def plotSeqAndFeatures(seq, X, model, createModelAx=False, padBothSides=False, capYLim=1000):
"""plots the time series above the associated feature matrix"""
plt.figure(figsize=(10, 8))
if createModelAx:
nRows = 4
nCols = 7
axSeq = plt.subplot2grid((nRows,nCols), (0,0), colspan=(nCols-1))
axSim = plt.subplot2grid((nRows,nCols), (1,0), colspan=(nCols-1), rowspan=(nRows-1))
axPattern = plt.subplot2grid((nRows,nCols), (1,nCols-1), rowspan=(nRows-1))
axes = (axSeq, axSim, axPattern)
else:
nRows = 4
nCols = 1
axSeq = plt.subplot2grid((nRows,nCols), (0,0))
axSim = plt.subplot2grid((nRows,nCols), (1,0), rowspan=(nRows-1))
axes = (axSeq, axSim)
for ax in axes:
ax.autoscale(tight=True)
axSeq.plot(seq)
axSeq.set_ylim([seq.min(), min(capYLim, seq.max())])
if padBothSides:
padLen = (len(seq) - X.shape[1]) // 2
Xpad = ar.addZeroCols(X, padLen, prepend=True)
Xpad = ar.addZeroCols(Xpad, padLen, prepend=False)
else:
padLen = len(seq) - X.shape[1]
Xpad = ar.addZeroCols(Xpad, padLen, prepend=False)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
axSeq.set_title("Time Series", fontsize=28, y=1.04)
axSim.set_title("Feature Matrix", fontsize=28, y=1.01)
# plot the learned pattern model
if createModelAx:
axPattern.set_title("Learned\nPattern", fontsize=24, y=1.02)
if model is not None:
axPattern.imshow(model, interpolation='nearest', aspect='auto')
tickLocations = plt.FixedLocator([0, model.shape[1]])
axPattern.xaxis.set_major_locator(tickLocations)
axPattern.yaxis.tick_right() # y ticks on right side
else:
print("WARNING: attempted to plot null feature weights!")
for ax in axes:
for tick in ax.get_xticklabels() + ax.get_yticklabels():
tick.set_fontsize(20)
removeEveryOtherYTick(axSeq)
return axes
def _learnFF(seq, X, Xblur, Lmin, Lmax, Lfilt, generateSeedsStep=0.1):
padLen = (len(seq) - X.shape[1]) // 2
X = ar.addZeroCols(X, padLen, prepend=True)
X = ar.addZeroCols(X, padLen, prepend=False)
Xblur = ar.addZeroCols(Xblur, padLen, prepend=True)
Xblur = ar.addZeroCols(Xblur, padLen, prepend=False)
timeStartSeed = time.clock()
# find seeds; i.e., candidate instance indices from which to generalize
numShifts = int(1.0 / generateSeedsStep) + 1
stepLen = int(Lmax * generateSeedsStep)
windowLen = Lmax + stepLen
# score all subseqs based on how much they don't look like random walks
# when examined using different sliding window lengths
# scores = np.zeros(len(seq))
# for dim in range(seq.shape[1]):
# # compute these just once, not once per length
# dimData = seq[:, dim].flatten()
# std = np.std(dimData[1:] - dimData[:-1])
# for divideBy in [1, 2, 4, 8]:
# partialScores = feat.windowScoresRandWalk(dimData, Lmin // divideBy, std)
# scores[:len(partialScores)] += partialScores
scores = _seedScores(seq, Lmin)
# figure out optimal pair based on scores of all subseqs
bestIdx = np.argmax(scores)
start = max(0, bestIdx - Lmin)
end = min(len(scores), start + Lmin)
scores[start:end] = -1 # disqualify idxs within Lmin of bestIdx
secondBestIdx = np.argmax(scores)
# compute all seed idxs from this pair
seedIdxs = [bestIdx, secondBestIdx]
maxIdx = X.shape[1] - windowLen - 1
seedStartIdxs = _computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen, maxIdx)
seedEndIdxs = seedStartIdxs + windowLen
timeEndSeed = time.clock()
bsfScore, bsfLocs, bsfFilt = _findInstancesUsingSeedLocs(
X, Xblur, seedStartIdxs, seedEndIdxs, Lmin, Lmax, Lfilt, windowLen=windowLen
)
startIdxs, endIdxs = _extractTrueLocs(X, Xblur, bsfLocs, bsfFilt, windowLen, Lmin, Lmax)
timeEndFF = time.clock()
print "learnFF(): seconds to find seeds, regions, total =\n\t{:.3f}\t{:.3f}\t{:.3f}".format(
timeEndSeed - timeStartSeed, timeEndFF - timeEndSeed, timeEndFF - timeStartSeed
)
return startIdxs, endIdxs, bsfFilt
