def setUp(self): class Mockobject(Normal): def __init__(self, parameters): super(Mockobject, self).__init__(parameters) def pdf(self, input_values, x): return x self.N1 = Uniform([[1.3], [1.55]], name='n1') self.N2 = Uniform([[self.N1], [1.60]], name='n2') self.N3 = Uniform([[2], [4]], name='n3') self.graph1 = Uniform([[self.N1], [self.N2]], name='graph1') self.graph2 = Uniform([[.5], [self.N3]]) self.graph = [self.graph1, self.graph2] statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() self.sampler1 = RejectionABC([self.graph1], [distance_calculator], backend) self.sampler2 = RejectionABC([self.graph2], [distance_calculator], backend) self.sampler3 = RejectionABC( self.graph, [distance_calculator, distance_calculator], backend) self.pdf1 = self.sampler1.pdf_of_prior(self.sampler1.model, [1.32088846, 1.42945274]) self.pdf2 = self.sampler2.pdf_of_prior(self.sampler2.model, [3]) self.pdf3 = self.sampler3.pdf_of_prior(self.sampler3.model, [1.32088846, 1.42945274, 3])
def infer_parameters(): # define observation for true parameters mean=170, std=15 y_obs = [ 160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812, 140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934, 176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886, 166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379, 170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047, 178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686, 146.67058471763457, 179.41946565658628, 238.02751620619537, 206.22458790620766, 220.89530574344568, 221.04082532837026, 142.25301427453394, 261.37656571434275, 171.63761180867033, 210.28121820385866, 237.29130237612236, 175.75558340169619, 224.54340549862235, 197.42448680731226, 165.88273684581381, 166.55094082844519, 229.54308602661584, 222.99844054358519, 185.30223966014586, 152.69149367593846, 206.94372818527413, 256.35498655339154, 165.43140916577741, 250.19273595481803, 148.87781549665536, 223.05547559193792, 230.03418198709608, 146.13611923127021, 138.24716809523139, 179.26755740864527, 141.21704876815426, 170.89587081800852, 222.96391329259626, 188.27229523693822, 202.67075179617672, 211.75963110985992, 217.45423324370509 ] # define prior from abcpy.distributions import Uniform prior = Uniform([150, 5], [200, 25]) # define the model model = Gaussian(prior) # define statistics from abcpy.statistics import Identity statistics_calculator = Identity(degree=2, cross=False) # define distance from abcpy.distances import LogReg distance_calculator = LogReg(statistics_calculator) # define kernel from abcpy.distributions import MultiStudentT mean, cov, df = np.array([.0, .0]), np.eye(2), 3. kernel = MultiStudentT(mean, cov, df) # define backend from abcpy.backends import BackendDummy as Backend backend = Backend() # define sampling scheme from abcpy.inferences import PMCABC sampler = PMCABC(model, distance_calculator, kernel, backend) # sample from scheme T, n_sample, n_samples_per_param = 3, 250, 10 eps_arr = np.array([.75]) epsilon_percentile = 10 journal = sampler.sample(y_obs, T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile) return journal
def test(self): B1 = Binomial([10, 0.2]) N1 = Normal([0.03, 0.01]) N2 = Normal([0.1, N1]) graph1 = Normal([B1, N2]) graph2 = Normal([1, N2]) statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() sampler = RejectionABC([graph1, graph2], [distance_calculator, distance_calculator], backend) rng = np.random.RandomState(1) sampler.sample_from_prior(rng=rng) y_sim = sampler.simulate(1, rng=rng) self.assertTrue(isinstance(y_sim, list)) self.assertTrue(len(y_sim) == 2) self.assertTrue(isinstance(y_sim[0][0], np.ndarray))
def setUp(self): self.B1 = Binomial([10, 0.2]) self.N1 = Normal([0.03, 0.01]) self.N2 = Normal([0.1, self.N1]) self.graph = Normal([self.B1, self.N2]) statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() self.sampler = PMCABC([self.graph], [distance_calculator], backend) self.rng = np.random.RandomState(1) self.sampler.sample_from_prior(rng=self.rng) kernel = DefaultKernel([self.N1, self.N2, self.B1]) self.sampler.kernel = kernel self.sampler.accepted_parameters_manager.update_broadcast( self.sampler.backend, [[3, 0.11, 0.029], [4, 0.098, 0.031]], accepted_cov_mats=[[[1, 0], [0, 1]]], accepted_weights=np.array([1, 1])) kernel_parameters = [] for kernel in self.sampler.kernel.kernels: kernel_parameters.append( self.sampler.accepted_parameters_manager. get_accepted_parameters_bds_values(kernel.models)) self.sampler.accepted_parameters_manager.update_kernel_values( self.sampler.backend, kernel_parameters)
