def __init__(self,
function,
initialParams,
methods,
logger=None,
isCollect=False):
"""
Parameters
----------
function: Funtion
Arguments
lmfit.parameters
isInitialze (bool). True on first call the
isGetBest (bool). True to retrieve best parameters
returns residuals (if bool arguments are false)
initialParams: lmfit.parameters
methods: list-_helpers.OptimizerMethod
isCollect: bool
Collects performance statistcs
"""
self._function = function
self._methods = methods
self._initialParams = initialParams
self._isCollect = isCollect
self.logger = logger
if self.logger is None:
self.logger = Logger()
# Outputs
self.performanceStats = [] # list of performance results
self.qualityStats = [] # relative rssq
self.params = None
self.minimizerResult = None
self.rssq = None
def __init__(self,
initialArgument,
inputQ,
outputQ,
isException=False,
logger=Logger()):
super().__init__(initialArgument, inputQ, outputQ, logger=Logger())
self.isException = isException
def testNoLogPerformance(self):
if IGNORE_TEST:
return
logger = Logger(toFile=LOG_PATH,
logPerformance=False,
logLevel=logs.LEVEL_MAX)
guid = logger.startBlock(BLOCK1)
self.assertEqual(len(self.logger.blockDct), 0)
logger.endBlock(guid)
self.assertEqual(len(self.logger.blockDct), 0)
def test(numBlock, sleepTime):
logger = Logger(logPerformance=True)
for idx in range(numBlock):
block = "blk_%d" % idx
guid = logger.startBlock(block)
time.sleep(sleepTime)
logger.endBlock(guid)
df = logger.performanceDF
self.assertLess(np.abs(sleepTime - df["mean"].mean()), sleepTime)
self.assertEqual(df["count"].mean(), 1.0)
def _residuals(self, params) -> np.ndarray:
"""
Compute the residuals between objective and experimental data
Handle nan values in observedTS. This internal-only method
is implemented to maximize efficieency.
Parameters
----------
kwargs: dict
arguments for simulation
Instance Variables Updated
--------------------------
self.residualsTS
Returns
-------
1-d ndarray of residuals
"""
block = Logger.join(self._loggerPrefix, "fitModel._residuals")
guid = self.logger.startBlock(block)
##V
self.roadrunnerModel.reset()
self._setupModel(params)
#
roadrunnerBlock = Logger.join(block, "roadrunner")
roadrunnerGuid = self.logger.startBlock(roadrunnerBlock)
## V
#
data = self.roadrunnerModel.simulate(self.observedTS.start,
self.observedTS.end,
len(self.observedTS),
self.selectedColumns)
## ^
self.logger.endBlock(roadrunnerGuid)
#
tailBlock = Logger.join(block, "tail")
tailGuid = self.logger.startBlock(tailBlock)
## V
residualsArr = self._observedArr - data.flatten()
residualsArr = np.nan_to_num(residualsArr)
## ^
self.logger.endBlock(tailGuid)
##^
self.logger.endBlock(guid)
#
# Used for detailed debugging
if False:
self.logger.details("_residuals/std(residuals): %f" %
np.std(residualsArr))
self.logger.details("_residuals/params: %s" % str(params))
return residualsArr
def _testApi(self, method, logLevel):
if IGNORE_TEST:
return
logger = Logger(toFile=LOG_PATH, logLevel=logLevel)
stmt = "logger.%s(MSG)" % method
exec(stmt)
line1s = self._checkMsg(MSG)
#
logger = Logger(toFile=LOG_PATH, logLevel=0)
stmt = "logger.%s(MSG)" % method
exec(stmt)
line2s = self.read()
self.assertEqual(len(line1s), len(line2s))
def __init__(self,
firstModel: int = 210,
numModel: int = 2,
pclPath=PCL_FILE,
figPath=FIG_PATH,
useExistingData: bool = False,
reportInterval: int = 10,
isPlot=IS_PLOT,
**kwargDct):
"""
Parameters
----------
firstModel: first model to use
numModel: number of models to use
pclPath: file to which results are saved
reportInterval: how frequently report progress
useExistingData: use data in existing PCL file
"""
self.useExistingData = useExistingData and os.path.isfile(pclPath)
# Recover previously saved results if desired
if self.useExistingData:
self.restore(pclPath=pclPath)
else:
# Initialize based on type of context variable
for name in CONTEXT:
if name[-1:] == "s":
self.__setattr__(name, [])
elif name[-3:] == "Dct":
self.__setattr__(name, {})
elif name[-4:] == "Path":
self.__setattr__(name, None)
elif name[0:2] == "is":
self.__setattr__(name, False)
else:
self.__setattr__(name, 0)
# Initialize to parameters for this instantiation
self.firstModel = firstModel
self.numModel = numModel
self.pclPath = pclPath
self.figPath = figPath
self.reportInterval = reportInterval
self.kwargDct = kwargDct
self.isPlot = isPlot
self.useExistingData = useExistingData
#
if LOGGER in kwargDct.keys():
self.logger = kwargDct[LOGGER]
else:
self.logger = Logger()
kwargDct[LOGGER] = self.logger
self.save()
def mkParameters(
cls,
parametersToFit: list,
logger: Logger = Logger(),
parameterLowerBound: float = cn.PARAMETER_LOWER_BOUND,
parameterUpperBound: float = cn.PARAMETER_UPPER_BOUND
) -> lmfit.Parameters:
"""
Constructs lmfit parameters based on specifications.
Parameters
----------
parametersToFit: list-Parameter/list-str
logger: error logger
parameterLowerBound: lower value of range for parameters
parameterUpperBound: upper value of range for parameters
Returns
-------
lmfit.Parameters
"""
if len(parametersToFit) == 0:
raise RuntimeError("Must specify at least one parameter.")
if logger is None:
logger = logger()
# Process each parameter
elements = []
for element in parametersToFit:
# Get the lower bound, upper bound, and initial value for the parameter
if not isinstance(element, SBstoat.Parameter):
element = SBstoat.Parameter(element,
lower=parameterLowerBound,
upper=parameterUpperBound)
elements.append(element)
return SBstoat.Parameter.mkParameters(elements)
def testJoin(self):
if IGNORE_TEST:
return
NAMES = ["aa", "bbb", "z"]
result = Logger.join(*NAMES)
for name in NAMES:
self.assertGreaterEqual(result.index(name), 0)
def __init__(self,
modelFitters,
modelNames=None,
modelWeights=None,
fitterMethods=None,
numRestart=0,
isParallel=False,
logger=Logger()):
"""
Parameters
----------
modelFitters: list-modelFiter
modelWeights: list-float
how models are weighted in least squares
modelNames: list-str
fitterMethods: list-optimization methods
numRestart: int
number of times the minimization is restarted with random
initial values for parameters to fit.
isParallel: bool
runs each fitter in parallel
logger: Logger
Raises
------
ValueError: len(modelNames) == len(modelFitters)
"""
self.numModel = len(modelFitters)
self.modelWeights = modelWeights
if self.modelWeights is None:
self.modelWeights = np.repeat(1, self.numModel)
self.modelNames = modelNames
if self.modelNames is None:
self.modelNames = [str(v) for v in range(len(modelSpecifications))]
self._numRestart = numRestart
self._isParallel = isParallel
self.logger = logger
# Validation checks
if self.numModel != len(self.modelNames):
msg = "Length of modelNames must be same as number of modelFitters."
raise ValueError(msg)
#
self.fitterDct = {n: f for n, f in zip(modelNames, modelFitters)}
# Construct tha parameters for each model
self.parametersCollection = [f.params for f in self.fitterDct.values()]
self.parameterManager = _ParameterManager(self.modelNames,
self.parametersCollection)
self._fitterMethods = ModelFitter.makeMethods(
fitterMethods, cn.METHOD_FITTER_DEFAULTS)
# Residuals calculations
self.residualsServers = [
ResidualsServer(f, None, None, logger=self.logger)
for f in self.fitterDct.values()
]
self.manager = None
# Results
self.optimizer = None
def merge(cls, bootstrapResults, fitter=None):
"""
Combines a list of BootstrapResult. Sets a fitter.
