format: different number of fields '
'in the header rows of file %s (%d, %d, %d)' %
(streamID, len(names), len(types), len(specials)))
# Verify standard file format
allowedTypes = ('string', 'datetime', 'int', 'float', 'bool', 'sdr')
for i, t in enumerate(types):
# This is a temporary hack for the Precog milestone, which passes in a
# type 'address' for address fields. Here we simply map the type "address"
# to "string".
if t == 'address':
types[i] = 'string'
t = 'string'
if t not in allowedTypes:
raise Exception(
'Invalid file format for "%s" - field type "%s" '
'not one of %s ' % (self._filename, t, allowedTypes))
for s in specials:
if s not in ('', 'T', 'R', 'S', 'C', 'L'):
raise Exception(
'Invalid file format. \'%s\' is not a valid special '
'flag' % s)
self._fields = [
FieldMetaInfo(*attrs) for attrs in zip(names, types, specials)
]
self._fieldCount = len(self._fields)
# Keep track on how many records have been read/written
self._recordCount = 0
self._timeStampIdx = specials.index('T') if 'T' in specials else None
self._resetIdx = specials.index('R') if 'R' in specials else None
self._sequenceIdIdx = specials.index('S') if 'S' in specials else None
self._categoryIdx = specials.index('C') if 'C' in specials else None
self._learningIdx = specials.index('L') if 'L' in specials else None
# keep track of the current sequence
self._currSequence = None
self._currTime = None
if self._timeStampIdx:
assert types[self._timeStampIdx] == 'datetime'
if self._sequenceIdIdx:
assert types[self._sequenceIdIdx] in ('string', 'int')
if self._resetIdx:
assert types[self._resetIdx] == 'int'
if self._categoryIdx:
assert types[self._categoryIdx] == 'int'
if self._learningIdx:
assert types[self._learningIdx] == 'int'
# Convert the types to the actual types in order to convert the strings
if self._mode == self._FILE_READ_MODE:
m = dict(int=intOrNone,
float=floatOrNone,
bool=parseBool,
string=unescape,
datetime=parseTimestamp,
sdr=parseSdr)
else:
if includeMS:
datetimeFunc = serializeTimestamp
else:
datetimeFunc = serializeTimestampNoMS
m = dict(int=str,
float=str,
string=escape,
bool=str,
datetime=datetimeFunc,
sdr=serializeSdr)
self._adapters = [m[t] for t in types]
self._missingValues = missingValues
#
# If the bookmark is set, we need to skip over first N records
#
if bookmark is not None:
rowsToSkip = self._getStartRow(bookmark)
elif firstRecord is not None:
rowsToSkip = firstRecord
else:
rowsToSkip = 0
while rowsToSkip > 0:
self.next()
rowsToSkip -= 1
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
def __init__(self,
streamDef,
bookmark=None,
saveOutput=False,
isBlocking=True,
maxTimeout=0,
eofOnTimeout=False):
""" Base class constructor, performs common initialization
Parameters:
----------------------------------------------------------------
streamDef: The stream definition, potentially containing multiple sources
(not supported yet). See
/nupic/frameworks/opf/jsonschema/stream_def.json for the format
of this dict
bookmark: Bookmark to start reading from. This overrides the first_record
field of the streamDef if provided.
saveOutput: If true, save the output to a csv file in a temp directory.
The path to the generated file can be found in the log
output.
isBlocking: should read operation block *forever* if the next row of data
is not available, but the stream is not marked as 'completed'
yet?
maxTimeout: if isBlocking is False, max seconds to wait for more data before
timing out; ignored when isBlocking is True.
eofOnTimeout: If True and we get a read timeout (isBlocking must be False
to get read timeouts), assume we've reached the end of the
input and produce the last aggregated record, if one can be
completed.
"""
# Call superclass constructor
super(StreamReader, self).__init__()
loggerPrefix = 'com.numenta.nupic.data.StreamReader'
self._logger = logging.getLogger(loggerPrefix)
jsonhelpers.validate(streamDef,
schemaPath=pkg_resources.resource_filename(
jsonschema.__name__, "stream_def.json"))
assert len(
streamDef['streams']) == 1, "Only 1 source stream is supported"
# Save constructor args
sourceDict = streamDef['streams'][0]
self._recordCount = 0
self._eofOnTimeout = eofOnTimeout
self._logger.debug('Reading stream with the def: %s', sourceDict)
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
# ---------------------------------------------------------------------
# Get the stream definition params
# Limiting window of the stream. It would not return any records until
# 'first_record' ID is read (or very first with the ID above that). The
# stream will return EOS once it reads record with ID 'last_record' or
# above (NOTE: the name 'lastRecord' is misleading because it is NOT
# inclusive).
firstRecordIdx = sourceDict.get('first_record', None)
self._sourceLastRecordIdx = sourceDict.get('last_record', None)
