def test_profiler(self):
# defining some dummy parameters and observables
Parameter('tmp a')
Parameter('tmp b')
Parameter('tmp c')
Parameter('tmp d')
p = ParameterConstraints()
p.set_constraint('tmp b', '2+-0.3')
p.set_constraint('tmp c', '0.2+-0.1')
p.set_constraint('tmp d', '1+-0.5')
def prediction(wc_obj, par):
return par['tmp a']**2 + par['tmp b'] + par['tmp c'] + par[
'tmp d']**2
flavio.Observable('tmp obs')
Prediction('tmp obs', prediction)
m = Measurement('tmp measurement')
m.add_constraint(['tmp obs'],
flavio.statistics.probability.NormalDistribution(
1, 0.2))
# test 1D profiler
fit_1d = FrequentistFit('test profiler 1d', p, ['tmp a'],
['tmp b', 'tmp c', 'tmp d'], ['tmp obs'])
profiler_1d = profiler.Profiler1D(fit_1d, -10, 10)
x, z, n = profiler_1d.run(steps=4)
self.assertEqual(x.shape, (4, ))
self.assertEqual(z.shape, (4, ))
self.assertEqual(n.shape, (3, 4))
npt.assert_array_equal(x, profiler_1d.x)
npt.assert_array_equal(z, profiler_1d.log_profile_likelihood)
npt.assert_array_equal(n, profiler_1d.profile_nuisance)
pdat = profiler_1d.pvalue_prob_plotdata()
npt.assert_array_equal(pdat['x'], x)
# test 2D profiler
p.remove_constraint('d')
fit_2d = FrequentistFit('test profiler 2d', p, ['tmp a', 'tmp d'],
['tmp b', 'tmp c'], ['tmp obs'])
profiler_2d = profiler.Profiler2D(fit_2d, -10, 10, -10, 10)
x, y, z, n = profiler_2d.run(steps=(3, 4))
self.assertEqual(x.shape, (3, ))
self.assertEqual(y.shape, (4, ))
self.assertEqual(z.shape, (3, 4))
self.assertEqual(n.shape, (2, 3, 4))
npt.assert_array_equal(x, profiler_2d.x)
npt.assert_array_equal(y, profiler_2d.y)
npt.assert_array_equal(z, profiler_2d.log_profile_likelihood)
npt.assert_array_equal(n, profiler_2d.profile_nuisance)
pdat = profiler_2d.contour_plotdata()
npt.assert_array_almost_equal(pdat['z'], -2 * (z - np.max(z)))
# delete dummy instances
for p in ['tmp a', 'tmp b', 'tmp c', 'tmp d']:
Parameter.del_instance(p)
FrequentistFit.del_instance('test profiler 1d')
Observable.del_instance('tmp obs')
Measurement.del_instance('tmp measurement')
def test_profiler(self):
# defining some dummy parameters and observables
Parameter('tmp a');
Parameter('tmp b');
Parameter('tmp c');
Parameter('tmp d');
p = ParameterConstraints()
p.set_constraint('tmp b', '2+-0.3')
p.set_constraint('tmp c', '0.2+-0.1')
p.set_constraint('tmp d', '1+-0.5')
def prediction(wc_obj, par):
return par['tmp a']**2+par['tmp b']+par['tmp c']+par['tmp d']**2
flavio.Observable('tmp obs');
Prediction('tmp obs', prediction);
m=Measurement('tmp measurement')
m.add_constraint(['tmp obs'],
flavio.statistics.probability.NormalDistribution(1, 0.2))
# test 1D profiler
fit_1d = FrequentistFit('test profiler 1d',
p, ['tmp a'], ['tmp b', 'tmp c', 'tmp d'], ['tmp obs'])
profiler_1d = profiler.Profiler1D(fit_1d, -10, 10)
x, z, n = profiler_1d.run(steps=4)
self.assertEqual(x.shape, (4,))
self.assertEqual(z.shape, (4,))
self.assertEqual(n.shape, (3, 4))
npt.assert_array_equal(x, profiler_1d.x)
npt.assert_array_equal(z, profiler_1d.log_profile_likelihood)
npt.assert_array_equal(n, profiler_1d.profile_nuisance)
pdat = profiler_1d.pvalue_prob_plotdata()
npt.assert_array_equal(pdat['x'], x)
# test multiprocessing
for threads in [2, 3, 4]:
xt, zt, nt = profiler_1d.run(steps=4, threads=threads)
npt.assert_array_almost_equal(x, xt, decimal=4)
npt.assert_array_almost_equal(z, zt, decimal=4)
npt.assert_array_almost_equal(n, nt, decimal=4)
with self.assertRaises(ValueError):
profiler_1d.run(steps=4, threads=5)
# test 2D profiler
p.remove_constraint('d')
fit_2d = FrequentistFit('test profiler 2d',
p, ['tmp a', 'tmp d'], ['tmp b', 'tmp c'], ['tmp obs'])
profiler_2d = profiler.Profiler2D(fit_2d, -10, 10, -10, 10)
x, y, z, n = profiler_2d.run(steps=(3,4))
self.assertEqual(x.shape, (3,))
self.assertEqual(y.shape, (4,))
self.assertEqual(z.shape, (3, 4))
self.assertEqual(n.shape, (2, 3, 4))
npt.assert_array_equal(x, profiler_2d.x)
npt.assert_array_equal(y, profiler_2d.y)
npt.assert_array_equal(z, profiler_2d.log_profile_likelihood)
npt.assert_array_equal(n, profiler_2d.profile_nuisance)
pdat = profiler_2d.contour_plotdata()
npt.assert_array_almost_equal(pdat['z'], -2*(z-np.max(z)))
# test multiprocessing
for threads in [2, 5, 12]:
xt, yt, zt, nt = profiler_2d.run(steps=(3,4))
npt.assert_array_almost_equal(x, xt, decimal=4)
npt.assert_array_almost_equal(y, yt, decimal=4)
npt.assert_array_almost_equal(z, zt, decimal=4)
npt.assert_array_almost_equal(n, nt, decimal=4)
with self.assertRaises(ValueError):
profiler_2d.run(steps=(3,4), threads=13)
# delete dummy instances
for p in ['tmp a', 'tmp b', 'tmp c', 'tmp d']:
Parameter.del_instance(p)
FrequentistFit.del_instance('test profiler 1d')
Observable.del_instance('tmp obs')
Measurement.del_instance('tmp measurement')
