observation dimensions - Githubissues

Hi, I was wondering if you have considered amending the script to be able to do inference with multiple observations. Or if I am missing how to do this. Thank you for your help!

As a simple example, I'm simulating taking a sample from normal population with some mean mu and some standard deviation sigma. The sample will have summary statistics mean xbar and standard deviation s. The sample size is small (n=10), so we would benefit from the observed data being able to include multiple samples. When I try to make the observed data multiple samples, I get this error:

AssertionError Traceback (most recent call last)

in () 10 n_mades=n_mades, 11 prior_norm=prior_norm, ---> 12 density=density) 13 # train 14 log, _, posterior = res.run( ~/delfi/delfi/inference/APT.py in __init__(self, generator, obs, prior_norm, pilot_samples, reg_lambda, seed, verbose, add_prior_precision, Ptol, **kwargs) 67 if self.obs.ndim == 1: 68 self.obs = self.obs.reshape(1, -1) ---> 69 assert self.obs.shape[0] == 1 70 71 if np.any(np.isnan(self.obs)): AssertionError: --------------------------------------------------------------------------- Here is the code I used to generate this: ``` import numpy as np def SampleSimulator(parameters, N=1e6, seed=None): """ Sample simulator Simulates taking a sample of size 10 from population of size N with mean mu and standard dev sigma Returns sample as 1d np.array of length 10 Parameters ------------------- mu : float pop mean sigma : float pop standard deviation N : int population size seed : int """ if seed is not None: np.random.seed(seed=seed) else: np.random.seed() # generate population mu, sigma = parameters N = np.uint64(N) population = np.random.normal(mu, sigma, N) # take a random sample of size 10 from the population without replacement sam = np.random.choice(population, 10, replace=False) return sam from delfi.simulator.BaseSimulator import BaseSimulator class Sampler(BaseSimulator): def __init__(self, N, seed=None): """ Sample simulator Simulates taking a sample of size 10 from population of size N with mean mu and standard dev sigma Returns sample as 1d np.array of length 10 Parameters ------------------- N : int population size seed : int or None If set, randomness across runs is disabled """ dim_param = 2 super().__init__(dim_param=dim_param, seed=seed) self.N = N self.SampleSimulator = SampleSimulator def gen_single(self, param_set): """Forward model for simulator for single parameter set Parameters ---------- params : list or np.array, 1d of length dim_param Parameter vector Returns ------- dict : dictionary with data The dictionary must contain a key data that contains the results of the forward run. Additional entries can be present. """ params = np.asarray(param_set) assert params.ndim == 1, 'params.ndim must be 1' sim_seed = self.gen_newseed() states = self.SampleSimulator(param_set, N, seed=sim_seed) return {'data': states, 'N': self.N} ## priors ## import delfi.distribution as dd seed_p = 2 prior_min = np.array([0,0]) prior_max = np.array([40,8]) prior = dd.Uniform(lower=prior_min, upper=prior_max,seed=seed_p) ## summary stats ## from delfi.summarystats.BaseSummaryStats import BaseSummaryStats from scipy import stats as spstats class SampleStats(BaseSummaryStats): """SummaryStats class for the sample from a normal distribution Calculates summary statistics """ def __init__(self, n_summary=2, seed=None): """See SummaryStats.py for docstring""" super(SampleStats, self).__init__(seed=seed) self.n_summary = n_summary def calc(self, repetition_list): """Calculate summary statistics Parameters ---------- repetition_list : list of dictionaries, one per repetition data list, returned by `gen` method of Simulator instance Returns ------- np.array, 2d with n_reps x n_summary """ stats = [] for r in range(len(repetition_list)): samp = np.transpose(repetition_list[r]['data']) xbar = np.mean(samp) s = np.std(samp) ss = np.array([xbar, s]) stats.append(ss) ##TEST THIS ALL OUT return np.asarray(stats) ## generator ## import delfi.generator as dg N = 1e6 # summary statistics hyperparameters n_summary = 2 seed_m = 3 m = Sampler(N, seed=seed_m) s = SampleStats(n_summary = n_summary) g = dg.Default(model=m, prior=prior, summary=s) ## true parameters and respective labels ## true_params = np.array([28, 2.9]) labels_params = ['mu', 'sigma'] # observed data: simulation given true parameters #### this is the multiple observations ##### obs = [] for i in range(0,25): obs.append(m.gen_single(true_params)) ## summary stats for >1 observation ## obs_stats = s.calc(obs) ## Hyperparameters ## seed_inf = 1 pilot_samples = 2000 # training schedule n_train = 2000 n_rounds = 1 # fitting setup minibatch = 256 epochs = 100 val_frac = 0.05 # network setup n_hiddens = [50,50] # convenience prior_norm = True # MAF parameters density = 'maf' n_mades = 5 ## Inference ## import delfi.inference as infer # inference object res = infer.APT(g, obs=obs_stats, n_hiddens=n_hiddens, seed=seed_inf, pilot_samples=pilot_samples, n_mades=n_mades, prior_norm=prior_norm, density=density) # train log, _, posterior = res.run( n_train=n_train, n_rounds=n_rounds, minibatch=minibatch, epochs=epochs, silent_fail=False, proposal='prior', val_frac=val_frac, verbose=True,) ```

mackelab / delfi

observation dimensions #67