def findOutcastOfLength(seqs, length, shiftStep=0.1, norm="each", mdl=False):
# if numShifts < 0:
# numShifts = 1
# stepLen = shiftStep * length
motif = findMotifOfLengthFast(seqs[:], length, norm=norm)
Xnorm = motif.Xnorm
seedIdxs = computeAllSeedIdxsFromPair(motif.idx1, motif.idx2, shiftStep)
# XXX this assumes only one seq
seedIdxs, _ = startsAndEndsWithinBounds(seedIdxs, length, len(seqs[0]))
bestScore = -np.inf
bestOutcast = None
for idx in seedIdxs:
seed = Xnorm[idx]
if mdl:
info = findOutcastInstancesMDL(Xnorm, seed, length)
else:
info = findOutcastInstances(Xnorm, seed, length)
if info and info.score > bestScore:
bestScore = info.score
bestOutcast = info
print "bestOutcast idxs at length {}: {}".format(length, bestOutcast.idxs, bestOutcast.length)
return bestOutcast
def findOutcastOfLength(seqs, length, shiftStep=.1, norm='each', mdl=False):
# if numShifts < 0:
# numShifts = 1
# stepLen = shiftStep * length
motif = findMotifOfLengthFast(seqs[:], length, norm=norm)
Xnorm = motif.Xnorm
seedIdxs = computeAllSeedIdxsFromPair(motif.idx1, motif.idx2, shiftStep)
# XXX this assumes only one seq
seedIdxs, _ = startsAndEndsWithinBounds(seedIdxs, length, len(seqs[0]))
bestScore = -np.inf
bestOutcast = None
for idx in seedIdxs:
seed = Xnorm[idx]
if mdl:
info = findOutcastInstancesMDL(Xnorm, seed, length)
else:
info = findOutcastInstances(Xnorm, seed, length)
if info and info.score > bestScore:
bestScore = info.score
bestOutcast = info
print "bestOutcast idxs at length {}: {}".format(length, bestOutcast.idxs,
bestOutcast.length)
return bestOutcast
def learnFF(seq,
X,
Xblur,
Lmin,
Lmax,
Lfilt,
generateSeedsAlgo=DEFAULT_SEEDS_ALGO,
generalizeSeedsAlgo=DEFAULT_GENERALIZE_ALGO,
extractTrueLocsAlgo=DEFAULT_EXTRACT_LOCS_ALGO,
generateSeedsStep=.1,
padBothSides=True,
**generalizeKwargs):
padLen = (len(seq) - X.shape[1]) // 2
if padBothSides:
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)
tStartSeed = 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
print "learnFF(): stepLen, numShifts", stepLen, numShifts
if generateSeedsAlgo == 'pair':
searchLen = (Lmin + Lmax) // 2
motif = findMotifOfLengthFast([seq], searchLen)
seedIdxs = [motif.idx1, motif.idx2]
print "seedIdxs from motif: ", seedIdxs
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
elif generateSeedsAlgo == 'all':
seedIdxs = np.arange(X.shape[1] - windowLen) # TODO remove after debug
elif generateSeedsAlgo == 'random':
seedIdxs = list(np.random.choice(np.arange(len(seq) - Lmax), 2))
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
elif generateSeedsAlgo == 'walk':
# 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].ravel()
diffs = dimData[1:] - dimData[:-1]
std = np.std(diffs)
for divideBy in [1, 2, 4, 8]:
partialScores = windowScoresRandWalk(dimData,
Lmin // divideBy,
std=std)
scores[:len(partialScores)] += partialScores
# figure out optimal seeds 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
secondBestIdx = np.argmax(scores)
seedIdxs = [bestIdx, secondBestIdx]
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
else:
raise NotImplementedError(
"Only algo 'pair' supported to generate seeds"
"; got unrecognized algo {}".format(generateSeedsAlgo))
# compute start and end indices of seeds to try
seedStartIdxs = np.sort(np.array(seedIdxs))
seedStartIdxs = seedStartIdxs[seedStartIdxs >= 0]
seedStartIdxs = seedStartIdxs[seedStartIdxs < X.shape[1] - windowLen]
seedEndIdxs = seedStartIdxs + windowLen
print "learnFF(): seedIdxs after removing invalid idxs: ", seedStartIdxs
