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Always on param math changes #8

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Always on param math changes #8

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25 changes: 15 additions & 10 deletions source/strategy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ def getAllJobHashes(self, alwaysParams, neverParams):
return hashes

@staticmethod
def create(specification, strategy):
def create(specification, strategy, numAlwaysIncludeParams):
if "type" not in specification:
return None

Expand All @@ -96,6 +96,10 @@ def create(specification, strategy):
g.log(LOG_ERROR, "parameter selection ranges must contain numbers in the range [1 .. n]")
return None

# if always-on parameters are named, adjust the range to always optimize for them
start += numAlwaysIncludeParams
end += numAlwaysIncludeParams

if specification["type"] == "full-set":
x = ParamSelectionFullSet(strategy)
elif specification["type"] == "zero":
Expand Down Expand Up @@ -349,7 +353,7 @@ def isActive(self):
return self.activeJobPool is not None

def getTotalNumJobs(self):
return max(self.nextJobID - 1, self.totalNumJobs)
return max(max(self.nextJobID - 1, self.totalNumJobs), 0)

def getTotalNumParams(self):
if self.copasiConfig is None:
Expand Down Expand Up @@ -777,34 +781,37 @@ def getSpecialParams(self):
def execute(self):
parameterSelections = []

alwaysParams, neverParams = self.getSpecialParams()
numAlwaysParams = len(alwaysParams)

if self.taskType == COPASI_TASK_OPTIMIZATION:
# always add the zero'th job at start, needed to show baseline in graphs and for TOP
g.log(LOG_INFO, "optimizing for zero parameters initially to find the baseline")
spec = {"type" : "zero"}
parameterSelections.append(ParamSelection.create(spec, self))
parameterSelections.append(ParamSelection.create(spec, self, numAlwaysParams))
hasZerothJob = True
else:
hasZerothJob = False

if self.isTOPEnabled():
# add all parameters: will define the target value
spec = {"type" : "full-set"}
parameterSelections.append(ParamSelection.create(spec, self))
parameterSelections.append(ParamSelection.create(spec, self, numAlwaysParams))

if g.getConfig("restartOnFile"):
# Guess which parameters have not been optimized yet based on the .csv result file
filename = g.getConfig("restartOnFile").replace("@SELF@", SELF_PATH)
parameterSets = getNonconvergedResults(filename)
for ps in parameterSets:
spec = {"type" : "explicit", "parameters" : ps}
x = ParamSelection.create(spec, self)
x = ParamSelection.create(spec, self, numAlwaysParams)
if x not in parameterSelections:
parameterSelections.append(x)

elif g.getConfig("parameters"):
# Take the paramter sets from the file
for spec in g.getConfig("parameters"):
x = ParamSelection.create(spec, self)
x = ParamSelection.create(spec, self, numAlwaysParams)
if x is None:
g.log(LOG_ERROR, "invalid parameter specification: {}".format(ENC.encode(spec)))
continue
Expand All @@ -815,16 +822,14 @@ def execute(self):
# add the default optimization target: all parameters
g.log(LOG_INFO, "optimizing only for all parameters")
spec = {"type" : "full-set"}
parameterSelections.append(ParamSelection.create(spec, self))

alwaysParams, neverParams = self.getSpecialParams()
parameterSelections.append(ParamSelection.create(spec, self, numAlwaysParams))

hashes = set()
for sel in parameterSelections:
hashes = hashes.union(sel.getAllJobHashes(alwaysParams, neverParams))
self.totalNumJobs = len(hashes)
# if the extra "dummy" job0 is scheduled, do not include it in this number
if hasZerothJob:
if hasZerothJob and self.totalNumJobs > 0:
self.totalNumJobs -= 1
else:
# start from job1, not job0 (which is ignored by the web interface)
Expand Down