def _learnFF(seq, X, Xblur, Lmin, Lmax, Lfilt, generateSeedsStep=.1):
padLen = (len(seq) - X.shape[1]) // 2
X = ar.addZeroCols(X, padLen, prepend=True)
X = ar.addZeroCols(X, padLen, prepend=False)
Xblur = ar.addZeroCols(Xblur, padLen, prepend=True)
Xblur = ar.addZeroCols(Xblur, padLen, prepend=False)
timeStartSeed = time.clock()
# find seeds; i.e., candidate instance indices from which to generalize
numShifts = int(1. / generateSeedsStep) + 1
stepLen = int(Lmax * generateSeedsStep)
windowLen = Lmax + stepLen
# score all subseqs based on how much they don't look like random walks
# when examined using different sliding window lengths
scores = np.zeros(len(seq))
for dim in range(seq.shape[1]):
# compute these just once, not once per length
dimData = seq[:, dim].flatten()
std = np.std(dimData[1:] - dimData[:-1])
for divideBy in [1, 2, 4, 8]:
partialScores = feat.windowScoresRandWalk(dimData,
Lmin // divideBy, std)
scores[:len(partialScores)] += partialScores
# figure out optimal pair based on scores of all subseqs
bestIdx = np.argmax(scores)
start = max(0, bestIdx - Lmin)
end = min(len(scores), start + Lmin)
scores[start:end] = -1 # disqualify idxs within Lmin of bestIdx
secondBestIdx = np.argmax(scores)
# compute all seed idxs from this pair
seedIdxs = [bestIdx, secondBestIdx]
maxIdx = X.shape[1] - windowLen - 1
seedStartIdxs = _computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen,
maxIdx)
seedEndIdxs = seedStartIdxs + windowLen
timeEndSeed = time.clock()
bsfScore, bsfLocs, bsfFilt = _findInstancesUsingSeedLocs(
X,
Xblur,
seedStartIdxs,
seedEndIdxs,
Lmin,
Lmax,
Lfilt,
windowLen=windowLen)
startIdxs, endIdxs = _extractTrueLocs(X, Xblur, bsfLocs, bsfFilt,
windowLen, Lmin, Lmax)
timeEndFF = time.clock()
print "learnFF(): seconds to find seeds, regions, total =\n\t{:.3f}\t{:.3f}\t{:.3f}".format(
timeEndSeed - timeStartSeed, timeEndFF - timeEndSeed,
timeEndFF - timeStartSeed)
return startIdxs, endIdxs, bsfFilt
def plotSeqAndFeatures(seq,
X,
model,
createModelAx=False,
padBothSides=False,
capYLim=1000):
"""plots the time series above the associated feature matrix"""
plt.figure(figsize=(10, 8))
if createModelAx:
nRows = 4
nCols = 7
axSeq = plt.subplot2grid((nRows, nCols), (0, 0), colspan=(nCols - 1))
axSim = plt.subplot2grid((nRows, nCols), (1, 0),
colspan=(nCols - 1),
rowspan=(nRows - 1))
axPattern = plt.subplot2grid((nRows, nCols), (1, nCols - 1),
rowspan=(nRows - 1))
axes = (axSeq, axSim, axPattern)
else:
nRows = 4
nCols = 1
axSeq = plt.subplot2grid((nRows, nCols), (0, 0))
axSim = plt.subplot2grid((nRows, nCols), (1, 0), rowspan=(nRows - 1))
axes = (axSeq, axSim)
for ax in axes:
ax.autoscale(tight=True)
axSeq.plot(seq)
axSeq.set_ylim([seq.min(), min(capYLim, seq.max())])
if padBothSides:
padLen = (len(seq) - X.shape[1]) // 2
Xpad = ar.addZeroCols(X, padLen, prepend=True)
Xpad = ar.addZeroCols(Xpad, padLen, prepend=False)
else:
padLen = len(seq) - X.shape[1]
Xpad = ar.addZeroCols(Xpad, padLen, prepend=False)
axSim.imshow(Xpad, interpolation='nearest', aspect='auto')
axSeq.set_title("Time Series", fontsize=28, y=1.04)
axSim.set_title("Feature Matrix", fontsize=28, y=1.01)
# plot the learned pattern model
if createModelAx:
axPattern.set_title("Learned\nPattern", fontsize=24, y=1.02)
if model is not None:
axPattern.imshow(model, interpolation='nearest', aspect='auto')
tickLocations = plt.FixedLocator([0, model.shape[1]])
axPattern.xaxis.set_major_locator(tickLocations)
axPattern.yaxis.tick_right() # y ticks on right side
else:
print("WARNING: attempted to plot null feature weights!")
for ax in axes:
for tick in ax.get_xticklabels() + ax.get_yticklabels():
tick.set_fontsize(20)
removeEveryOtherYTick(axSeq)
return axes