print "learnFF(): fraction of idxs used as seeds: {}".format(
len(seedStartIdxs) / float(len(seq)))
tEndSeed = time.clock()
generalizeKwargs['windowLen'] = windowLen # TODO remove after prototype
bsfScore, bsfLocs, bsfFilt = findInstancesUsingSeedLocs(
X,
Xblur,
seedStartIdxs,
seedEndIdxs,
Lmin,
Lmax,
Lfilt,
generalizeSeedsAlgo=generalizeSeedsAlgo,
**generalizeKwargs)
# print "learnFF(): got bsfFilt shape", bsfFilt.shape
startIdxs, endIdxs = extractTrueLocs(
X,
Xblur,
bsfLocs,
bsfFilt,
windowLen,
Lmin,
Lmax,
extractTrueLocsAlgo=extractTrueLocsAlgo)
tEndFF = time.clock()
print "learnFF(): seconds to find seeds, locs, total =\n\t{:.3f}\t{:.3f}\t{:.3f}".format(
tEndSeed - tStartSeed, tEndFF - tEndSeed, tEndFF - tStartSeed)
return startIdxs, endIdxs, bsfFilt
def learnFF(seq, X, Xblur, Lmin, Lmax, Lfilt,
generateSeedsAlgo=DEFAULT_SEEDS_ALGO,
generalizeSeedsAlgo=DEFAULT_GENERALIZE_ALGO,
extractTrueLocsAlgo=DEFAULT_EXTRACT_LOCS_ALGO,
generateSeedsStep=.1, padBothSides=True, **generalizeKwargs):
padLen = (len(seq) - X.shape[1]) // 2
if padBothSides:
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)
tStartSeed = 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
print "learnFF(): stepLen, numShifts", stepLen, numShifts
if generateSeedsAlgo == 'pair':
searchLen = (Lmin + Lmax) // 2
motif = findMotifOfLengthFast([seq], searchLen)
seedIdxs = [motif.idx1, motif.idx2]
print "seedIdxs from motif: ", seedIdxs
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
elif generateSeedsAlgo == 'all':
seedIdxs = np.arange(X.shape[1] - windowLen) # TODO remove after debug
elif generateSeedsAlgo == 'random':
seedIdxs = list(np.random.choice(np.arange(len(seq) - Lmax), 2))
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
elif generateSeedsAlgo == 'walk':
# 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].ravel()
diffs = dimData[1:] - dimData[:-1]
std = np.std(diffs)
for divideBy in [1, 2, 4, 8]:
partialScores = windowScoresRandWalk(dimData, Lmin // divideBy,
std=std)
scores[:len(partialScores)] += partialScores
# figure out optimal seeds 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
secondBestIdx = np.argmax(scores)
seedIdxs = [bestIdx, secondBestIdx]
seedIdxs = computeAllSeedIdxsFromPair(seedIdxs, numShifts, stepLen)
else:
raise NotImplementedError("Only algo 'pair' supported to generate seeds"
"; got unrecognized algo {}".format(generateSeedsAlgo))
# compute start and end indices of seeds to try
seedStartIdxs = np.sort(np.array(seedIdxs))
seedStartIdxs = seedStartIdxs[seedStartIdxs >= 0]
seedStartIdxs = seedStartIdxs[seedStartIdxs < X.shape[1] - windowLen]
seedEndIdxs = seedStartIdxs + windowLen
print "learnFF(): seedIdxs after removing invalid idxs: ", seedStartIdxs
print "learnFF(): fraction of idxs used as seeds: {}".format(
len(seedStartIdxs) / float(len(seq)))
tEndSeed = time.clock()
generalizeKwargs['windowLen'] = windowLen # TODO remove after prototype
bsfScore, bsfLocs, bsfFilt = findInstancesUsingSeedLocs(X, Xblur,
seedStartIdxs, seedEndIdxs, Lmin, Lmax, Lfilt,
generalizeSeedsAlgo=generalizeSeedsAlgo,
**generalizeKwargs)
# print "learnFF(): got bsfFilt shape", bsfFilt.shape
startIdxs, endIdxs = extractTrueLocs(X, Xblur, bsfLocs, bsfFilt, windowLen,
Lmin, Lmax, extractTrueLocsAlgo=extractTrueLocsAlgo)
tEndFF = time.clock()
print "learnFF(): seconds to find seeds, locs, total =\n\t{:.3f}\t{:.3f}\t{:.3f}".format(
tEndSeed - tStartSeed, tEndFF - tEndSeed, tEndFF - tStartSeed)
return startIdxs, endIdxs, bsfFilt