Parameter
---------
bootstrapResults: list-BootstrapResult
fitter: ModelFitter
Return
------
BootstrapResult
"""
if len(bootstrapResults) == 0:
raise ValueError("Must provide a non-empty list")
parameterDct = {p: [] for p in bootstrapResults[0].parameterDct}
numIteration = sum([r.numIteration for r in bootstrapResults])
bootstrapError = sum([b.bootstrapError for b in bootstrapResults])
fittedStatistic = None
if numIteration > 0:
# Merge the logs
logger = Logger.merge([b.logger for b in bootstrapResults])
# Merge the statistics for fitted timeseries
fittedStatistics = [
b.fittedStatistic for b in bootstrapResults
if b.fittedStatistic is not None
]
fittedStatistic = TimeseriesStatistic.merge(fittedStatistics)
# Accumulate the results
for bootstrapResult in bootstrapResults:
for parameter in parameterDct.keys():
parameterDct[parameter].extend(
bootstrapResult.parameterDct[parameter])
#
fitter.logger = Logger.merge([fitter.logger, logger])
bootstrapResult = BootstrapResult(fitter,
numIteration,
parameterDct,
fittedStatistic,
bootstrapError=bootstrapError)
bootstrapResult.setFitter(fitter)
return bootstrapResult
def testBug(self):
if IGNORE_TEST:
return
runner = Runner(firstModel=607,
numModel=1,
useExistingData=False,
figPath=FIG_PATH,
pclPath=PCL_PATH,
isPlot=IS_PLOT,
logger=Logger())
runner.run()
def __init__(self, runnerArgument):
"""
Parameters
----------
runnerArgument: RunnerArgument
Notes
-----
1. Uses METHOD_LEASTSQ for fitModel iterations.
"""
super().__init__()
#
self.lastErr = ""
self.fitter = runnerArgument.fitter
self.numIteration = runnerArgument.numIteration
self.kwargs = runnerArgument.kwargs
self.synthesizerClass = runnerArgument.synthesizerClass
if "logger" in self.fitter.__dict__.keys():
self.logger = self.fitter.logger
else:
self.logger = Logger()
self._isDone = not self._fitInitial()
self.columns = self.fitter.selectedColumns
# Initializations for bootstrap loop
if not self.isDone:
fittedTS = self.fitter.fittedTS.subsetColumns(self.columns,
isCopy=False)
self.synthesizer = self.synthesizerClass(
observedTS=self.fitter.observedTS.subsetColumns(self.columns,
isCopy=False),
fittedTS=fittedTS,
**self.kwargs)
self.numSuccessIteration = 0
if self.fitter.minimizerResult is None:
self.fitter.fitModel()
self.baseChisq = self.fitter.minimizerResult.redchi
self.curIteration = 0
self.fd = self.logger.getFileDescriptor()
self.baseFittedStatistic = TimeseriesStatistic(
self.fitter.observedTS.subsetColumns(
self.fitter.selectedColumns, isCopy=False))
def __init__(self, fitter, inputQ, outputQ, logger=Logger()):
"""
Parameters
----------
fitter: ModelFitter
cannot have swig objects (e.g., roadrunner)
inputQ: multiprocessing.queue
outputQ: multiprocessing.queue
logger: Logger
"""
super().__init__(fitter, inputQ, outputQ, logger=logger)
self.fitter = fitter
def testWolfBug(self):
if IGNORE_TEST:
return
fullDct = {
#"J1_n": (1, 1, 8), # 4
#"J4_kp": (3600, 36000, 150000), #76411
#"J5_k": (10, 10, 160), # 80
#"J6_k": (1, 1, 10), # 9.7
"J9_k": (1, 50, 50), # 28
}
for parameter in fullDct.keys():
logger = Logger(logLevel=LEVEL_MAX)
logger = Logger()
ts = NamedTimeseries(csvPath=WOLF_DATA)
parameterDct = {parameter: fullDct[parameter]}
fitter = ModelFitter(WOLF_MODEL, ts[0:100],
parameterDct=parameterDct,
logger=logger, fitterMethods=[
"differential_evolution", "leastsq"])
fitter.fitModel()
self.assertTrue("J9_k" in fitter.reportFit())
def __init__(self, initialArgument, inputQ, outputQ,
logger=Logger()):
"""
Parameters
----------
initialArgument: Object
used by RunFunction
inputQ: multiprocessing.queue
outputQ: multiprocessing.queue
logger: Logger
"""
self.initialArgument = initialArgument
multiprocessing.Process.__init__(self)
self.inputQ = inputQ
self.outputQ = outputQ
self.logger = logger
def setupModel(cls, roadrunner, parameters, logger=Logger()):
"""
Sets up the model for use based on the parameter parameters
Parameters
----------
roadrunner: ExtendedRoadRunner
parameters: lmfit.Parameters
logger Logger
"""
pp = parameters.valuesdict()
for parameter in pp.keys():
try:
roadrunner.model[parameter] = pp[parameter]
except Exception as err:
msg = "_modelFitterCore.setupModel: Could not set value for %s" \
% parameter
logger.error(msg, err)
def __init__(self, cls, initialArguments, logger=Logger(), **kwargs):
"""
Parameters
----------
cls: AbstractServer
initialArguments: list
kwargs: dict
optional arguments for cls constructor
"""
self.cls = cls
self.logger = logger
self.numProcess = len(initialArguments)
# Create the servers
self.inputQs = [multiprocessing.Queue() for _ in range(self.numProcess)]
self.outputQs = [multiprocessing.Queue() for _ in range(self.numProcess)]
self.servers = [self.cls(initialArguments[i], self.inputQs[i],
self.outputQs[i], logger=self.logger, **kwargs)
for i in range(self.numProcess)]
_ = [s.start() for s in self.servers]
def mkSuiteFitter(modelSpecifications, datasets, parametersCol,
modelNames=None, modelWeights=None, fitterMethods=None,
numRestart=0, isParallel=False, logger=Logger(), **kwargs):
"""
Constructs a SuiteFitterCore with fitters that have similar
structure.
Parameters
----------
modelSpecifications: list-modelSpecification as in ModelFitter
datasets: list-observedData as in ModelFitter
paramersCol: list-parametersToFit as in ModelFitter
modelNames: list-str
modelWeights: list-float
how models are weighted in least squares
fitterMethods: list-optimization methods
numRestart: int
number of times the minimization is restarted with random
initial values for parameters to fit.
isParallel: bool
run fits in parallel for each fitter
logger: Logger
kwargs: dict
keyword arguments for ModelFitter
Returns
-------
SuiteFitter
"""
modelFitters = []
for modelSpecification, dataset, parametersToFit in \
zip(modelSpecifications, datasets, parametersCol):
modelFitter = ModelFitter(modelSpecification, dataset,
parametersToFit=parametersToFit, logger=logger, **kwargs)
modelFitters.append(modelFitter)
return SuiteFitter(modelFitters, modelNames=modelNames,
modelWeights=modelWeights, fitterMethods=fitterMethods,
numRestart=numRestart, isParallel=isParallel, logger=logger)
class Runner(object):
"""Runs tests on biomodels."""
