def test_transform(self): # Test optimisation with parameter transformation. # Test with LogPDF r = pints.toy.TwistedGaussianLogPDF(2, 0.01) x0 = np.array([0, 1.01]) b = pints.RectangularBoundaries([-0.01, 0.95], [0.01, 1.05]) s = 0.01 t = pints.RectangularBoundariesTransformation(b) opt = pints.OptimisationController(r, x0, s, b, t, method) opt.set_log_to_screen(False) opt.set_max_unchanged_iterations(None) opt.set_max_iterations(10) opt.run() # Test with ErrorMeasure r = pints.toy.ParabolicError() x0 = [0.1, 0.1] b = pints.RectangularBoundaries([-1, -1], [1, 1]) s = 0.1 t = pints.RectangularBoundariesTransformation(b) pints.OptimisationController(r, x0, boundaries=b, transform=t, method=method) opt = pints.OptimisationController(r, x0, s, b, t, method) opt.set_log_to_screen(False) opt.set_max_unchanged_iterations(None) opt.set_max_iterations(10) x, _ = opt.run() # Test output are detransformed self.assertEqual(x.shape, (2, )) self.assertTrue(b.check(x))
def setUpClass(cls): # Create Transformation class lower1 = np.array([1]) upper1 = np.array([10]) lower2 = np.array([1, 2]) upper2 = np.array([10, 20]) # Test normal construction with lower and upper cls.t1 = pints.RectangularBoundariesTransformation(lower1, upper1) cls.t2 = pints.RectangularBoundariesTransformation(lower2, upper2) # Test construction with rectangular boundaries object b2 = pints.RectangularBoundaries(lower2, upper2) cls.t2b = pints.RectangularBoundariesTransformation(b2) cls.p = [1.5, 15.] cls.x = [-2.8332133440562162, 0.9555114450274365] cls.j = np.diag([0.4722222222222225, 3.6111111111111098]) cls.j_s1_diag = [0.4197530864197533, -1.6049382716049378] cls.j_s1 = np.zeros((2, 2, 2)) for i in range(2): cls.j_s1[i, i, i] = cls.j_s1_diag[i] cls.log_j_det = 0.5337099175995788 cls.log_j_det_s1 = [0.8888888888888888, -0.4444444444444445]
def setUpClass(cls): # Create Transformation class cls.t1 = TestNonElementWiseIdentityTransformation(1) lower2 = np.array([1, 2]) upper2 = np.array([10, 20]) cls.t2 = pints.RectangularBoundariesTransformation(lower2, upper2) cls.t3 = pints.LogTransformation(1) cls.t = pints.ComposedTransformation(cls.t1, cls.t2, cls.t3) cls.p = [0.1, 1.5, 15., 999.] cls.x = [0.1, -2.8332133440562162, 0.9555114450274365, 6.9067547786485539] cls.j = np.diag([1., 0.4722222222222225, 3.6111111111111098, 999.]) cls.j_s1_diag = [0., 0.4197530864197533, -1.6049382716049378, 999.] cls.j_s1 = np.zeros((4, 4, 4)) for i in range(4): cls.j_s1[i, i, i] = cls.j_s1_diag[i] cls.log_j_det = 7.4404646962481324 cls.log_j_det_s1 = [0., 0.8888888888888888, -0.4444444444444445, 1.]