def infer_parameters(backend, steps=3, n_sample=250, n_samples_per_param=10, logging_level=logging.WARN): logging.basicConfig(level=logging_level) # define observation for true parameters mean=170, std=15 height_obs = [ 160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812, 140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934, 176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886, 166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379, 170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047, 178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686, 146.67058471763457, 179.41946565658628, 238.02751620619537, 206.22458790620766, 220.89530574344568, 221.04082532837026, 142.25301427453394, 261.37656571434275, 171.63761180867033, 210.28121820385866, 237.29130237612236, 175.75558340169619, 224.54340549862235, 197.42448680731226, 165.88273684581381, 166.55094082844519, 229.54308602661584, 222.99844054358519, 185.30223966014586, 152.69149367593846, 206.94372818527413, 256.35498655339154, 165.43140916577741, 250.19273595481803, 148.87781549665536, 223.05547559193792, 230.03418198709608, 146.13611923127021, 138.24716809523139, 179.26755740864527, 141.21704876815426, 170.89587081800852, 222.96391329259626, 188.27229523693822, 202.67075179617672, 211.75963110985992, 217.45423324370509 ] # define prior from abcpy.continuousmodels import Uniform mu = Uniform([[150], [200]], name='mu') sigma = Uniform([[5], [25]], name='sigma') # define the model from abcpy.continuousmodels import Normal height = Normal([mu, sigma], name='height') # define statistics from abcpy.statistics import Identity statistics_calculator = Identity(degree=2, cross=False) # define distance from abcpy.distances import LogReg distance_calculator = LogReg(statistics_calculator, seed=42) # define sampling scheme from abcpy.inferences import PMCABC sampler = PMCABC([height], [distance_calculator], backend, seed=1) # sample from scheme eps_arr = np.array([.75]) epsilon_percentile = 10 journal = sampler.sample([height_obs], steps, eps_arr, n_sample, n_samples_per_param, epsilon_percentile) return journal
def setUp(self): self.B1 = Binomial([10, 0.2]) self.N1 = Normal([0.03, 0.01]) self.N2 = Normal([0.1, self.N1]) self.graph = Normal([self.B1, self.N2]) statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() self.sampler = RejectionABC([self.graph], [distance_calculator], backend) self.rng = np.random.RandomState(1) self.sampler.sample_from_prior(rng=self.rng)
def test(self): N1 = Normal([1, 0.1]) N2 = Normal([N1, 0.1]) N2.visited = True N1.visited = True statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() sampler = RejectionABC([N2], [distance_calculator], backend) sampler._reset_flags() self.assertFalse(N1.visited) self.assertFalse(N2.visited)
def test(self): B1 = Binomial([10, 0.2]) N1 = Normal([0.03, 0.01]) N2 = Normal([0.1, N1]) graph = Normal([B1, N2]) statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() sampler = RejectionABC([graph], [distance_calculator], backend) rng = np.random.RandomState(1) sampler.sample_from_prior(rng=rng) self.assertIsNotNone(B1._fixed_values) self.assertIsNotNone(N1._fixed_values) self.assertIsNotNone(N2._fixed_values)
def test(self): B1 = Binomial([10, 0.2]) N1 = Normal([0.03, 0.01]) N2 = Normal([0.1, N1]) graph1 = Normal([B1, N2]) graph2 = Normal([1, N2]) statistics_calculator = Identity(degree=2, cross=False) distance_calculator = LogReg(statistics_calculator) backend = Backend() sampler = RejectionABC([graph1, graph2], [distance_calculator, distance_calculator], backend) rng = np.random.RandomState(1) sampler.sample_from_prior(rng=rng) mapping, index = sampler._get_mapping() self.assertTrue(mapping == [(B1, 0), (N2, 1), (N1, 2)])
def infer_parameters(): # define backend # Note, the dummy backend does not parallelize the code! from abcpy.backends import BackendDummy as Backend backend = Backend() # define observation for true parameters mean=170, std=15 height_obs = [ 160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812, 140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934, 176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886, 166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379, 170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047, 178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686, 146.67058471763457, 179.41946565658628, 238.02751620619537, 206.22458790620766, 220.89530574344568, 221.04082532837026, 142.25301427453394, 261.37656571434275, 171.63761180867033, 210.28121820385866, 237.29130237612236, 175.75558340169619, 224.54340549862235, 197.42448680731226, 165.88273684581381, 166.55094082844519, 229.54308602661584, 222.99844054358519, 185.30223966014586, 152.69149367593846, 206.94372818527413, 256.35498655339154, 165.43140916577741, 250.19273595481803, 148.87781549665536, 223.05547559193792, 230.03418198709608, 146.13611923127021, 138.24716809523139, 179.26755740864527, 141.21704876815426, 170.89587081800852, 222.96391329259626, 188.27229523693822, 202.67075179617672, 211.75963110985992, 217.45423324370509 ] # define prior from abcpy.continuousmodels import Uniform mu = Uniform([[150], [200]], ) sigma = Uniform([[5], [25]], ) # define the model from abcpy.continuousmodels import Normal height = Normal([mu, sigma], ) # define statistics from abcpy.statistics import Identity statistics_calculator = Identity(degree=3, cross=True) # Learn the optimal summary statistics