def __init__(self,
firstModel: int = 210,
numModel: int = 2,
pclPath=PCL_FILE,
figPath=FIG_PATH,
useExistingData: bool = False,
isPlot=IS_PLOT,
**kwargDct):
"""
Parameters
----------
firstModel: first model to use
numModel: number of models to use
pclPath: file to which results are saved
useExistingData: use data in existing PCL file
"""
self.useExistingData = useExistingData and os.path.isfile(pclPath)
# Recover previously saved results if desired
if self.useExistingData:
self.restore(pclPath=pclPath)
else:
# Initialize based on type of context variable
for name in CONTEXT:
if name[-1:] == "s":
self.__setattr__(name, [])
elif name[-3:] == "Dct":
self.__setattr__(name, {})
elif name[-4:] == "Path":
self.__setattr__(name, None)
elif name[0:2] == "is":
self.__setattr__(name, False)
else:
self.__setattr__(name, 0)
# Initialize to parameters for this instantiation
self.firstModel = firstModel
self.numModel = numModel
self.pclPath = pclPath
self.figPath = figPath
self.kwargDct = kwargDct
self.isPlot = isPlot
self.useExistingData = useExistingData
#
if LOGGER in kwargDct.keys():
self.logger = kwargDct[LOGGER]
else:
self.logger = Logger()
kwargDct[LOGGER] = self.logger
self.save()
def _isListSame(self, list1, list2):
diff = set(list1).symmetric_difference(list2)
return len(diff) == 0
def equals(self, other):
selfKeys = list(self.__dict__.keys())
otherKeys = list(other.__dict__.keys())
if not self._isListSame(selfKeys, otherKeys):
return False
#
for key, value in self.__dict__.items():
if isinstance(value, list):
isEqual = self._isListSame(value, other.__getattribute__(key))
if not isEqual:
return False
elif any([isinstance(value, t) for t in [int, str, float, bool]]):
if self.__getattribute__(key) != other.__getattribute__(key):
return False
else:
pass
#
return True
def run(self):
"""
Runs the tests. Saves state after each tests.
"""
# Processing models
modelNums = self.firstModel + np.array(range(self.numModel))
for modelNum in modelNums:
if (modelNum in self.processedModels) and self.useExistingData:
continue
else:
self.processedModels.append(modelNum)
input_path = PATH_PAT % modelNum
msg = "Model %s" % input_path
self.logger.activity(msg)
try:
harness = TestHarness(input_path, **self.kwargDct)
if len(harness.parametersToFit) == 0:
self.logger.result("No fitable parameters in model.")
self.save()
continue
harness.evaluate(stdResiduals=1.0,
fractionParameterDeviation=1.0,
relError=2.0)
except Exception as err:
self.erroredModels.append(modelNum)
self.logger.error("TestHarness failed", err)
self.save()
continue
# Parameters for model
self.modelParameterDct[modelNum] = \
list(harness.fitModelResult.parameterRelErrorDct.keys())
# Relative error in initial fit
values = [
v for v in
harness.fitModelResult.parameterRelErrorDct.values()
]
self.fitModelRelerrors.extend(values)
# Relative error in bootstrap
values = [
v for v in
harness.bootstrapResult.parameterRelErrorDct.values()
]
self.bootstrapRelerrors.extend(values)
# Count models without exceptions
self.nonErroredModels.append(modelNum)
self.numNoError = len(self.nonErroredModels)
self.save()
# Check for plot
if self.isPlot:
self.plot()
def save(self):
"""
Saves state. Maintain in sync with self.restore().
"""
if self.pclPath is not None:
data = [self.__getattribute__(n) for n in CONTEXT]
with (open(self.pclPath, "wb")) as fd:
pickle.dump(data, fd)
def restore(self, pclPath=None):
"""
Restores state. Maintain in sync with self.save().
"""
if pclPath is None:
pclPath = self.pclPath
if os.path.isfile(pclPath):
with (open(pclPath, "rb")) as fd:
data = pickle.load(fd)
[self.__setattr__(n, v) for n, v in zip(CONTEXT, data)]
else:
raise ValueError("***Restart file %s does not exist" %
self.pclPath)
@staticmethod
def _pruneRelativeErrors(relativeErrors, maxError=MAX_RELATIVE_ERROR):
"""
Deletes Nans. Removes very large values.
Parameters
----------
list: relative errors
maxError: maximum relative error considered
Returns
-------
list: pruned errors
float: fraction pruned from non-nan values
"""
noNanErrors = [v for v in relativeErrors if not np.isnan(v)]
prunedErrors = [v for v in noNanErrors if v <= maxError]
prunedFrc = 1 - len(prunedErrors) / len(noNanErrors)
return prunedErrors, prunedFrc
def plot(self):
"""
Does all plots.
"""
_, axes = plt.subplots(1, 2)
prunedModelErrors, modelPrunedFrc = \
self._pruneRelativeErrors(self.fitModelRelerrors)
prunedBootstrapErrors, bootstrapPrunedFrc = \
self._pruneRelativeErrors(self.bootstrapRelerrors)
maxBin1 = self._plotRelativeErrors(axes[0], prunedModelErrors,
FIT_MODEL, modelPrunedFrc)
maxBin2 = self._plotRelativeErrors(axes[1],
prunedBootstrapErrors,
BOOTSTRAP,
bootstrapPrunedFrc,
isYLabel=False)
maxBin = max(maxBin1, maxBin2)
if maxBin > 0:
axes[0].set_ylim([0, maxBin])
axes[1].set_ylim([0, maxBin])
#
if len(self.processedModels) == 0:
frac = 0.0
else:
frac = 1.0 * self.numNoError / len(self.processedModels)
suptitle = "Models %d-%d. Fraction non-errored: %2.3f"
lastModel = self.firstModel + len(self.processedModels) - 1
suptitle = suptitle % (self.firstModel, lastModel, frac)
plt.suptitle(suptitle)
plt.show()
plt.savefig(self.figPath)
def _plotRelativeErrors(self,
ax,
relErrors,
title,
prunedFrc,
isYLabel=True):
"""
Plots histogram of relative errors.
Parameters
----------
ax: Matplotlib.axes
relErrors: list-float
title: str
prunedFrc: float
isYlabel: bool
Returns
-------
float: maximum number in a bin
"""
rr = ax.hist(relErrors)
fullTitle = "%s. Frc Pruned: %2.2f" % (title, prunedFrc)
ax.set_title(fullTitle)
ax.set_xlabel("relative error")
if isYLabel:
ax.set_ylabel("number parameters")
ax.set_xlim([0, 1])
return max(rr[0])
def __init__(self,
modelSpecification,
dataSources,
dirStudyPath=None,
instanceNames=None,
logger=Logger(),
useSerialized=True,
doSerialize=True,
isPlot=True,
**kwargs):
"""
Parameters
---------
modelSpecification: ExtendedRoadRunner/str
roadrunner model or antimony model
dataSources: list-NamedTimeseries/list-str or dict of either
str: path to CSV file
dirStudyPath: str
Path to the output directory containing the serialized fitters
for the study.
instanceNames: list-str
Names of study instances corresponds to the list of dataSources
useSerialized: bool
Use the serialization of each ModelFitter, if it exists
doSerialized: bool
Serialize each ModelFitter
isPlot: bool
Do plots
kwargs: dict
arguments passed to ModelFitter
"""
self.dirStudyPath = dirStudyPath # Path to the directory serialized ModelFitter
if self.dirStudyPath is None:
length = len(inspect.stack())
absPath = os.path.abspath((inspect.stack()[length - 1])[1])
dirCaller = os.path.dirname(absPath)
self.dirStudyPath = os.path.join(dirCaller, DIR_NAME)
self.isPlot = isPlot
self.doSerialize = doSerialize
self.useSerialized = useSerialized
self.instanceNames, self.dataSourceDct = self._mkInstanceData(
instanceNames, dataSources)
self.fitterPathDct = {
} # Path to serialized fitters; key is instanceName
self.fitterDct = {} # Fitters: key is instanceName
self.logger = logger
# Ensure that the directory exists
if not os.path.isdir(self.dirStudyPath):
os.makedirs(self.dirStudyPath)
# Construct the fitters
for name, dataSource in self.dataSourceDct.items():
filePath = self._getSerializePath(name)
self.fitterPathDct[name] = filePath
if os.path.isfile(filePath) and useSerialized:
self.fitterDct[name] = ModelFitter.deserialize(filePath)
else:
self.fitterDct[name] = ModelFitter(modelSpecification,
dataSource,
logger=self.logger,
isPlot=self.isPlot,
**kwargs)
self._serializeFitter(name)
def rpRevise(self):
"""
Overrides rpickler.