def inference(model, values, times): # Create an object with links to the model and time series problem = pints.SingleOutputProblem(model, times, values) # Create a log-likelihood function (adds an extra parameter!) log_likelihood = pints.GaussianLogLikelihood(problem) # Create a uniform prior over both the parameters and the new noise variable lower_bounds = np.array([1e-3, 0.0, 0.4, 0.1, 1e-6, 8.0, 1e-4]) upper_bounds = np.array([10.0, 0.4, 0.6, 100.0, 100e-6, 10.0, 0.2]) log_prior = pints.UniformLogPrior(lower_bounds, upper_bounds) # Create a posterior log-likelihood (log(likelihood * prior)) log_posterior = pints.LogPosterior(log_likelihood, log_prior) # Choose starting points for 3 mcmc chains # params = ['k0', 'E0', 'a', 'Ru', 'Cdl', 'freq', 'sigma'] start_parameters = np.array( [0.0101, 0.214, 0.53, 8.0, 20.0e-6, 9.0152, 0.01]) transform = pints.ComposedTransformation( pints.LogTransformation(1), pints.RectangularBoundariesTransformation(lower_bounds[1:], upper_bounds[1:]), ) sigma0 = [0.1 * (h - l) for l, h in zip(lower_bounds, upper_bounds)] boundaries = pints.RectangularBoundaries(lower_bounds, upper_bounds) found_parameters, found_value = pints.optimise(log_posterior, start_parameters, sigma0, boundaries, transform=transform, method=pints.CMAES) xs = [ found_parameters * 1.001, found_parameters * 1.002, found_parameters * 1.003, ] for x in xs: x[5] = found_parameters[5] print('start_parameters', start_parameters) print('found_parameters', found_parameters) print('lower_bounds', lower_bounds) print('upper_bounds', upper_bounds) # Create mcmc routine with four chains mcmc = pints.MCMCController(log_posterior, 3, xs, method=pints.HaarioBardenetACMC, transform=transform) # Add stopping criterion mcmc.set_max_iterations(10000) # Run! chains = mcmc.run() # Save chains for plotting and analysis pickle.dump((xs, pints.GaussianLogLikelihood, log_prior, chains, 'HaarioBardenetACMC'), open('results.pickle', 'wb'))
def inference2(model_raw, model_old, model, values, times): # Create an object with links to the model and time series problem = pints.SingleOutputProblem(model_old, times, values) # Create a log-likelihood function (adds an extra parameter!) log_likelihood = pints.GaussianLogLikelihood(problem) # Create a uniform prior over both the parameters and the new noise variable e0_buffer = 0.1 * (model_raw.params['Ereverse'] - model_raw.params['Estart']) lower_bounds = np.array([ 0.0, model_raw.params['Estart'] + e0_buffer, 0.0, 0.0, 0.4, 0.9* model_raw.params['omega'], 1e-4, ]) upper_bounds = np.array([ 100 * model_raw.params['k0'], model_raw.params['Ereverse'] - e0_buffer, 10 * model_raw.params['Cdl'], 10 * model_raw.params['Ru'], 0.6, 1.1* model_raw.params['omega'], 0.2, ]) log_prior = pints.UniformLogPrior(lower_bounds, upper_bounds) # Create a posterior log-likelihood (log(likelihood * prior)) log_posterior = pints.LogPosterior(log_likelihood, log_prior) # Choose starting points for 3 mcmc chains param_names = ['k0', 'E0', 'Cdl', 'Ru', 'alpha', 'omega', 'sigma'] start_parameters = np.array([ model_raw.params['k0'], model_raw.params['E0'], model_raw.params['Cdl'], model_raw.params['Ru'], model_raw.params['alpha'], model_raw.params['omega'], 0.01 ]) sigma0 = [0.5 * (h - l) for l, h in zip(lower_bounds, upper_bounds)] boundaries = pints.RectangularBoundaries(lower_bounds, upper_bounds) #found_parameters, found_value = pints.optimise( # log_posterior, # start_parameters, # sigma0, # boundaries, # method=pints.CMAES # ) found_parameters = start_parameters print('start_parameters', start_parameters) print('found_parameters', found_parameters) xs = [ found_parameters * 1.001, found_parameters * 0.999, found_parameters * 0.998, ] for x in xs: x[5] = found_parameters [5] # adjust Ru to something reasonable xs[0][3] = 1.001*5e-5 xs[1][3] = 1.00*5e-5 xs[2][3] = 0.999*5e-5 transform = pints.ComposedElementWiseTransformation( pints.LogTransformation(1), pints.RectangularBoundariesTransformation( lower_bounds[1:], upper_bounds[1:] ), ) # Create mcmc routine with four chains mcmc = pints.MCMCController(log_posterior, 3, xs, method=pints.HaarioBardenetACMC, transform=transform) # Add stopping criterion mcmc.set_max_iterations(10000) # Run! chains = mcmc.run() # Save chains for plotting and analysis pickle.dump((xs, pints.GaussianLogLikelihood, log_prior, chains, 'HaarioACMC'), open('results2.pickle', 'wb'))