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 testMakeMethods(self):
if IGNORE_TEST:
return
self._init()
METHOD = "dummy"
def test(methods):
result = ModelFitter.makeMethods(methods, None)
self.assertTrue(isinstance(result, list))
optimizerMethod = result[0]
self.assertEqual(optimizerMethod.method, METHOD)
self.assertTrue(isinstance(optimizerMethod.kwargs, dict))
#
test([METHOD])
test([METHOD, METHOD])
test([_helpers.OptimizerMethod(method=METHOD, kwargs={})])
#
methods = _helpers.OptimizerMethod(method=METHOD, kwargs={"a": 1})
result = ModelFitter.makeMethods([methods, methods], None)
def test(methods):
result = ModelFitter.makeMethods(methods, None)
self.assertTrue(isinstance(result, list))
optimizerMethod = result[0]
self.assertEqual(optimizerMethod.method, METHOD)
self.assertTrue(isinstance(optimizerMethod.kwargs, dict))