# If a bookmark was given, then override first_record from the stream
# definition.
if bookmark is not None:
firstRecordIdx = None
# Column names must be provided in the streamdef json
# Special case is ['*'], meaning all available names from the record stream
self._streamFieldNames = sourceDict.get('columns', None)
if self._streamFieldNames != None and self._streamFieldNames[0] == '*':
self._needFieldsFiltering = False
else:
self._needFieldsFiltering = True
# Types must be specified in streamdef json, or in case of the
# file_recod_stream types could be implicit from the file
streamFieldTypes = sourceDict.get('types', None)
self._logger.debug('Types from the def: %s', streamFieldTypes)
# Validate that all types are valid
if streamFieldTypes is not None:
for dataType in streamFieldTypes:
assert FieldMetaType.isValid(dataType)
# Reset, sequence and time fields might be provided by streamdef json
streamResetFieldName = streamDef.get('resetField', None)
streamTimeFieldName = streamDef.get('timeField', None)
streamSequenceFieldName = streamDef.get('sequenceIdField', None)
self._logger.debug('r, t, s fields: %s, %s, %s', streamResetFieldName,
streamTimeFieldName, streamSequenceFieldName)
# =======================================================================
# Open up the underlying record store
dataUrl = sourceDict.get('source', None)
assert dataUrl is not None
self._recordStore = self._openStream(dataUrl, isBlocking, maxTimeout,
bookmark, firstRecordIdx)
assert self._recordStore is not None
# =======================================================================
# Prepare the data structures we need for returning just the fields
# the caller wants from each record
recordStoreFields = self._recordStore.getFields()
self._recordStoreFieldNames = self._recordStore.getFieldNames()
if not self._needFieldsFiltering:
self._streamFieldNames = self._recordStoreFieldNames
# Build up the field definitions for each field. This is a list of tuples
# of (name, type, special)
self._streamFields = []
for dstIdx, name in enumerate(self._streamFieldNames):
if name not in self._recordStoreFieldNames:
raise RuntimeError(
"The column '%s' from the stream definition "
"is not present in the underlying stream which has the following "
"columns: %s" % (name, self._recordStoreFieldNames))
fieldIdx = self._recordStoreFieldNames.index(name)
fieldType = recordStoreFields[fieldIdx].type
fieldSpecial = recordStoreFields[fieldIdx].special
# If the types or specials were defined in the stream definition,
# then override what was found in the record store
if streamFieldTypes is not None:
fieldType = streamFieldTypes[dstIdx]
if streamResetFieldName is not None and streamResetFieldName == name:
fieldSpecial = FieldMetaSpecial.reset
if streamTimeFieldName is not None and streamTimeFieldName == name:
fieldSpecial = FieldMetaSpecial.timestamp
if (streamSequenceFieldName is not None
and streamSequenceFieldName == name):
fieldSpecial = FieldMetaSpecial.sequence
self._streamFields.append(
FieldMetaInfo(name, fieldType, fieldSpecial))
# ========================================================================
# Create the aggregator which will handle aggregation of records before
# returning them.
self._aggregator = Aggregator(
aggregationInfo=streamDef.get('aggregation', None),
inputFields=recordStoreFields,
timeFieldName=streamDef.get('timeField', None),
sequenceIdFieldName=streamDef.get('sequenceIdField', None),
resetFieldName=streamDef.get('resetField', None))
# We rely on the aggregator to tell us the bookmark of the last raw input
# that contributed to the aggregated record
self._aggBookmark = None
# Compute the aggregation period in terms of months and seconds
if 'aggregation' in streamDef:
self._aggMonthsAndSeconds = nupic.support.aggregationToMonthsSeconds(
streamDef.get('aggregation'))
else:
self._aggMonthsAndSeconds = None
# ========================================================================
# Are we saving the generated output to a csv?
if saveOutput:
tmpDir = tempfile.mkdtemp()
outFilename = os.path.join(tmpDir, "generated_output.csv")
self._logger.info(
"StreamReader: Saving generated records to: '%s'" %
outFilename)
self._writer = FileRecordStream(streamID=outFilename,
write=True,
fields=self._streamFields)
else:
self._writer = None
def testCloneModel(self):
modelSchedulerSubprocess = self._startModelSchedulerSubprocess()
self.addCleanup(lambda: modelSchedulerSubprocess.kill() if
modelSchedulerSubprocess.returncode is None else None)
modelID = "abc"
destModelID = "def"
resultBatches = []
with ModelSwapperInterface() as swapperAPI:
args = getScalarMetricWithTimeOfDayAnomalyParams(metricData=[0],
minVal=0,
maxVal=1000)
# Submit requests including a model creation command and two data rows.
args["inputRecordSchema"] = (
FieldMetaInfo("c0", FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo("c1", FieldMetaType.float,
FieldMetaSpecial.none),
)
# Define the model
_LOGGER.info("Defining the model")
swapperAPI.defineModel(modelID=modelID,
args=args,
commandID="defineModelCmd1")
resultBatches.extend(self._consumeResults(1, timeout=20))
self.assertEqual(len(resultBatches), 1)
# Clone the just-defined model
_LOGGER.info("Cloning model")
swapperAPI.cloneModel(modelID,
destModelID,
commandID="cloneModelCmd1")
resultBatches.extend(self._consumeResults(1, timeout=20))
self.assertEqual(len(resultBatches), 2)
# Send input rows to the clone
inputRows = [
ModelInputRow(
rowID="rowfoo",
data=[datetime.datetime(2013, 5, 23, 8, 13, 00), 5.3]),
ModelInputRow(
rowID="rowbar",
data=[datetime.datetime(2013, 5, 23, 8, 13, 15), 2.4]),
]
_LOGGER.info("Submitting batch of %d input rows...",
len(inputRows))
swapperAPI.submitRequests(modelID=destModelID, requests=inputRows)
_LOGGER.info("These models have pending input: %s",
swapperAPI.getModelsWithInputPending())
resultBatches.extend(self._consumeResults(1, timeout=20))
self.assertEqual(len(resultBatches), 3)
with MessageBusConnector() as bus:
# The results message queue should be empty now
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
# Delete the model
_LOGGER.info("Deleting the model")
swapperAPI.deleteModel(modelID=destModelID,
commandID="deleteModelCmd1")
_LOGGER.info("Waiting for model deletion result")
resultBatches.extend(self._consumeResults(1, timeout=20))
self.assertEqual(len(resultBatches), 4)
with MessageBusConnector() as bus:
# The results message queue should be empty now
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
# The model input queue should be deleted now
self.assertFalse(
bus.isMessageQeueuePresent(
swapperAPI._getModelInputQName(modelID=destModelID)))
# Verify results
# First result batch should be the defineModel result
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "defineModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "defineModelCmd1")
# The second result batch should for the cloneModel result
batch = resultBatches[1]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "cloneModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "cloneModelCmd1")
# The third batch should be for the two input rows
batch = resultBatches[2]
self.assertEqual(batch.modelID, destModelID)
self.assertEqual(len(batch.objects), len(inputRows))
for inputRow, result in zip(inputRows, batch.objects):
self.assertIsInstance(result, ModelInferenceResult)
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.rowID, inputRow.rowID)
self.assertIsInstance(result.anomalyScore, float)
self.assertIsInstance(result.multiStepBestPredictions, dict)
# The fourth batch should be for the "deleteModel"
batch = resultBatches[3]
self.assertEqual(batch.modelID, destModelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "deleteModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "deleteModelCmd1")
# Signal Model Scheduler Service subprocess to shut down and wait for it
waitResult = dict()
def runWaiterThread():
try:
waitResult["returnCode"] = modelSchedulerSubprocess.wait()
except:
_LOGGER.exception(
"Waiting for modelSchedulerSubprocess failed")
waitResult["exceptionInfo"] = traceback.format_exc()
raise
return
modelSchedulerSubprocess.terminate()
waiterThread = threading.Thread(target=runWaiterThread)
waiterThread.setDaemon(True)
waiterThread.start()
waiterThread.join(timeout=30)
self.assertFalse(waiterThread.isAlive())
self.assertEqual(waitResult["returnCode"], 0, msg=repr(waitResult))
def _auxTestRunModelWithFullThenIncrementalCheckpoints(
self, classifierEnabled):
modelID = "foobar"
checkpointMgr = model_checkpoint_mgr.ModelCheckpointMgr()
args = getScalarMetricWithTimeOfDayAnomalyParams(metricData=[0],
minVal=0,
maxVal=1000)
