def test_rsquared_perfect_corr_is_1(self):
"""rsquared for perfect correlation should be 1"""
res = of.rsquared(self.evaluation, self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
res = of.rsquared(self.evaluation, 2*self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
res = of.rsquared(self.evaluation, 0.5*self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
res = of.rsquared(self.evaluation, -self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
res = of.rsquared(self.evaluation, -2*self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
res = of.rsquared(self.evaluation, -0.5*self.evaluation)
self.assertAlmostEqual(res, 1, self.tolerance)
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_rsquared_random(self):
res = of.rsquared(self.evaluation, self.simulation)
self.assertAlmostEqual(res, 0.012212676098496588, self.tolerance)