def updateAllModelLabels(data, labels):
# This is the problems that the label update is in
txn = db.getTxn()
problems = Tracks.getProblems(data)
for problem in problems:
modelSummaries = db.ModelSummaries(data['user'], data['hub'],
data['track'], problem['chrom'],
problem['chromStart'])
modelsums = modelSummaries.get(txn=txn, write=True)
if len(modelsums.index) < 1:
submitPregenJob(problem, data, txn=txn)
continue
newSum = modelsums.apply(modelSumLabelUpdate,
axis=1,
args=(labels, data, problem, txn))
modelSummaries.put(newSum, txn=txn)
checkGenerateModels(newSum, problem, data, txn=txn)
txn.commit()
def checkForMoreJobs(task):
txn = db.getTxn()
problem = task['problem']
modelSums = db.ModelSummaries(task['user'], task['hub'], task['track'],
problem['chrom'],
problem['chromStart']).get(txn=txn,
write=True)
Models.checkGenerateModels(modelSums, problem, task, txn=txn)
txn.commit()
def checkLabelRegions():
labeledRegions = 0
for key in db.ModelSummaries.db_key_tuples():
modelSum = db.ModelSummaries(*key).get()
if not modelSum.empty:
if modelSum['regions'].max() > 0:
labeledRegions = labeledRegions + 1
return labeledRegions > cfg.minLabeledRegions
def getModels(data):
problems = Tracks.getProblems(data)
output = []
for problem in problems:
modelSummaries = db.ModelSummaries(data['user'], data['hub'],
data['track'], problem['chrom'],
problem['chromStart']).get()
if len(modelSummaries.index) < 1:
lopartOutput = generateLOPARTModel(data, problem)
output.extend(lopartOutput)
continue
nonZeroRegions = modelSummaries[modelSummaries['regions'] > 0]
if len(nonZeroRegions.index) < 1:
lopartOutput = generateLOPARTModel(data, problem)
output.extend(lopartOutput)
continue
withPeaks = nonZeroRegions[nonZeroRegions['numPeaks'] > 0]
if len(withPeaks.index) < 1:
lopartOutput = generateLOPARTModel(data, problem)
output.extend(lopartOutput)
continue
noError = withPeaks[withPeaks['errors'] < 1]
if len(noError.index) < 1:
lopartOutput = generateLOPARTModel(data, problem)
output.extend(lopartOutput)
continue
elif len(noError.index) > 1:
# Select which model to display from modelSums with 0 error
noError = whichModelToDisplay(data, problem, noError)
penalty = noError['penalty'].iloc[0]
minErrorModel = db.Model(data['user'], data['hub'], data['track'],
problem['chrom'], problem['chromStart'],
penalty)
model = minErrorModel.getInBounds(data['ref'], data['start'],
data['end'])
onlyPeaks = model[model['annotation'] == 'peak']
# Organize the columns
onlyPeaks = onlyPeaks[modelColumns]
onlyPeaks.columns = jbrowseModelColumns
output.extend(onlyPeaks.to_dict('records'))
return output
def numCorrectModels():
correct = 0
for key in db.ModelSummaries.db_key_tuples():
modelSum = db.ModelSummaries(*key).get()
if modelSum.empty:
continue
zeroErrors = modelSum[modelSum['errors'] < 0]
correct = correct + len(zeroErrors.index)
return correct
def getModelSummary(data):
txn = db.getTxn()
problems = Tracks.getProblems(data, txn=txn)
output = {}
for problem in problems:
# TODO: Replace 1 with user of hub NOT current user
modelSummaries = db.ModelSummaries(data['user'], data['hub'],
data['track'], problem['chrom'],
problem['chromStart']).get(txn=txn)
if len(modelSummaries.index) < 1:
continue
output[problem['chromStart']] = modelSummaries.to_dict('records')
txn.commit()
return output
def putModel(data):
modelData = pd.read_json(data['modelData'])
modelData.columns = modelColumns
modelInfo = data['modelInfo']
problem = modelInfo['problem']
penalty = data['penalty']
user = modelInfo['user']
hub = modelInfo['hub']
track = modelInfo['track']
txn = db.getTxn()
db.Model(user, hub, track, problem['chrom'], problem['chromStart'],
penalty).put(modelData, txn=txn)
labels = db.Labels(user, hub, track, problem['chrom']).get()
errorSum = calculateModelLabelError(modelData, labels, problem, penalty)
db.ModelSummaries(user, hub, track, problem['chrom'],
problem['chromStart']).add(errorSum, txn=txn)
txn.commit()
return modelInfo
def getDataPoints():
if not check():
return
dataPoints = pd.DataFrame()
for key in db.ModelSummaries.db_key_tuples():
modelSum = db.ModelSummaries(*key).get()
if modelSum.empty:
continue
if modelSum['regions'].max() < 1:
continue
withPeaks = modelSum[modelSum['numPeaks'] > 0]
noError = withPeaks[withPeaks['errors'] < 1]
logPenalties = np.log10(noError['penalty'].astype(float))
featuresDb = db.Features(*key)
featuresTxn = db.getTxn()
features = featuresDb.get(txn=featuresTxn, write=True)
for penalty in logPenalties:
datapoint = features.copy()
datapoint['logPenalty'] = penalty
dataPoints = dataPoints.append(datapoint, ignore_index=True)
featuresTxn.commit()
# TODO: Save datapoints, update ones which have changed, not all of them every time
Y = dataPoints['logPenalty']
X = dataPoints.drop('logPenalty', 1)
return dropBadCols(X), Y