args["modelConfig"]["modelParams"]["clEnable"] = classifierEnabled
# Submit requests including a model creation command and two data rows.
args["inputRecordSchema"] = (
FieldMetaInfo("c0", FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo("c1", FieldMetaType.float, FieldMetaSpecial.none),
)
with ModelSwapperInterface() as swapperAPI:
# Define the model
_LOGGER.info("Defining the model")
swapperAPI.defineModel(modelID=modelID,
args=args,
commandID="defineModelCmd1")
# Send input rows to the model
inputRows = [
ModelInputRow(
rowID="rowfoo",
data=[datetime.datetime(2014, 5, 23, 8, 13, 00), 5.3]),
ModelInputRow(
rowID="rowbar",
data=[datetime.datetime(2014, 5, 23, 8, 13, 15), 2.4]),
]
_LOGGER.info(
"Submitting batch of %d input rows with ids=[%s..%s]...",
len(inputRows), inputRows[0].rowID, inputRows[-1].rowID)
swapperAPI.submitRequests(modelID=modelID, requests=inputRows)
# Run model_runner and collect results
with self._startModelRunnerSubprocess(
modelID) as modelRunnerProcess:
resultBatches = self._consumeResults(numExpectedBatches=2,
timeout=15)
self._waitForProcessToStopAndCheck(modelRunnerProcess)
with MessageBusConnector() as bus:
# The results message queue should be empty now
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
self.assertEqual(len(resultBatches), 2, repr(resultBatches))
# First result batch should be the first defineModel result
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "defineModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "defineModelCmd1")
# The second result batch should be for the two input rows
batch = resultBatches[1]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), len(inputRows))
for inputRow, result in zip(inputRows, batch.objects):
self.assertIsInstance(result, ModelInferenceResult)
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.rowID, inputRow.rowID)
self.assertIsInstance(result.anomalyScore, float)
if classifierEnabled:
self.assertIsInstance(result.multiStepBestPredictions,
dict)
else:
self.assertIsNone(result.multiStepBestPredictions)
# Verify model checkpoint
model = checkpointMgr.load(modelID)
del model
attrs = checkpointMgr.loadCheckpointAttributes(modelID)
self.assertIn(
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
self.assertEqual(len(attrs[
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME]),
2,
msg=repr(attrs))
self.assertNotIn(model_runner._ModelArchiver.
_INPUT_SAMPLES_SINCE_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
# Now, check incremental checkpointing
inputRows2 = [
ModelInputRow(
rowID=2,
data=[datetime.datetime(2014, 5, 23, 8, 13, 20), 2.7]),
ModelInputRow(
rowID=3,
data=[datetime.datetime(2014, 5, 23, 8, 13, 25), 3.9]),
]
_LOGGER.info(
"Submitting batch of %d input rows with ids=[%s..%s]...",
len(inputRows2), inputRows2[0].rowID, inputRows2[-1].rowID)
inputBatchID = swapperAPI.submitRequests(modelID=modelID,
requests=inputRows2)
with self._startModelRunnerSubprocess(
modelID) as modelRunnerProcess:
resultBatches = self._consumeResults(numExpectedBatches=1,
timeout=15)
self._waitForProcessToStopAndCheck(modelRunnerProcess)
with MessageBusConnector() as bus:
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), len(inputRows2))
for inputRow, result in zip(inputRows2, batch.objects):
self.assertIsInstance(result, ModelInferenceResult)
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.rowID, inputRow.rowID)
self.assertIsInstance(result.anomalyScore, float)
if classifierEnabled:
self.assertIsInstance(result.multiStepBestPredictions,
dict)
else:
self.assertIsNone(result.multiStepBestPredictions)
model = checkpointMgr.load(modelID)
del model
attrs = checkpointMgr.loadCheckpointAttributes(modelID)
self.assertIn(
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
self.assertSequenceEqual(attrs[
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME],
[inputBatchID],
msg=repr(attrs))
self.assertIn(model_runner._ModelArchiver.
_INPUT_SAMPLES_SINCE_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
self.assertSequenceEqual(
model_runner._ModelArchiver._decodeDataSamples(
attrs[model_runner._ModelArchiver.