"""
if "logger" not in self.__dict__.keys():
self.logger = Logger()
class TestLogger(unittest.TestCase):
def setUp(self):
self.remove()
self.logger = Logger(toFile=LOG_PATH,
logPerformance=True,
logLevel=logs.LEVEL_MAX)
def tearDown(self):
self.remove()
def remove(self):
for ffile in FILES:
if os.path.isfile(ffile):
os.remove(ffile)
def isFile(self):
return os.path.isfile(LOG_PATH)
def read(self):
if not self.isFile():
raise RuntimeError("Missing log file.")
with open(LOG_PATH, "r") as fd:
lines = fd.readlines()
return lines
def testConstructor(self):
if IGNORE_TEST:
return
self.assertFalse(self.isFile())
self.assertEqual(self.logger.logLevel, logs.LEVEL_MAX)
def testFileDescriptor(self):
if IGNORE_TEST:
return
fd = self.logger.getFileDescriptor()
self.assertIsInstance(fd, io.TextIOWrapper)
fd.close()
def _checkMsg(self, msg):
lines = self.read()
true = any([MSG in t for t in lines])
self.assertTrue(true)
return lines
def testWrite(self):
if IGNORE_TEST:
return
self.logger._write(MSG, 0)
_ = self._checkMsg(MSG)
def _testApi(self, method, logLevel):
if IGNORE_TEST:
return
logger = Logger(toFile=LOG_PATH, logLevel=logLevel)
stmt = "logger.%s(MSG)" % method
exec(stmt)
line1s = self._checkMsg(MSG)
#
logger = Logger(toFile=LOG_PATH, logLevel=0)
stmt = "logger.%s(MSG)" % method
exec(stmt)
line2s = self.read()
self.assertEqual(len(line1s), len(line2s))
def testActivity(self):
if IGNORE_TEST:
return
self._testApi("activity", logs.LEVEL_ACTIVITY)
def testResult(self):
if IGNORE_TEST:
return
self._testApi("result", logs.LEVEL_RESULT)
def testStatus(self):
if IGNORE_TEST:
return
self._testApi("status", logs.LEVEL_STATUS)
def testException(self):
if IGNORE_TEST:
return
self._testApi("status", logs.LEVEL_EXCEPTION)
def testStartBlock(self):
if IGNORE_TEST:
return
guid = self.logger.startBlock(BLOCK1)
self.assertLess(guid, BlockSpecification.guid)
self.assertEqual(len(self.logger.blockDct), 1)
def testEndBlock(self):
if IGNORE_TEST:
return
guid1 = self.logger.startBlock(BLOCK1)
guid2 = self.logger.startBlock(BLOCK2)
self.logger.endBlock(guid2)
self.logger.endBlock(guid1)
self.assertGreater(self.logger.statisticDct[BLOCK1].total,
self.logger.statisticDct[BLOCK2].total)
def testNoLogPerformance(self):
if IGNORE_TEST:
return
logger = Logger(toFile=LOG_PATH,
logPerformance=False,
logLevel=logs.LEVEL_MAX)
guid = logger.startBlock(BLOCK1)
self.assertEqual(len(self.logger.blockDct), 0)
logger.endBlock(guid)
self.assertEqual(len(self.logger.blockDct), 0)
def testPerformanceReport(self):
if IGNORE_TEST:
return
def test(numBlock, sleepTime):
logger = Logger(logPerformance=True)
for idx in range(numBlock):
block = "blk_%d" % idx
guid = logger.startBlock(block)
time.sleep(sleepTime)
logger.endBlock(guid)
df = logger.performanceDF
self.assertLess(np.abs(sleepTime - df["mean"].mean()), sleepTime)
self.assertEqual(df["count"].mean(), 1.0)
#
test(3, 0.1)
test(30, 0.1)
def testJoin(self):
if IGNORE_TEST:
return
NAMES = ["aa", "bbb", "z"]
result = Logger.join(*NAMES)
for name in NAMES:
self.assertGreaterEqual(result.index(name), 0)
def testCopy(self):
if IGNORE_TEST:
return
newLogger = self.logger.copy()
self.assertTrue(self.logger.equals(newLogger))
class ModelFitterCore(rpickle.RPickler):
def __init__(
self,
modelSpecification,
observedData,
# The following must be kept in sync with ModelFitterBootstrap.bootstrap
parametersToFit=None, # Must be first kw for backwards compatibility
bootstrapMethods=None,
endTime=None,
fitterMethods=None,
isPlot=True,
logger=Logger(),
_loggerPrefix="",
maxProcess: int = None,
numFitRepeat=1,
numIteration: int = 10,
numPoint=None,
numRestart=0,
parameterLowerBound=cn.PARAMETER_LOWER_BOUND,
parameterUpperBound=cn.PARAMETER_UPPER_BOUND,
selectedColumns=None,
serializePath: str = None,
isParallel=True,
isProgressBar=True,
):
"""
Constructs estimates of parameter values.
Parameters
----------
endTime: float
end time for the simulation
modelSpecification: ExtendedRoadRunner/str
roadrunner model or antimony model
observedData: NamedTimeseries/str
str: path to CSV file
parametersToFit: list-str/SBstoat.Parameter/None
parameters in the model that you want to fit
if None, no parameters are fit
selectedColumns: list-str
species names you wish use to fit the model
default: all columns in observedData
parameterLowerBound: float
lower bound for the fitting parameters
parameterUpperBound: float
upper bound for the fitting parameters
logger: Logger
fitterMethods: str/list-str/list-OptimizerMethod
method used for minimization in fitModel
numFitRepeat: int
number of times fitting is repeated for a method
bootstrapMethods: str/list-str/list-OptimizerMethod
method used for minimization in bootstrap
numIteration: number of bootstrap iterations
numPoint: int
number of time points in the simulation
maxProcess: Maximum number of processes to use. Default: numCPU
serializePath: Where to serialize the fitter after bootstrap
numRestart: int
number of times the minimization is restarted with random
initial values for parameters to fit.
isParallel: bool
run in parallel where possible
isProgressBar: bool
display the progress bar
Usage
-----
parametersToFit = [SBstoat.Parameter("k1", lower=1, upper=10, value=5),
SBstoat.Parameter("k2", lower=2, upper=6, value=3),
]
ftter = ModelFitter(roadrunnerModel, "observed.csv",
parametersToFit=parametersToFit)
fitter.fitModel() # Do the fit
fitter.bootstrap() # Estimate parameter variance with bootstrap
"""
if modelSpecification is not None:
# Not the default constructor
self._numIteration = numIteration # Save for copy constructor
self._serializePath = serializePath # Save for copy constructor
self._loggerPrefix = _loggerPrefix
self.modelSpecification = modelSpecification
self.observedData = observedData
self.parametersToFit = parametersToFit
self.parameterLowerBound = parameterLowerBound
self.parameterUpperBound = parameterUpperBound
self._maxProcess = maxProcess
self.bootstrapKwargs = dict(
numIteration=numIteration,
serializePath=serializePath,
)
self._numFitRepeat = numFitRepeat
self.selectedColumns = selectedColumns
self.observedTS, self.selectedColumns = self._updateObservedTS(
mkNamedTimeseries(self.observedData))
# Check for nan in observedTS
self._isObservedNan = np.isnan(np.sum(self.observedTS.flatten()))
#
self.selectedColumns = [c.strip() for c in self.selectedColumns]
self.numPoint = numPoint
if self.numPoint is None:
self.numPoint = len(self.observedTS)
self.endTime = endTime
if self.endTime is None:
self.endTime = self.observedTS.end
# Other internal state
self._fitterMethods = ModelFitterCore.makeMethods(
fitterMethods, cn.METHOD_FITTER_DEFAULTS)
self._bootstrapMethods = ModelFitterCore.makeMethods(
bootstrapMethods, cn.METHOD_BOOTSTRAP_DEFAULTS)
if isinstance(self._bootstrapMethods, str):
self._bootstrapMethods = [self._bootstrapMethods]
self._isPlot = isPlot
self._plotter = tp.TimeseriesPlotter(isPlot=self._isPlot)
self.logger = logger
self._numRestart = numRestart
self._isParallel = isParallel
self._isProgressBar = isProgressBar
self._selectedIdxs = None
self._params = self.mkParams()
# The following are calculated during fitting
self.roadrunnerModel = None
self.minimizerResult = None # Results of minimization
self.fittedTS = self.observedTS.copy(
isInitialize=True) # Initialize
self.residualsTS = None # Residuals for selectedColumns
self.bootstrapResult = None # Result from bootstrapping
self.optimizer = None
self.suiteFitterParams = None # Result from a suite fitter
#
else:
pass
def copy(self, isKeepLogger=False, isKeepOptimizer=False, **kwargs):
"""
Creates a copy of the model fitter, overridding any argument
that is not None.