using Semiautomatic summary selection from abcpy.summaryselections import Semiautomatic summary_selection = Semiautomatic([height], statistics_calculator, backend, n_samples=1000, n_samples_per_param=1, seed=1) # Redefine the statistics function statistics_calculator.statistics = lambda x, f2=summary_selection.transformation, \ f1=statistics_calculator.statistics: f2(f1(x)) # define distance from abcpy.distances import LogReg distance_calculator = LogReg(statistics_calculator) # define kernel from abcpy.perturbationkernel import DefaultKernel kernel = DefaultKernel([mu, sigma]) # define sampling scheme from abcpy.inferences import PMCABC sampler = PMCABC([height], [distance_calculator], backend, kernel, seed=1) # sample from scheme T, n_sample, n_samples_per_param = 3, 250, 10 eps_arr = np.array([.75]) epsilon_percentile = 10 journal = sampler.sample([height_obs], T, eps_arr, n_sample, n_samples_per_param, epsilon_percentile) return journal
def infer_parameters(steps=3, n_sample=250, n_samples_per_param=10, logging_level=logging.WARN): """Perform inference for this example. Parameters ---------- steps : integer, optional Number of iterations in the sequential PMCABC algoritm ("generations"). The default value is 3 n_samples : integer, optional Number of posterior samples to generate. The default value is 250. n_samples_per_param : integer, optional Number of data points in each simulated data set. The default value is 10. Returns ------- abcpy.output.Journal A journal containing simulation results, metadata and optionally intermediate results. """ logging.basicConfig(level=logging_level) # define observation for true parameters mean=170, std=15 y_obs = [ 160.82499176, 167.24266737, 185.71695756, 153.7045709, 163.40568812, 140.70658699, 169.59102084, 172.81041696, 187.38782738, 179.66358934, 176.63417241, 189.16082803, 181.98288443, 170.18565017, 183.78493886, 166.58387299, 161.9521899, 155.69213073, 156.17867343, 144.51580379, 170.29847515, 197.96767899, 153.36646527, 162.22710198, 158.70012047, 178.53470703, 170.77697743, 164.31392633, 165.88595994, 177.38083686, 146.67058471763457, 179.41946565658628, 238.02751620619537, 206.22458790620766, 220.89530574344568, 221.04082532837026, 142.25301427453394, 261.37656571434275, 171.63761180867033, 210.28121820385866, 237.29130237612236, 175.75558340169619, 224.54340549862235, 197.42448680731226, 165.88273684581381, 166.55094082844519, 229.54308602661584, 222.99844054358519, 185.30223966014586, 152.69149367593846, 206.94372818527413, 256.35498655339154, 165.43140916577741, 250.19273595481803, 148.87781549665536, 223.05547559193792, 230.03418198709608, 146.13611923127021, 138.24716809523139, 179.26755740864527, 141.21704876815426, 170.89587081800852, 222.96391329259626, 188.27229523693822, 202.67075179617672, 211.75963110985992, 217.45423324370509 ] # define prior from abcpy.continuousmodels import Uniform mu = Uniform([[150], [200]], name="mu") sigma = Uniform([[5], [25]], name="sigma") # define the model model = Gaussian([mu, sigma], name='height') # define statistics from abcpy.statistics import Identity statistics_calculator = Identity(degree=2, cross=False) # define distance from abcpy.distances import LogReg distance_calculator = LogReg(statistics_calculator, seed=42) # define backend from abcpy.backends import BackendDummy as Backend backend = Backend() # define sampling scheme from abcpy.inferences import PMCABC sampler = PMCABC([model], [distance_calculator], backend, seed=1) # sample from scheme eps_arr = np.array([.75]) epsilon_percentile = 10 journal = sampler.sample([y_obs], steps, eps_arr, n_sample, n_samples_per_param, epsilon_percentile) return journal
def setUp(self): self.stat_calc = Identity(degree=1, cross=0) self.distancefunc = LogReg(self.stat_calc)
rng = np.random.RandomState(rseed) n_samples = int(T / dt) + 1 noise = rng.normal(loc=0, scale=0.05, size=(n_samples)) obs_data = hh_simulator.forward_simulate([gbar_K_true, gbar_Na_true]) # define statistics # statistics_calculator = Identity() t = stimulus_dict["t"] stim_duration = stimulus_dict["duration"] t_stim_on = stimulus_dict["t_stim_on"] statistics_calculator = Features(t, stim_duration, t_stim_on) # define distance distance_calculator = Euclidean(statistics_calculator) distance_calculator2 = Wasserstein(statistics_calculator) distance_calculator3 = LogReg(statistics_calculator) # checking sim_data = hh_simulator.forward_simulate( [gbar_K_true + 3, gbar_Na_true + 4]) #obs_data = [obs_data[0] / np.max(obs_data[0]) + noise] #sim_data = [sim_data[0] / np.max(sim_data[0])] s1 = statistics_calculator.statistics(obs_data) s2 = statistics_calculator.statistics(sim_data) dist = distance_calculator.distance(obs_data, sim_data) dist2 = distance_calculator2.distance(obs_data, sim_data) dist3 = distance_calculator3.distance(obs_data, sim_data) print(s1) print(s2)
def setUp(self): self.stat_calc = Identity(degree=2, cross=False) self.distancefunc = LogReg(self.stat_calc, seed=1) self.rng = np.random.RandomState(1)