_INPUT_SAMPLES_SINCE_CHECKPOINT_ATTR_NAME]),
[row.data for row in inputRows2],
msg=repr(attrs))
# Final run with incremental checkpointing
inputRows3 = [
ModelInputRow(
rowID=4,
data=[datetime.datetime(2014, 5, 23, 8, 13, 30), 4.7]),
ModelInputRow(
rowID=5,
data=[datetime.datetime(2014, 5, 23, 8, 13, 35), 5.9]),
]
_LOGGER.info(
"Submitting batch of %d input rows with ids=[%s..%s]...",
len(inputRows3), inputRows3[0].rowID, inputRows3[-1].rowID)
inputBatchID = swapperAPI.submitRequests(modelID=modelID,
requests=inputRows3)
with self._startModelRunnerSubprocess(
modelID) as modelRunnerProcess:
resultBatches = self._consumeResults(numExpectedBatches=1,
timeout=15)
self._waitForProcessToStopAndCheck(modelRunnerProcess)
with MessageBusConnector() as bus:
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), len(inputRows3))
for inputRow, result in zip(inputRows3, batch.objects):
self.assertIsInstance(result, ModelInferenceResult)
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.rowID, inputRow.rowID)
self.assertIsInstance(result.anomalyScore, float)
if classifierEnabled:
self.assertIsInstance(result.multiStepBestPredictions,
dict)
else:
self.assertIsNone(result.multiStepBestPredictions)
model = checkpointMgr.load(modelID)
del model
attrs = checkpointMgr.loadCheckpointAttributes(modelID)
self.assertIn(
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
self.assertSequenceEqual(attrs[
model_runner._ModelArchiver._BATCH_IDS_CHECKPOINT_ATTR_NAME],
[inputBatchID],
msg=repr(attrs))
self.assertIn(model_runner._ModelArchiver.
_INPUT_SAMPLES_SINCE_CHECKPOINT_ATTR_NAME,
attrs,
msg=repr(attrs))
self.assertSequenceEqual(
model_runner._ModelArchiver._decodeDataSamples(
attrs[model_runner._ModelArchiver.
_INPUT_SAMPLES_SINCE_CHECKPOINT_ATTR_NAME]),
[row.data for row in itertools.chain(inputRows2, inputRows3)],
msg=repr(attrs))
# Delete the model
_LOGGER.info("Deleting the model=%s", modelID)
swapperAPI.deleteModel(modelID=modelID,
commandID="deleteModelCmd1")
with self._startModelRunnerSubprocess(
modelID) as modelRunnerProcess:
resultBatches = self._consumeResults(numExpectedBatches=1,
timeout=15)
self._waitForProcessToStopAndCheck(modelRunnerProcess)
self.assertEqual(len(resultBatches), 1, repr(resultBatches))
# First result batch should be the first defineModel result
batch = resultBatches[0]
self.assertEqual(batch.modelID, modelID)
self.assertEqual(len(batch.objects), 1)
result = batch.objects[0]
self.assertIsInstance(result, ModelCommandResult)
self.assertEqual(result.method, "deleteModel")
self.assertEqual(result.status, htmengineerrno.SUCCESS)
self.assertEqual(result.commandID, "deleteModelCmd1")
with MessageBusConnector() as bus:
self.assertTrue(bus.isEmpty(swapperAPI._resultsQueueName))
# The model input queue should be deleted now
self.assertFalse(
bus.isMessageQeueuePresent(
swapperAPI._getModelInputQName(modelID=modelID)))
# The model checkpoint should be gone too
with self.assertRaises(model_checkpoint_mgr.ModelNotFound):
checkpointMgr.load(modelID)
with self.assertRaises(model_checkpoint_mgr.ModelNotFound):
checkpointMgr.loadModelDefinition(modelID)
with self.assertRaises(model_checkpoint_mgr.ModelNotFound):
checkpointMgr.loadCheckpointAttributes(modelID)
with self.assertRaises(model_checkpoint_mgr.ModelNotFound):
checkpointMgr.remove(modelID)
def __init__(self, streamID, write=False, fields=None, missingValues=None,
bookmark=None, includeMS=True, firstRecord=None):
super(FileRecordStream, self).__init__()
# Only bookmark or firstRow can be specified, not both
if bookmark is not None and firstRecord is not None:
raise RuntimeError(
"Only bookmark or firstRecord can be specified, not both")
if fields is None:
fields = []
if missingValues is None:
missingValues = ['']
# We'll be operating on csvs with arbitrarily long fields
size = 2**27
csv.field_size_limit(size)
self._filename = streamID
# We can't guarantee what system files are coming from, use universal
# newlines
self._write = write
self._mode = self._FILE_WRITE_MODE if write else self._FILE_READ_MODE
self._file = open(self._filename, self._mode)
self._sequences = set()
self.rewindAtEOF = False
if write:
assert fields is not None
assert isinstance(fields, (tuple, list))
# Verify all fields are 3-tuple
assert all(isinstance(f, (tuple, FieldMetaInfo)) and len(f) == 3
for f in fields)
names, types, specials = zip(*fields)
self._writer = csv.writer(self._file)
else:
# Read header lines
self._reader = csv.reader(self._file, dialect="excel")
try:
names = [n.strip() for n in self._reader.next()]
except:
raise Exception('The header line of the file %s contained a NULL byte' \
% self._filename)
types = [t.strip() for t in self._reader.next()]
specials = [s.strip() for s in self._reader.next()]
# If there are no specials, this means there was a blank line
if len(specials) == 0:
specials=[""]
if not len(names) == len(types) == len(specials):
raise Exception('Invalid file format: different number of fields '
'in the header rows of file %s (%d, %d, %d)' %
(streamID, len(names), len(types), len(specials)))
# Verify standard file format
for t in types:
if not FieldMetaType.isValid(t):
raise Exception('Invalid file format for "%s" - field type "%s" '
'not a valid FieldMetaType' % (self._filename, t,))
for s in specials:
if not FieldMetaSpecial.isValid(s):
raise Exception('Invalid file format. \'%s\' is not a valid special '
'flag' % s)
self._fields = [FieldMetaInfo(*attrs)
for attrs in zip(names, types, specials)]
self._fieldCount = len(self._fields)
# Keep track on how many records have been read/written
self._recordCount = 0
self._timeStampIdx = (specials.index(FieldMetaSpecial.timestamp)
if FieldMetaSpecial.timestamp in specials else None)
self._resetIdx = (specials.index(FieldMetaSpecial.reset)
if FieldMetaSpecial.reset in specials else None)
self._sequenceIdIdx = (specials.index(FieldMetaSpecial.sequence)
if FieldMetaSpecial.sequence in specials else None)