Preserves the user-specified settings and the results
of bootstrapping.
Parameters
----------
isKeepLogger: bool
isKeepOptimizer: bool
kwargs: dict
arguments to override in copy
Returns
-------
ModelFitter
"""
def setValues(names):
"""
Sets the value for a list of names.
Parameters
----------
names: list-str
Returns
-------
list-object
"""
results = []
for name in names:
altName = "_%s" % name
if name in kwargs.keys():
results.append(kwargs[name])
elif name in self.__dict__.keys():
results.append(self.__dict__[name])
elif altName in self.__dict__.keys():
results.append(self.__dict__[altName])
else:
raise RuntimeError("%s: not found" % name)
return results
#
# Get the positional and keyword arguments
inspectResult = inspect.getfullargspec(ModelFitterCore.__init__)
allArgNames = inspectResult.args[1:] # Ignore 'self'
if inspectResult.defaults is None:
numKwarg = 0
else:
numKwarg = len(inspectResult.defaults)
numParg = len(allArgNames) - numKwarg # positional arguments
pargNames = allArgNames[:numParg]
kwargNames = allArgNames[numParg:]
# Construct the arguments
callPargs = setValues(pargNames)
callKwargValues = setValues(kwargNames)
callKwargs = {n: v for n, v in zip(kwargNames, callKwargValues)}
if numKwarg > 0:
# Adjust model specification
modelSpecificationIdx = pargNames.index("modelSpecification")
observedDataIdx = pargNames.index("observedData")
modelSpecification = callPargs[modelSpecificationIdx]
observedTS = NamedTimeseries(callPargs[observedDataIdx])
selectedColumns = callKwargs["selectedColumns"]
if not isinstance(modelSpecification, str):
try:
modelSpecification = self.modelSpecification.getAntimony()
callPargs[modelSpecificationIdx] = modelSpecification
observedTS, selectedColumns = self._adjustNames(
modelSpecification, observedTS)
callPargs[observedDataIdx] = observedTS
callKwargs["selectedColumns"] = selectedColumns
except Exception as err:
self.logger.error(
"Problem wth conversion to Antimony. Details:", err)
raise ValueError("Cannot proceed.")
#
if isKeepLogger:
callKwargs["logger"] = self.logger
if numParg < 2:
callPargs = [None, None]
newModelFitter = self.__class__(*callPargs, **callKwargs)
if self.optimizer is not None:
if isKeepOptimizer:
newModelFitter.optimizer = self.optimizer.copyResults()
if self.bootstrapResult is not None:
newModelFitter.bootstrapResult = self.bootstrapResult.copy()
return newModelFitter
def _updateSelectedIdxs(self):
resultTS = self.simulate()
if resultTS is not None:
self._selectedIdxs = \
ModelFitterCore.selectCompatibleIndices(resultTS[TIME],
self.observedTS[TIME])
else:
self._selectedIdxs = list(range(self.numPoint))
def _updateObservedTS(self, newObservedTS, isCheck=True):
"""
Changes just the observed timeseries. The new timeseries must have
the same shape as the old one. Also used on initialization,
in which case, the check should be disabled.
Parameters
----------
newObservedTS: NamedTimeseries
isCheck: Bool
verify that new timeseries as the same shape as the old one
Returns
-------
NamedTimeseries
ObservedTS
list-str
selectedColumns
"""
if isCheck:
isError = False
if not isinstance(newObservedTS, NamedTimeseries):
isError = True
if "observedTS" in self.__dict__.keys():
isError = isError \
or (not self.observedTS.equalSchema(newObservedTS))
if isError:
raise RuntimeError(
"Timeseries argument: incorrect type or shape.")
#
self.observedTS = newObservedTS
if (self.selectedColumns is None) and (self.observedTS is not None):
self.selectedColumns = self.observedTS.colnames
if self.observedTS is None:
self._observedArr = None
else:
self._observedArr = self.observedTS[self.selectedColumns].flatten()
#
return self.observedTS, self.selectedColumns
@property
def params(self):
params = None
#
if params is None:
if self.suiteFitterParams is not None:
params = self.suiteFitterParams
if self.bootstrapResult is not None:
if self.bootstrapResult.params is not None:
params = self.bootstrapResult.params
if params is None:
if self.optimizer is not None:
params = self.optimizer.params
else:
params = self._params
return params
@staticmethod
def makeMethods(methods, default):
"""
Creates a method dictionary.
Parameters
----------
methods: str/list-str/dict
method used for minimization in fitModel
dict: key-method, value-optional parameters
Returns
-------
list-OptimizerMethod
key: method name
value: dict of optional parameters
"""
if methods is None:
methods = default
if isinstance(methods, str):
if methods == cn.METHOD_BOTH:
methods = cn.METHOD_FITTER_DEFAULTS
else:
methods = [methods]
if isinstance(methods, list):
if isinstance(methods[0], str):
results = [
_helpers.OptimizerMethod(method=m, kwargs={})
for m in methods
]
else:
results = methods
else:
raise RuntimeError("Must be a list")
trues = [isinstance(m, _helpers.OptimizerMethod) for m in results]
if not all(trues):
raise ValueError("Invalid methods: %s" % str(methods))
return results
@classmethod
def mkParameters(
cls,
parametersToFit: list,
logger: Logger = Logger(),
parameterLowerBound: float = cn.PARAMETER_LOWER_BOUND,
parameterUpperBound: float = cn.PARAMETER_UPPER_BOUND
) -> lmfit.Parameters:
"""
Constructs lmfit parameters based on specifications.
Parameters
----------
parametersToFit: list-Parameter/list-str
logger: error logger
parameterLowerBound: lower value of range for parameters
parameterUpperBound: upper value of range for parameters
Returns
-------
lmfit.Parameters
"""
if len(parametersToFit) == 0:
raise RuntimeError("Must specify at least one parameter.")
if logger is None:
logger = logger()
# Process each parameter
elements = []
for element in parametersToFit:
# Get the lower bound, upper bound, and initial value for the parameter
if not isinstance(element, SBstoat.Parameter):
element = SBstoat.Parameter(element,
lower=parameterLowerBound,
upper=parameterUpperBound)
elements.append(element)
return SBstoat.Parameter.mkParameters(elements)
@classmethod
def initializeRoadrunnerModel(cls, modelSpecification):
"""
Sets self.roadrunnerModel.
