def objectivefunction(self, simulation, evaluation):
if self.used_algorithm == 'sceua' or self.used_algorithm == 'abc' or self.used_algorithm == 'fscabc':
objectivefunction = rmse(evaluation=evaluation, simulation=simulation)
elif self.used_algorithm == 'dream' or self.used_algorithm == 'demcz' or self.used_algorithm == 'mcmc':
objectivefunction = log_p(evaluation=evaluation, simulation=simulation)
else:
objectivefunction = - rmse(evaluation=evaluation, simulation=simulation)
return objectivefunction
def objectivefunction(self, simulation, evaluation, params):
if self.objfunc is None:
return -1 * rmse(evaluation, simulation)
else:
pars, names = params
return self.objfunc(pars)
def objectivefunction(
self,
simulation: Union[list, np.array],
evaluation: Union[list, np.array],
params=None,
) -> float:
"""
A user defined objective function to calculate fitness.
Parameters
----------
simulation:
simulation results
evaluation:
evaluation results
params:
parameters leading to the simulation
Returns
-------
float
likelihood
"""
# SPOTPY expects to get one or multiple values back,
# that define the performance of the model run
if not self.obj_func:
# This is used if not overwritten by user
like = rmse(evaluation, simulation)
else:
# Way to ensure flexible spot setup class
like = self.obj_func(evaluation, simulation)
return like
def objectivefunction(self,simulation,evaluation, params=None):
#SPOTPY expects to get one or multiple values back,
#that define the performence of the model run
if not self.obj_func:
# This is used if not overwritten by user
like = rmse(evaluation,simulation)
else:
#Way to ensure flexible spot setup class
like = self.obj_func(evaluation,simulation)
return like
def objectivefunction(self, simulation, evaluation, params=None):
# SPOTPY expects to get one or multiple values back,
# that define the performance of the model run
if not self.obj_func:
eval = np.delete(evaluation.SC_median.values,
np.argwhere(np.isnan(simulation)))
sim = simulation[~np.isnan(simulation)]
like = -rmse(
eval, sim
) #set minus before rmse if trying to maximize, depends on algorithm
print("RMSE is: ", like)
else:
# Way to ensure flexible spot setup class
like = self.obj_func(evaluation.SC_median.values,
simulation.Med_TSL.values)
return like
def objectivefunction(self, simulation, evaluation):
evaluation_input = np.array([])
# Get rid of the impact of NULL values in the evaluation data
for index in range(0, len(simulation)):
if not evaluation[index]:
evaluation_input = np.append(evaluation_input,
simulation[index])
else:
evaluation_input = np.append(evaluation_input,
evaluation[index])
# Generates a minimum objective value of the output
objective_function = -rmse(evaluation=evaluation_input,
simulation=simulation)
print("Objective Value: {}".format(objective_function))
return objective_function
def objectivefunction(self, simulation, evaluation, params=None):
objectivefunction = -rmse(evaluation=evaluation, simulation=simulation)
return objectivefunction
def objectivefunction(self, simulation, evaluation):
objectivefunction = rmse(evaluation=evaluation, simulation=simulation)
return objectivefunction
return {'obs': pend_obs, 'sim': pend_sim, 'correcta': (pend_obs > 0) is (pend_sim > 0)}
algs_spotpy = {
'fast': spotpy.algorithms.fast,
'dream': spotpy.algorithms.dream,
'cm': spotpy.algorithms.mc,
'cmmc': spotpy.algorithms.mcmc,
'epm': spotpy.algorithms.mle,
'mhl': spotpy.algorithms.lhs,
'as': spotpy.algorithms.sa,
'sceua': spotpy.algorithms.sceua,
'erop': spotpy.algorithms.rope,
'caa': spotpy.algorithms.abc,
'fscabc': spotpy.algorithms.fscabc,
'bdd': spotpy.algorithms.dds
}
eval_funcs = {
'ens': lambda o, s, f: spt_f.nashsutcliffe(o, s),
'rcep': lambda o, s, f: -spt_f.rmse(o, s),
'corresp': lambda o, s, f: spt_f.agreementindex(o, s),
'ekg': lambda o, s, f: spt_f.kge(o, s),
'r2': lambda o, s, f: spt_f.rsquared(o, s),
'rcnep': lambda o, s, f: -spt_f.rrmse(o, s),
'log p': lambda o, s, f: spt_f.log_p(o, s),
'verosimil_gaus': lambda o, s, f: spt_l.gaussianLikelihoodMeasErrorOut(o, s),
'tendencia': _anlz_tendencia
}
def test_rmse_with_self_is_zero(self):
res = of.rmse(self.evaluation, self.evaluation)
self.assertAlmostEqual(res, 0.0, self.tolerance)
def test_rmse(self):
res = of.rmse(self.evaluation, self.simulation)
self.assertAlmostEqual(res, 1.6207985019059006, self.tolerance)