self._categoryIdx = (specials.index(FieldMetaSpecial.category)
if FieldMetaSpecial.category in specials else None)
self._learningIdx = (specials.index(FieldMetaSpecial.learning)
if FieldMetaSpecial.learning in specials else None)
# keep track of the current sequence
self._currSequence = None
self._currTime = None
if self._timeStampIdx:
assert types[self._timeStampIdx] == FieldMetaType.datetime
if self._sequenceIdIdx:
assert types[self._sequenceIdIdx] in (FieldMetaType.string,
FieldMetaType.integer)
if self._resetIdx:
assert types[self._resetIdx] == FieldMetaType.integer
if self._categoryIdx:
assert types[self._categoryIdx] in (FieldMetaType.list,
FieldMetaType.integer)
if self._learningIdx:
assert types[self._learningIdx] == FieldMetaType.integer
# Convert the types to the actual types in order to convert the strings
if self._mode == self._FILE_READ_MODE:
m = {FieldMetaType.integer: intOrNone,
FieldMetaType.float: floatOrNone,
FieldMetaType.boolean: parseBool,
FieldMetaType.string: unescape,
FieldMetaType.datetime: parseTimestamp,
FieldMetaType.sdr: parseSdr,
FieldMetaType.list: parseStringList}
else:
if includeMS:
datetimeFunc = serializeTimestamp
else:
datetimeFunc = serializeTimestampNoMS
m = {FieldMetaType.integer: str,
FieldMetaType.float: str,
FieldMetaType.string: escape,
FieldMetaType.boolean: str,
FieldMetaType.datetime: datetimeFunc,
FieldMetaType.sdr: serializeSdr,
FieldMetaType.list: stripList}
self._adapters = [m[t] for t in types]
self._missingValues = missingValues
#
# If the bookmark is set, we need to skip over first N records
#
if bookmark is not None:
rowsToSkip = self._getStartRow(bookmark)
elif firstRecord is not None:
rowsToSkip = firstRecord
else:
rowsToSkip = 0
while rowsToSkip > 0:
self.next()
rowsToSkip -= 1
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
def testEncoderWithSequenceFieldWithoutResetField(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('sid', FieldMetaType.string,
FieldMetaSpecial.sequence),
FieldMetaInfo('categories', FieldMetaType.list,
FieldMetaSpecial.category)
]
encoder = ModelRecordEncoder(fields=fields)
# _reset should be 1 the first time
result = encoder.encode([
'rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5, 99, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_1',
'timestamp': datetime(2010, 3, 1, 0, 0),
'integer': 5,
'real': 6.5,
'sid': 99,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 99,
'_timestamp': datetime(2010, 3, 1, 0, 0),
'_timestampRecordIdx': None
})
# _reset should be 0 when same sequence id is repeated
result = encoder.encode([
'rec_2',
datetime(day=2, month=3, year=2010), 5, 6.5, 99, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_2',
'timestamp': datetime(2010, 3, 2, 0, 0),
'integer': 5,
'real': 6.5,
'sid': 99,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 0,
'_sequenceId': 99,
'_timestamp': datetime(2010, 3, 2, 0, 0),
'_timestampRecordIdx': None
})
# _reset should be 1 when sequence id changes
result = encoder.encode([
'rec_3',
datetime(day=2, month=3, year=2010), 5, 6.5, 100, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_3',
'timestamp': datetime(2010, 3, 2, 0, 0),
'integer': 5,
'real': 6.5,
'sid': 100,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 100,
'_timestamp': datetime(2010, 3, 2, 0, 0),
'_timestampRecordIdx': None
})
def testEncoderWithResetFieldWithoutSequenceField(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('reset', FieldMetaType.integer,
FieldMetaSpecial.reset),
FieldMetaInfo('categories', FieldMetaType.list,
FieldMetaSpecial.category)
]
encoder = ModelRecordEncoder(fields=fields)
result = encoder.encode([
'rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5, 1, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_1',
'timestamp': datetime(2010, 3, 1, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 1, 0, 0),
'_timestampRecordIdx': None
})
# One more time to verify incremeting sequence id
result = encoder.encode([
'rec_2',
datetime(day=2, month=3, year=2010), 5, 6.5, 1, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_2',
'timestamp': datetime(2010, 3, 2, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 1,
'_timestamp': datetime(2010, 3, 2, 0, 0),
'_timestampRecordIdx': None
})
# Now with reset turned off, expecting no change to sequence id
result = encoder.encode([
'rec_3',
datetime(day=3, month=3, year=2010), 5, 6.5, 0, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_3',
'timestamp': datetime(2010, 3, 3, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 0,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 0,
'_sequenceId': 1,
'_timestamp': datetime(2010, 3, 3, 0, 0),
'_timestampRecordIdx': None
})
# Now check that rewind resets sequence id
encoder.rewind()
result = encoder.encode([
'rec_4',
datetime(day=4, month=3, year=2010), 5, 6.5, 1, [0, 1, 2]
])
self.assertEqual(
result, {
'name': 'rec_4',
'timestamp': datetime(2010, 3, 4, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 4, 0, 0),
'_timestampRecordIdx': None
})
def __init__(self,
streamID,
write=False,
fields=None,
missingValues=None,
bookmark=None,
includeMS=True,
firstRecord=None):
"""
streamID:
CSV file name, input or output
write:
True or False, open for writing if True
fields:
a list of nupic.data.fieldmeta.FieldMetaInfo field descriptors, only
applicable when write==True
missingValues:
what missing values should be replaced with?
bookmark:
a reference to the previous reader, if passed in, the records will be
returned starting from the point where bookmark was requested. Either
bookmark or firstRecord can be specified, not both. If bookmark is used,
then firstRecord MUST be None.
includeMS:
If false, the microseconds portion is not included in the
generated output file timestamp fields. This makes it compatible
with reading in from Excel.
firstRecord:
0-based index of the first record to start reading from. Either bookmark
or firstRecord can be specified, not both. If bookmark is used, then
firstRecord MUST be None.