Parameters
----------
modelSpecification: ExtendedRoadRunner/str
Returns
-------
ExtendedRoadRunner
"""
if isinstance(modelSpecification,
te.roadrunner.extended_roadrunner.ExtendedRoadRunner):
roadrunnerModel = modelSpecification
elif isinstance(modelSpecification, str):
roadrunnerModel = te.loada(modelSpecification)
else:
msg = 'Invalid model.'
msg = msg + "\nA model must either be a Roadrunner model "
msg = msg + "an Antimony model."
raise ValueError(msg)
return roadrunnerModel
@classmethod
def setupModel(cls, roadrunner, parameters, logger=Logger()):
"""
Sets up the model for use based on the parameter parameters
Parameters
----------
roadrunner: ExtendedRoadRunner
parameters: lmfit.Parameters
logger Logger
"""
pp = parameters.valuesdict()
for parameter in pp.keys():
try:
roadrunner.model[parameter] = pp[parameter]
except Exception as err:
msg = "_modelFitterCore.setupModel: Could not set value for %s" \
% parameter
logger.error(msg, err)
@classmethod
def runSimulation(cls, **kwargs):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Returns a NamedTimeseries.
Return
------
NamedTimeseries (or None if fail to converge)
"""
return NamedTimeseries(namedArray=cls.runSimulationNumpy(**kwargs))
@classmethod
def runSimulationDF(cls, **kwargs):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Returns a NamedTimeseries.
Return
------
pd.DataFrame
"""
fittedTS = NamedTimeseries(namedArray=cls.runSimulationArr(**kwargs))
return fittedTS.to_dataframe()
@classmethod
def runSimulationNumpy(
cls,
parameters=None,
modelSpecification=None,
startTime=0,
endTime=5,
numPoint=30,
selectedColumns=None,
_logger=Logger(),
_loggerPrefix="",
):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Returns a NamedArray
Parameters
----------
modelSpecification: ExtendedRoadRunner/str
Roadrunner model
parameters: lmfit.Parameters
lmfit parameters
startTime: float
start time for the simulation
endTime: float
end time for the simulation
numPoint: int
number of points in the simulation
selectedColumns: list-str
output columns in simulation
_logger: Logger
_loggerPrefix: str
Return
------
NamedArray (or None if fail to converge)
"""
roadrunnerModel = modelSpecification
if isinstance(modelSpecification, str):
roadrunnerModel = cls.initializeRoadrunnerModel(roadrunnerModel)
else:
roadrunnerModel.reset()
if parameters is not None:
# Parameters have been specified
cls.setupModel(roadrunnerModel, parameters, logger=_logger)
# Do the simulation
if selectedColumns is not None:
newSelectedColumns = list(selectedColumns)
if TIME not in newSelectedColumns:
newSelectedColumns.insert(0, TIME)
try:
dataArr = roadrunnerModel.simulate(startTime, endTime,
numPoint,
newSelectedColumns)
except Exception as err:
_logger.error("Roadrunner exception: ", err)
dataArr = None
else:
try:
dataArr = roadrunnerModel.simulate(startTime, endTime,
numPoint)
except Exception as err:
_logger.exception("Roadrunner exception: %s" % err)
dataArr = None
return dataArr
@classmethod
def rpConstruct(cls):
"""
Overrides rpickler.rpConstruct to create a method that
constructs an instance without arguments.
Returns
-------
Instance of cls
"""
return cls(None, None, None)
def rpRevise(self):
"""
Overrides rpickler.
"""
if "logger" not in self.__dict__.keys():
self.logger = Logger()
def _adjustNames(self, antimonyModel:str, observedTS:NamedTimeseries) \
->typing.Tuple[NamedTimeseries, list]:
"""
Antimony exports can change the names of floating species
by adding a "_" at the end. Check for this and adjust
the names in observedTS.
Return
------
NamedTimeseries: newObservedTS
list: newSelectedColumns
"""
rr = te.loada(antimonyModel)
dataNames = rr.simulate().colnames
names = ["[%s]" % n for n in observedTS.colnames]
missingNames = [n[1:-1] for n in set(names).difference(dataNames)]
newSelectedColumns = list(self.selectedColumns)
if len(missingNames) > 0:
newObservedTS = observedTS.copy()
self.logger.exception("Missing names in antimony export: %s" %
str(missingNames))
for name in observedTS.colnames:
missingName = "%s_" % name
if name in missingNames:
newObservedTS = newObservedTS.rename(name, missingName)
newSelectedColumns.remove(name)
newSelectedColumns.append(missingName)
else:
newObservedTS = observedTS
return newObservedTS, newSelectedColumns
def clean(self):
"""
Cleans the object so that it can be pickled.
"""
self.roadrunnerModel = None
return self
def initializeRoadRunnerModel(self):
"""
Sets self.roadrunnerModel.
"""
if self.roadrunnerModel is None:
self.roadrunnerModel = ModelFitterCore.initializeRoadrunnerModel(
self.modelSpecification)
def getDefaultParameterValues(self):
"""
Obtain the original values of parameters.
Returns
-------
list-SBstoat.Parameter
"""
dct = {}
self.initializeRoadRunnerModel()
self.roadrunnerModel.reset()
for parameterName in self.parametersToFit:
dct[parameterName] = self.roadrunnerModel.model[parameterName]
return dct
def simulate(self, **kwargs):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Parameters
----------
params: lmfit.Parameters
startTime: float
endTime: float
numPoint: int
Return
------
NamedTimeseries
"""
fixedArr = self._simulateNumpy(**kwargs)
return NamedTimeseries(namedArray=fixedArr)
def _simulateNumpy(self,
params=None,
startTime=None,
endTime=None,
numPoint=None):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Parameters
----------
params: lmfit.Parameters
startTime: float
endTime: float
numPoint: int
Return
------
NamedTimeseries
"""
def setValue(default, parameter):
# Sets to default if parameter unspecified
if parameter is None:
return default
return parameter
#
startTime = setValue(self.observedTS.start, startTime)
endTime = setValue(self.endTime, endTime)
numPoint = setValue(self.numPoint, numPoint)
#
if self.roadrunnerModel is None:
self.initializeRoadRunnerModel()
#
return self.runSimulationNumpy(parameters=params,
modelSpecification=self.roadrunnerModel,
startTime=startTime,
endTime=endTime,
numPoint=numPoint,
selectedColumns=self.selectedColumns,
_logger=self.logger,
_loggerPrefix=self._loggerPrefix)
def updateFittedAndResiduals(self, **kwargs) -> np.ndarray:
"""
Updates values of self.fittedTS and self.residualsTS
based on self.params.
Parameters
----------
kwargs: dict
arguments for simulation
Instance Variables Updated
--------------------------
self.fittedTS
self.residualsTS
Returns
-------
1-d ndarray of residuals
"""
self.fittedTS = self.simulate(**kwargs) # Updates self.fittedTS
if self._selectedIdxs is None:
self._updateSelectedIdxs()
self.fittedTS = self.fittedTS[self._selectedIdxs]
residualsArr = self.calcResiduals(self.params)
numRow = len(self.fittedTS)
numCol = len(residualsArr) // numRow
residualsArr = np.reshape(residualsArr, (numRow, numCol))
cols = self.selectedColumns
if self.residualsTS is None:
self.residualsTS = self.observedTS.subsetColumns(cols)
self.residualsTS[cols] = residualsArr
@staticmethod
def selectCompatibleIndices(bigTimes, smallTimes):
"""
Finds the indices such that smallTimes[n] is close to bigTimes[indices[n]]
Parameters
----------
bigTimes: np.ndarray
smalltimes: np.ndarray
Returns
np.ndarray
"""
indices = []
for idx, _ in enumerate(smallTimes):
distances = (bigTimes - smallTimes[idx])**2
def getValue(k):
return distances[k]
thisIndices = sorted(range(len(distances)), key=getValue)
indices.append(thisIndices[0])
return np.array(indices)
def calcResiduals(self, params) -> np.ndarray:
"""
Compute the residuals between objective and experimental data
Handle nan values in observedTS. This internal-only method
is implemented to maximize efficieency.
