def __init__(self,
data,
startIdxs,
endIdxs=None,
subseqLength=None,
labels=None,
name=None,
id=0):
self.data = ensure2D(data)
self.startIdxs = np.asarray(startIdxs, dtype=np.int)
self.labels = np.asarray(labels)
self.name = name
self.id = int(id)
if endIdxs is not None:
self.endIdxs = np.asarray(endIdxs, dtype=np.int)
self.subseqLength = None
elif subseqLength:
self.endIdxs = self.startIdxs + subseqLength
self.subseqLength = subseqLength
else:
raise ValueError(
"Either endIdxs or subseqLength must be specified!")
if labels is None or len(labels) == 0:
self.labels = np.zeros(len(startIdxs),
dtype=np.int) + DEFAULT_LABEL
if startIdxs is not None and endIdxs is not None:
# equal lengths
nStart, nEnd = len(startIdxs), len(endIdxs)
if nStart != nEnd:
raise ValueError("Number of start indices must equal number"
"of end indices! {0} != {1}".format(
nStart, nEnd))
# starts before or equal to ends
violators = np.where(startIdxs > endIdxs)[0]
if np.any(violators):
raise ValueError("Some start indices exceed end indices!"
"Violators at {}".format(str(violators)))
# valid indices
violators = np.where(startIdxs < 0)[0]
if np.any(violators):
raise ValueError("Some start indices < 0!"
"Violators at {}".format(str(violators)))
violators = np.where(endIdxs > len(data))[0]
if np.any(violators):
violatorValues = endIdxs[violators]
raise ValueError("Some end indices > length of data {}! "
"Violators {} at {}".format(
len(data), str(violatorValues),
str(violators)))
def __init__(self, data, startIdxs, endIdxs=None, subseqLength=None,
labels=None, name=None, id=0):
self.data = ensure2D(data)
self.startIdxs = np.asarray(startIdxs, dtype=np.int)
self.labels = np.asarray(labels)
self.name = name
self.id = int(id)
if endIdxs is not None:
self.endIdxs = np.asarray(endIdxs, dtype=np.int)
self.subseqLength = None
elif subseqLength:
self.endIdxs = self.startIdxs + subseqLength
self.subseqLength = subseqLength
else:
raise ValueError("Either endIdxs or subseqLength must be specified!")
if labels is None or len(labels) == 0:
self.labels = np.zeros(len(startIdxs), dtype=np.int) + DEFAULT_LABEL
if startIdxs is not None and endIdxs is not None:
# equal lengths
nStart, nEnd = len(startIdxs), len(endIdxs)
if nStart != nEnd:
raise ValueError("Number of start indices must equal number"
"of end indices! {0} != {1}".format(nStart, nEnd))
# starts before or equal to ends
violators = np.where(startIdxs > endIdxs)[0]
if np.any(violators):
raise ValueError("Some start indices exceed end indices!"
"Violators at {}".format(str(violators)))
# valid indices
violators = np.where(startIdxs < 0)[0]
if np.any(violators):
raise ValueError("Some start indices < 0!"
"Violators at {}".format(str(violators)))
violators = np.where(endIdxs > len(data))[0]
if np.any(violators):
violatorValues = endIdxs[violators]
raise ValueError("Some end indices > length of data {}! "
"Violators {} at {}".format(len(data),
str(violatorValues), str(violators)))
def sectionsOfDataNearAnnotationsImpure(X, startIdxs, endIdxs, labels,
instancesPerTs=10, shuffle=False, padLen=0, maxPadJitter=0,
keepLabels=None, datasetName="Dataset"):
assert(len(startIdxs) == len(endIdxs))
assert(len(startIdxs) == len(labels))
startIdxs = np.asarray(startIdxs)
endIdxs = np.asarray(endIdxs)
# filter out labels we don't care about
if keepLabels:
allIdxs = np.arange(len(labels))
keepIdxs = [i for i in allIdxs if labels[i] in keepLabels]
keepIdxs = np.array(keepIdxs, dtype=np.int)
startIdxs = startIdxs[keepIdxs]
endIdxs = endIdxs[keepIdxs]
labels = labels[keepIdxs]
# find sections of nearby annotations in the data and group these
# sections together; we'll concat these groups together to form a ts
combinedRanges = unionOfRanges(startIdxs, endIdxs, len(X), padLen=padLen)
rangeGroups = formGroupsOfSize(combinedRanges, groupSize=instancesPerTs,
shuffle=shuffle)
# now the hard part--create a LabeledTimeSeries from each of these
# sections of signal; we have to not only find which annotations
# fall within each range, but also adjust the start and end indices
# so that they're correct in the new ts formed by concatenating the
# data in each range together
tsList = []
for groupNum, ranges in enumerate(rangeGroups):
ranges = sorted(ranges, key=lambda r: r[0]) # sort by range start idx
dataLenSoFar = 0
dataInRanges = []
startsInRanges = []
endsInRanges = []
labelsInRanges = []
for rang in ranges:
start, end = rang
firstInRange, lastInRange = whereStartEndPairsInRange(startIdxs,
endIdxs, start, end)
idxsInRange = np.arange(firstInRange, lastInRange)
# move the start and end indices around a bit so that ranges
# aren't spaced exactly uniformly, which can lead to an
# artificial semblance of regularity
if maxPadJitter > 0:
if firstInRange > 0:
firstStartIdx = startIdxs[firstInRange]
prevEndIdx = endIdxs[firstInRange-1]
gap = firstStartIdx - prevEndIdx
if gap > 1:
gap = min(gap - 1, maxPadJitter)
offset = int(np.random.rand() * gap)
start -= offset
if lastInRange < (len(startIdxs) - 1):
lastEndIdx = endIdxs[lastInRange-1] # last idx not inclusive
nextStartIdx = startIdxs[lastInRange]
gap = nextStartIdx - lastEndIdx
if gap > 1:
gap = min(gap - 1, maxPadJitter)
offset = int(np.random.rand() * gap)
end += offset
starts = startIdxs[idxsInRange] - start + dataLenSoFar
ends = endIdxs[idxsInRange] - start + dataLenSoFar
lbls = labels[idxsInRange]
startsInRanges += list(starts)
endsInRanges += list(ends)
labelsInRanges += list(lbls)
data = ensure2D(X[start:end])
dataInRanges.append(data)
dataLenSoFar += len(data)
if len(labelsInRanges) < 2: # need more than one pattern instance per ts
continue
groupData = np.vstack(dataInRanges)
groupStarts = np.array(startsInRanges, dtype=np.int)
groupEnds = np.array(endsInRanges, dtype=np.int)
groupLabels = np.array(labelsInRanges, dtype=np.object)
name = "{}-group{}".format(datasetName, groupNum)
uniqId = hash(name)
ts = LabeledTimeSeries(groupData, startIdxs=groupStarts,
endIdxs=groupEnds, labels=groupLabels, name=name, id=uniqId)
tsList.append(ts)
return tsList