Each field is a 3-tuple (name, type, special or FieldMetaSpecial.none)
The name is the name of the field. The type is one of the constants in
`FieldMetaType`. The special is one of the `FieldMetaSpecial` values
that designate their field as the sequenceId, reset, timestamp, or category.
With exception of multiple categories, there can be at most one of each.
There may be multiple fields of type datetime, but no more than one of them
may be the timestamp field (FieldMetaSpecial.timestamp). The sequence id
field must be either a string or an int. The reset field must be an int (and
must contain 0 or 1).
The category field must be an int or space-separated list of ints, where
the former represents single-label classification and the latter is for
multi-label classification (e.g. "1 3 4" designates a record for labels 1,
3, and 4). The number of categories is allowed to vary record to record;
sensor regions represent non-categories with -1, thus the category values
must be >= 0.
The FileRecordStream iterates over the field names, types and specials and
stores the information.
"""
super(FileRecordStream, self).__init__()
# Only bookmark or firstRow can be specified, not both
if bookmark is not None and firstRecord is not None:
raise RuntimeError(
"Only bookmark or firstRecord can be specified, not both")
if fields is None:
fields = []
if missingValues is None:
missingValues = ['']
# We'll be operating on csvs with arbitrarily long fields
size = 2**27
csv.field_size_limit(size)
self._filename = streamID
# We can't guarantee what system files are coming from, use universal
# newlines
self._write = write
self._mode = self._FILE_WRITE_MODE if write else self._FILE_READ_MODE
self._file = open(self._filename, self._mode)
self._sequences = set()
self.rewindAtEOF = False
if write:
assert fields is not None
assert isinstance(fields, (tuple, list))
# Verify all fields are 3-tuple
assert all(
isinstance(f, (tuple, FieldMetaInfo)) and len(f) == 3
for f in fields)
names, types, specials = zip(*fields)
self._writer = csv.writer(self._file)
else:
# Read header lines
self._reader = csv.reader(self._file, dialect="excel")
try:
names = [n.strip() for n in self._reader.next()]
except:
raise Exception('The header line of the file %s contained a NULL byte' \
% self._filename)
types = [t.strip() for t in self._reader.next()]
specials = [s.strip() for s in self._reader.next()]
# If there are no specials, this means there was a blank line
if len(specials) == 0:
specials = [""]
if not len(names) == len(types) == len(specials):
raise Exception('Invalid file format: different number of fields '
'in the header rows of file %s (%d, %d, %d)' %
(streamID, len(names), len(types), len(specials)))
# Verify standard file format
for t in types:
if not FieldMetaType.isValid(t):
raise Exception(
'Invalid file format for "%s" - field type "%s" '
'not a valid FieldMetaType' % (
self._filename,
t,
))
for s in specials:
if not FieldMetaSpecial.isValid(s):
raise Exception(
'Invalid file format. \'%s\' is not a valid special '
'flag' % s)
self._fields = [
FieldMetaInfo(*attrs) for attrs in zip(names, types, specials)
]
self._fieldCount = len(self._fields)
# Keep track on how many records have been read/written
self._recordCount = 0
self._timeStampIdx = (specials.index(FieldMetaSpecial.timestamp) if
FieldMetaSpecial.timestamp in specials else None)
self._resetIdx = (specials.index(FieldMetaSpecial.reset)
if FieldMetaSpecial.reset in specials else None)
self._sequenceIdIdx = (specials.index(FieldMetaSpecial.sequence) if
FieldMetaSpecial.sequence in specials else None)
self._categoryIdx = (specials.index(FieldMetaSpecial.category) if
FieldMetaSpecial.category in specials else None)
self._learningIdx = (specials.index(FieldMetaSpecial.learning) if
FieldMetaSpecial.learning in specials else None)
# keep track of the current sequence
self._currSequence = None
self._currTime = None
if self._timeStampIdx:
assert types[self._timeStampIdx] == FieldMetaType.datetime
if self._sequenceIdIdx:
assert types[self._sequenceIdIdx] in (FieldMetaType.string,
FieldMetaType.integer)
if self._resetIdx:
assert types[self._resetIdx] == FieldMetaType.integer
if self._categoryIdx:
assert types[self._categoryIdx] in (FieldMetaType.list,
FieldMetaType.integer)
if self._learningIdx:
assert types[self._learningIdx] == FieldMetaType.integer
# Convert the types to the actual types in order to convert the strings
if self._mode == self._FILE_READ_MODE:
m = {
FieldMetaType.integer: intOrNone,
FieldMetaType.float: floatOrNone,
FieldMetaType.boolean: parseBool,
FieldMetaType.string: unescape,
FieldMetaType.datetime: parseTimestamp,
FieldMetaType.sdr: parseSdr,
FieldMetaType.list: parseStringList
}
else:
if includeMS:
datetimeFunc = serializeTimestamp
else:
datetimeFunc = serializeTimestampNoMS
m = {
FieldMetaType.integer: str,
FieldMetaType.float: str,
FieldMetaType.string: escape,
FieldMetaType.boolean: str,
FieldMetaType.datetime: datetimeFunc,
FieldMetaType.sdr: serializeSdr,
FieldMetaType.list: stripList
}
self._adapters = [m[t] for t in types]
self._missingValues = missingValues