Parameters
----------
params: lmfit.Parameters
arguments for simulation
Returns
-------
1-d ndarray of residuals
"""
if self._selectedIdxs is None:
self._updateSelectedIdxs()
dataArr = ModelFitterCore.runSimulationNumpy(
parameters=params,
modelSpecification=self.roadrunnerModel,
startTime=self.observedTS.start,
endTime=self.endTime,
numPoint=self.numPoint,
selectedColumns=self.selectedColumns,
_logger=self.logger,
_loggerPrefix=self._loggerPrefix)
if dataArr is None:
residualsArr = np.repeat(LARGE_RESIDUAL, len(self._observedArr))
else:
truncatedArr = dataArr[self._selectedIdxs, 1:]
truncatedArr = truncatedArr.flatten()
residualsArr = self._observedArr - truncatedArr
if self._isObservedNan:
residualsArr = np.nan_to_num(residualsArr)
return residualsArr
def fitModel(self, params: lmfit.Parameters = None):
"""
Fits the model by adjusting values of parameters based on
differences between simulated and provided values of
floating species.
Parameters
----------
params: lmfit.parameters
starting values of parameters
Example
-------
f.fitModel()
"""
if params is None:
params = self.params
self.initializeRoadRunnerModel()
if self.parametersToFit is not None:
self.optimizer = Optimizer.optimize(self.calcResiduals,
params,
self._fitterMethods,
logger=self.logger,
numRestart=self._numRestart)
self.minimizerResult = self.optimizer.minimizerResult
# Ensure that residualsTS and fittedTS match the parameters
self.updateFittedAndResiduals(params=self.params)
def getFittedModel(self):
"""
Provides the roadrunner model with fitted parameters
Returns
-------
ExtendedRoadrunner
"""
self._checkFit()
self.roadrunnerModel.reset()
self._setupModel(self.params)
return self.roadrunnerModel
def _setupModel(self, parameters):
"""
Sets up the model for use based on the parameter parameters
Parameters
----------
parameters: lmfit.Parameters
"""
ModelFitterCore.setupModel(self.roadrunnerModel,
parameters,
logger=self.logger)
def mkParams(self, parametersToFit: list = None) -> lmfit.Parameters:
"""
Constructs lmfit parameters based on specifications.
Parameters
----------
parametersToFit: list-Parameter
Returns
-------
lmfit.Parameters
"""
if parametersToFit is None:
parametersToFit = self.parametersToFit
if parametersToFit is not None:
return ModelFitterCore.mkParameters(
parametersToFit,
logger=self.logger,
parameterLowerBound=self.parameterLowerBound,
parameterUpperBound=self.parameterUpperBound)
else:
return None
def _checkFit(self):
if self.params is None:
raise ValueError("Must use fitModel before using this method.")
def serialize(self, path):
"""
Serialize the model to a path.
Parameters
----------
path: str
File path
"""
newModelFitter = self.copy()
with open(path, "wb") as fd:
rpickle.dump(newModelFitter, fd)
@classmethod
def deserialize(cls, path):
"""
Deserialize the model from a path.
Parameters
----------
path: str
File path
Return
------
ModelFitter
Model is initialized.
"""
with open(path, "rb") as fd:
fitter = rpickle.load(fd)
fitter.initializeRoadRunnerModel()
return fitter
def __init__(
self,
modelSpecification,
observedData,
# The following must be kept in sync with ModelFitterBootstrap.bootstrap
parametersToFit=None, # Must be first kw for backwards compatibility
bootstrapMethods=None,
endTime=None,
fitterMethods=None,
isPlot=True,
logger=Logger(),
_loggerPrefix="",
maxProcess: int = None,
numFitRepeat=1,
numIteration: int = 10,
numPoint=None,
numRestart=0,
parameterLowerBound=cn.PARAMETER_LOWER_BOUND,
parameterUpperBound=cn.PARAMETER_UPPER_BOUND,
selectedColumns=None,
serializePath: str = None,
isParallel=True,
isProgressBar=True,
):
"""
Constructs estimates of parameter values.
Parameters
----------
endTime: float
end time for the simulation
modelSpecification: ExtendedRoadRunner/str
roadrunner model or antimony model
observedData: NamedTimeseries/str
str: path to CSV file
parametersToFit: list-str/SBstoat.Parameter/None
parameters in the model that you want to fit
if None, no parameters are fit
selectedColumns: list-str
species names you wish use to fit the model
default: all columns in observedData
parameterLowerBound: float
lower bound for the fitting parameters
parameterUpperBound: float
upper bound for the fitting parameters
logger: Logger
fitterMethods: str/list-str/list-OptimizerMethod
method used for minimization in fitModel
numFitRepeat: int
number of times fitting is repeated for a method
bootstrapMethods: str/list-str/list-OptimizerMethod
method used for minimization in bootstrap
numIteration: number of bootstrap iterations
numPoint: int
number of time points in the simulation
maxProcess: Maximum number of processes to use. Default: numCPU
serializePath: Where to serialize the fitter after bootstrap
numRestart: int
number of times the minimization is restarted with random
initial values for parameters to fit.
isParallel: bool
run in parallel where possible
isProgressBar: bool
display the progress bar
Usage
-----
parametersToFit = [SBstoat.Parameter("k1", lower=1, upper=10, value=5),
SBstoat.Parameter("k2", lower=2, upper=6, value=3),
]
ftter = ModelFitter(roadrunnerModel, "observed.csv",
parametersToFit=parametersToFit)
fitter.fitModel() # Do the fit
fitter.bootstrap() # Estimate parameter variance with bootstrap
"""
if modelSpecification is not None:
# Not the default constructor
self._numIteration = numIteration # Save for copy constructor
self._serializePath = serializePath # Save for copy constructor
self._loggerPrefix = _loggerPrefix
self.modelSpecification = modelSpecification
self.observedData = observedData
self.parametersToFit = parametersToFit
self.parameterLowerBound = parameterLowerBound
self.parameterUpperBound = parameterUpperBound
self._maxProcess = maxProcess
self.bootstrapKwargs = dict(
numIteration=numIteration,
serializePath=serializePath,
)
self._numFitRepeat = numFitRepeat
self.selectedColumns = selectedColumns
self.observedTS, self.selectedColumns = self._updateObservedTS(
mkNamedTimeseries(self.observedData))
# Check for nan in observedTS
self._isObservedNan = np.isnan(np.sum(self.observedTS.flatten()))
#
self.selectedColumns = [c.strip() for c in self.selectedColumns]
self.numPoint = numPoint
if self.numPoint is None:
self.numPoint = len(self.observedTS)
self.endTime = endTime
if self.endTime is None:
self.endTime = self.observedTS.end
# Other internal state
self._fitterMethods = ModelFitterCore.makeMethods(
fitterMethods, cn.METHOD_FITTER_DEFAULTS)
self._bootstrapMethods = ModelFitterCore.makeMethods(
bootstrapMethods, cn.METHOD_BOOTSTRAP_DEFAULTS)
if isinstance(self._bootstrapMethods, str):
self._bootstrapMethods = [self._bootstrapMethods]
self._isPlot = isPlot
self._plotter = tp.TimeseriesPlotter(isPlot=self._isPlot)
self.logger = logger
self._numRestart = numRestart
self._isParallel = isParallel
self._isProgressBar = isProgressBar
self._selectedIdxs = None
self._params = self.mkParams()
# The following are calculated during fitting
self.roadrunnerModel = None
self.minimizerResult = None # Results of minimization
self.fittedTS = self.observedTS.copy(
isInitialize=True) # Initialize
self.residualsTS = None # Residuals for selectedColumns
self.bootstrapResult = None # Result from bootstrapping
self.optimizer = None
self.suiteFitterParams = None # Result from a suite fitter
#
else:
pass
import numpy as np
import os
import unittest
IGNORE_TEST = False
IS_PLOT = False
DIR = os.path.dirname(os.path.abspath(__file__))
LOG_PATH = os.path.join(DIR, "testTestHarness.log")
DATA_DIR = os.path.join(os.path.dirname(DIR), "biomodels")
PATH_PAT = os.path.join(DATA_DIR, "BIOMD0000000%03d.xml")
INPUT_PATH = PATH_PAT % 339
VARIABLE_NAMES = ["Va_Xa", "IIa_Tmod", "VIIa_TF"]
PARAMETER_NAMES = ["r27_c", "r28_c", "r29_c"]
VARIABLE_NAMES = ["Pk", "VK"]
PARAMETER_NAMES = ["d_Pk", "d_VK"]
LOGGER = Logger()
if os.path.isfile(LOG_PATH):
os.remove(LOG_PATH)
class TestFunctions(unittest.TestCase):
def setUp(self):
if IGNORE_TEST:
return
self.harness = TestHarness(INPUT_PATH,
PARAMETER_NAMES,
VARIABLE_NAMES,
logger=LOGGER)
def testConstructor(self):
import unittest
import matplotlib
import matplotlib.pyplot as plt
IGNORE_TEST = False
IS_PLOT = False
DIR = os.path.dirname(os.path.abspath(__file__))
PCL_PATH = os.path.join(DIR, "testMainTestHarness.pcl")
FIG_PATH = os.path.join(DIR, "testMainTestHarness.png")
FILES = [PCL_PATH, FIG_PATH]
FIRST_MODEL = 200
NUM_MODEL = 1
DIR = os.path.dirname(os.path.abspath(__file__))
LOG_FILE = os.path.join(DIR, "testMainTestHarness.log")
if IGNORE_TEST:
LOGGER = Logger()
else:
LOGGER = Logger(toFile=LOG_FILE)
if os.path.isfile(LOG_FILE):
os.remove(LOG_FILE)
class TestRunner(unittest.TestCase):
def setUp(self):
self._remove()
self.runner = Runner(firstModel=FIRST_MODEL,
numModel=NUM_MODEL,
useExistingData=False,
figPath=FIG_PATH,
pclPath=PCL_PATH,
class BootstrapRunner(AbstractRunner):
def __init__(self, runnerArgument):
"""