#
# If the bookmark is set, we need to skip over first N records
#
if bookmark is not None:
rowsToSkip = self._getStartRow(bookmark)
elif firstRecord is not None:
rowsToSkip = firstRecord
else:
rowsToSkip = 0
while rowsToSkip > 0:
self.next()
rowsToSkip -= 1
# Dictionary to store record statistics (min and max of scalars for now)
self._stats = None
def testFieldMetaInfoRaisesValueErrorOnInvalidFieldSpecial(self):
with self.assertRaises(ValueError):
FieldMetaInfo("fieldName", FieldMetaType.integer, "bogus-special")
def __openDatafile(self, modelResult):
"""Open the data file and write the header row"""
# Write reset bit
resetFieldMeta = FieldMetaInfo(name="reset",
type=FieldMetaType.integer,
special=FieldMetaSpecial.reset)
self.__outputFieldsMeta.append(resetFieldMeta)
# -----------------------------------------------------------------------
# Write each of the raw inputs that go into the encoders
rawInput = modelResult.rawInput
rawFields = rawInput.keys()
rawFields.sort()
for field in rawFields:
if field.startswith('_') or field == 'reset':
continue
value = rawInput[field]
meta = FieldMetaInfo(name=field,
type=FieldMetaType.string,
special=FieldMetaSpecial.none)
self.__outputFieldsMeta.append(meta)
self._rawInputNames.append(field)
# -----------------------------------------------------------------------
# Handle each of the inference elements
for inferenceElement, value in modelResult.inferences.iteritems():
inferenceLabel = InferenceElement.getLabel(inferenceElement)
# TODO: Right now we assume list inferences are associated with
# The input field metadata
if type(value) in (list, tuple):
# Append input and prediction field meta-info
self.__outputFieldsMeta.extend(
self.__getListMetaInfo(inferenceElement))
elif isinstance(value, dict):
self.__outputFieldsMeta.extend(
self.__getDictMetaInfo(inferenceElement, value))
else:
if InferenceElement.getInputElement(inferenceElement):
self.__outputFieldsMeta.append(
FieldMetaInfo(name=inferenceLabel + ".actual",
type=FieldMetaType.string,
special=''))
self.__outputFieldsMeta.append(
FieldMetaInfo(name=inferenceLabel,
type=FieldMetaType.string,
special=''))
if self.__metricNames:
for metricName in self.__metricNames:
metricField = FieldMetaInfo(name=metricName,
type=FieldMetaType.float,
special=FieldMetaSpecial.none)
self.__outputFieldsMeta.append(metricField)
# Create the inference directory for our experiment
inferenceDir = _FileUtils.createExperimentInferenceDir(
self.__experimentDir)
# Consctruct the prediction dataset file path
filename = (self.__label + "." +
opfutils.InferenceType.getLabel(self.__inferenceType) +
".predictionLog.csv")
self.__datasetPath = os.path.join(inferenceDir, filename)
# Create the output dataset
print "OPENING OUTPUT FOR PREDICTION WRITER AT: %r" % self.__datasetPath
print "Prediction field-meta: %r" % (
[tuple(i) for i in self.__outputFieldsMeta], )
self.__dataset = FileRecordStream(streamID=self.__datasetPath,
write=True,
fields=self.__outputFieldsMeta)
# Copy data from checkpoint cache
if self.__checkpointCache is not None:
self.__checkpointCache.seek(0)
reader = csv.reader(self.__checkpointCache, dialect='excel')
# Skip header row
try:
header = reader.next()
except StopIteration:
print "Empty record checkpoint initializer for %r" % (
self.__datasetPath, )
else:
assert tuple(self.__dataset.getFieldNames()) == tuple(header), \
"dataset.getFieldNames(): %r; predictionCheckpointFieldNames: %r" % (
tuple(self.__dataset.getFieldNames()), tuple(header))
# Copy the rows from checkpoint
numRowsCopied = 0
while True:
try:
row = reader.next()
except StopIteration:
break
#print "DEBUG: restoring row from checkpoint: %r" % (row,)
self.__dataset.appendRecord(row)
numRowsCopied += 1
self.__dataset.flush()
print "Restored %d rows from checkpoint for %r" % (
numRowsCopied, self.__datasetPath)
# Dispose of our checkpoint cache
self.__checkpointCache.close()
self.__checkpointCache = None
return
def testMissingValues(self):
print "Beginning Missing Data test..."
filename = _getTempFileName()
# Some values missing of each type
# read dataset from disk, retrieve values
# string should return empty string, numeric types sentinelValue
print 'Creating tempfile:', filename
# write dataset to disk with float, int, and string fields
fields = [
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none)
]
s = FileRecordStream(streamID=filename, write=True, fields=fields)
# Records
records = ([datetime(day=1, month=3, year=2010), 'rec_1', 5, 6.5], [
datetime(day=2, month=3, year=2010), '', 8, 7.5
], [datetime(day=3, month=3, year=2010), 'rec_3', '', 8.5], [
datetime(day=4, month=3, year=2010), 'rec_4', 12, ''
], [datetime(day=5, month=3, year=2010), 'rec_5', -87657496599, 6.5], [
datetime(day=6, month=3, year=2010), 'rec_6', 12, -87657496599
], [datetime(day=6, month=3, year=2010),
str(-87657496599), 12, 6.5])
for r in records:
s.appendRecord(list(r))
s.close()
# Read the standard file
s = FileRecordStream(streamID=filename, write=False)
fieldsRead = s.getFields()
self.assertEqual(fields, fieldsRead)
recordsRead = []
while True:
r = s.getNextRecord()
if r is None:
break
print 'Reading record ...'
print r
recordsRead.append(r)
# sort the records by date, so we know for sure which is which
sorted(recordsRead, key=lambda rec: rec[0])
# empty string
self.assertEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[1][1])
# missing int
self.assertEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[2][2])
# missing float
self.assertEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[3][3])
# sentinel value in input handled correctly for int field
self.assertNotEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[4][2])
# sentinel value in input handled correctly for float field
self.assertNotEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[5][3])
# sentinel value in input handled correctly for string field
# this should leave the string as-is, since a missing string
# is encoded not with a sentinel value but with an empty string
self.assertNotEqual(SENTINEL_VALUE_FOR_MISSING_DATA, recordsRead[6][1])
def loadModel(self):
""" Load the model and construct the input row encoder. On success,
the loaded model may be accessed via the `model` attribute
:raises: model_checkpoint_mgr.ModelNotFound
"""
if self._model is not None:
return
modelDefinition = None
# Load the model
try:
self._model = self._checkpointMgr.load(self._modelID)
self._hasCheckpoint = True
except model_checkpoint_mgr.ModelNotFound:
# So, we didn't have a checkpoint... try to create our model from model
# definition params
self._hasCheckpoint = False
try:
modelDefinition = self._checkpointMgr.loadModelDefinition(
self._modelID)
except model_checkpoint_mgr.ModelNotFound:
raise _ModelRunnerError(errno=htmengineerrno.ERR_NO_SUCH_MODEL,
msg="modelID=%s not found" %
(self._modelID))
else:
modelParams = modelDefinition["modelParams"]
# TODO: when creating the model from params, do we need to call
# its model.setFieldStatistics() method? And where will the
# fieldStats come from, anyway?
self._model = ModelFactory.create(
modelConfig=modelParams["modelConfig"])
self._model.enableLearning()
self._model.enableInference(modelParams["inferenceArgs"])
# Construct the object for converting a flat input row into a format
# that is consumable by an OPF model
try:
if modelDefinition is None:
modelDefinition = self._checkpointMgr.loadModelDefinition(
self._modelID)
except model_checkpoint_mgr.ModelNotFound:
raise _ModelRunnerError(errno=htmengineerrno.ERR_NO_SUCH_MODEL,
msg="modelID=%s not found" %
(self._modelID))
else:
inputSchema = modelDefinition["inputSchema"]
# Convert it to a sequence of FieldMetaInfo instances
# NOTE: if loadMetaInfo didn't raise, we expect "inputSchema" to be
# present; it would be a logic error if it isn't.
inputFieldsMeta = tuple(FieldMetaInfo(*f) for f in inputSchema)
self._inputRowEncoder = _InputRowEncoder(fieldsMeta=inputFieldsMeta)
# If the checkpoint was incremental, feed the cached data into the model
for inputSample in self._inputSamplesSinceLastFullCheckpoint:
# Convert a flat input sample into a format that is consumable by an OPF
# model
self._inputRowEncoder.appendRecord(inputSample)
# Infer
self._model.run(self._inputRowEncoder.getNextRecordDict())
def testGetNextRecordDictWithResetFieldWithoutSequenceField(self):
fields = [
FieldMetaInfo('name', FieldMetaType.string, FieldMetaSpecial.none),
FieldMetaInfo('timestamp', FieldMetaType.datetime,
FieldMetaSpecial.timestamp),
FieldMetaInfo('integer', FieldMetaType.integer,
FieldMetaSpecial.none),
FieldMetaInfo('real', FieldMetaType.float, FieldMetaSpecial.none),
FieldMetaInfo('reset', FieldMetaType.integer,
FieldMetaSpecial.reset),
FieldMetaInfo('categories', FieldMetaType.list,
FieldMetaSpecial.category)
]
stream = self.MyRecordStream(fields)
with mock.patch.object(stream,
'getNextRecord',
autospec=True,
return_value=[
'rec_1',
datetime(day=1, month=3, year=2010), 5, 6.5,
1, [0, 1, 2]
]):
result = stream.getNextRecordDict()
self.assertEqual(
result, {
'name': 'rec_1',
'timestamp': datetime(2010, 3, 1, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 1, 0, 0),
'_timestampRecordIdx': None
})
# One more time to verify incremeting sequence id
with mock.patch.object(stream,
'getNextRecord',
autospec=True,
return_value=[
'rec_2',
datetime(day=2, month=3, year=2010), 5, 6.5,
1, [0, 1, 2]
]):
result = stream.getNextRecordDict()
self.assertEqual(
result, {
'name': 'rec_2',
'timestamp': datetime(2010, 3, 2, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 1,
'_timestamp': datetime(2010, 3, 2, 0, 0),
'_timestampRecordIdx': None
})
# Now with reset turned off, expecting no change to sequence id
with mock.patch.object(stream,
'getNextRecord',
autospec=True,
return_value=[
'rec_3',
datetime(day=3, month=3, year=2010), 5, 6.5,
0, [0, 1, 2]
]):
result = stream.getNextRecordDict()
self.assertEqual(
result, {
'name': 'rec_3',
'timestamp': datetime(2010, 3, 3, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 0,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 0,
'_sequenceId': 1,
'_timestamp': datetime(2010, 3, 3, 0, 0),
'_timestampRecordIdx': None
})
# Now check that rewind resets sequence id
with mock.patch.object(stream,
'getNextRecord',
autospec=True,
return_value=[
'rec_4',
datetime(day=4, month=3, year=2010), 5, 6.5,
1, [0, 1, 2]
]):
stream.rewind()
result = stream.getNextRecordDict()
self.assertEqual(
result, {
'name': 'rec_4',
'timestamp': datetime(2010, 3, 4, 0, 0),
'integer': 5,
'real': 6.5,
'reset': 1,
'categories': [0, 1, 2],
'_category': [0, 1, 2],
'_reset': 1,
'_sequenceId': 0,
'_timestamp': datetime(2010, 3, 4, 0, 0),
'_timestampRecordIdx': None
})