Parameters
----------
runnerArgument: RunnerArgument
Notes
-----
1. Uses METHOD_LEASTSQ for fitModel iterations.
"""
super().__init__()
#
self.lastErr = ""
self.fitter = runnerArgument.fitter
self.numIteration = runnerArgument.numIteration
self.kwargs = runnerArgument.kwargs
self.synthesizerClass = runnerArgument.synthesizerClass
if "logger" in self.fitter.__dict__.keys():
self.logger = self.fitter.logger
else:
self.logger = Logger()
self._isDone = not self._fitInitial()
self.columns = self.fitter.selectedColumns
# Initializations for bootstrap loop
if not self.isDone:
fittedTS = self.fitter.fittedTS.subsetColumns(self.columns,
isCopy=False)
self.synthesizer = self.synthesizerClass(
observedTS=self.fitter.observedTS.subsetColumns(self.columns,
isCopy=False),
fittedTS=fittedTS,
**self.kwargs)
self.numSuccessIteration = 0
if self.fitter.minimizerResult is None:
self.fitter.fitModel()
self.baseChisq = self.fitter.minimizerResult.redchi
self.curIteration = 0
self.fd = self.logger.getFileDescriptor()
self.baseFittedStatistic = TimeseriesStatistic(
self.fitter.observedTS.subsetColumns(
self.fitter.selectedColumns, isCopy=False))
def report(self, id=None):
if True:
return
if id is None:
self._startTime = time.time()
else:
elapsed = time.time() - self._startTime
print("%s: %2.3f" % (id, elapsed))
@property
def numWorkUnit(self):
return self.numIteration
@property
def isDone(self):
return self._isDone
def run(self):
"""
Runs the bootstrap.
Returns
-------
BootstrapResult
"""
def mkNullResult():
fittedStatistic = TimeseriesStatistic(
self.fitter.observedTS[self.fitter.selectedColumns])
return BootstrapResult(self.fitter, 0, {}, fittedStatistic)
#
if self.isDone:
return
# Set up logging for this run
if self.fd is not None:
sys.stderr = self.fd
sys.stdout = self.fd
isSuccess = False
bootstrapError = 0
self.report()
for _ in range(ITERATION_MULTIPLIER):
newObservedTS = self.synthesizer.calculate()
self.report("newObservedTS")
# Update fitter to use the new observed data
_ = self.fitter._updateObservedTS(newObservedTS, isCheck=False)
self.report("updated fitter")
# Try fitting
try:
self.fitter.fitModel(params=self.fitter.params)
self.report("fitter.fit")
except Exception as err:
# Problem with the fit.
msg = "modelFitterBootstrap. Fit failed on iteration %d." \
% iteration
self.logger.error(msg, err)
bootstrapError += 1
continue
# Verify that there is a result
if self.fitter.minimizerResult is None:
continue
# Check if the fit is of sufficient quality
if self.fitter.minimizerResult.redchi > MAX_CHISQ_MULT * self.baseChisq:
continue
if self.fitter.params is None:
continue
isSuccess = True
self.report("break")
break
# Create the result
if isSuccess:
self.numSuccessIteration += 1
parameterDct = {
k: [v]
for k, v in self.fitter.params.valuesdict().items()
}
fittedStatistic = self.baseFittedStatistic.copy()
fittedStatistic.accumulate(
self.fitter.fittedTS.subsetColumns(self.fitter.selectedColumns,
isCopy=False))
bootstrapResult = BootstrapResult(self.fitter,
self.numSuccessIteration,
parameterDct,
fittedStatistic,
bootstrapError=bootstrapError)
else:
bootstrapResult = mkNullResult()
self._isDone = True
# Close the logging file
if self.fd is not None:
if not self.fd.closed:
self.fd.close()
# See if completed work
if self.numSuccessIteration >= self.numIteration:
self._isDone = True
return bootstrapResult
def _fitInitial(self):
"""
Do the initial fit.
Returns
-------
bool
successful fit
"""
isSuccess = False
for _ in range(MAX_TRIES):
try:
self.fitter.fitModel() # Initialize model
isSuccess = True
break
except Exception as err:
self.lastErr = err
msg = "Could not do initial fit"
self.logger.error(msg, err)
return isSuccess
def setUp(self):
self.remove()
self.logger = Logger(toFile=LOG_PATH,
logPerformance=True,
logLevel=logs.LEVEL_MAX)
def runSimulationNumpy(
cls,
parameters=None,
modelSpecification=None,
startTime=0,
endTime=5,
numPoint=30,
selectedColumns=None,
_logger=Logger(),
_loggerPrefix="",
):
"""
Runs a simulation. Defaults to parameter values in the simulation.
Returns a NamedArray
Parameters
----------
modelSpecification: ExtendedRoadRunner/str
Roadrunner model
parameters: lmfit.Parameters
lmfit parameters
startTime: float
start time for the simulation
endTime: float
end time for the simulation
numPoint: int
number of points in the simulation
selectedColumns: list-str
output columns in simulation
_logger: Logger
_loggerPrefix: str
Return
------
NamedArray (or None if fail to converge)
"""
roadrunnerModel = modelSpecification
if isinstance(modelSpecification, str):
roadrunnerModel = cls.initializeRoadrunnerModel(roadrunnerModel)
else:
roadrunnerModel.reset()
if parameters is not None:
# Parameters have been specified
cls.setupModel(roadrunnerModel, parameters, logger=_logger)
# Do the simulation
if selectedColumns is not None:
newSelectedColumns = list(selectedColumns)
if TIME not in newSelectedColumns:
newSelectedColumns.insert(0, TIME)
try:
dataArr = roadrunnerModel.simulate(startTime, endTime,
numPoint,
newSelectedColumns)
except Exception as err:
_logger.error("Roadrunner exception: ", err)
dataArr = None
else:
try:
dataArr = roadrunnerModel.simulate(startTime, endTime,
numPoint)
except Exception as err:
_logger.exception("Roadrunner exception: %s" % err)
dataArr = None
return dataArr
