def get_mmd_postprop(exp_desc, seed):
"""
Calculates the MMD for a given Post Prop experiment.
"""
assert isinstance(exp_desc.inf, ed.PostProp_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
all_prop_mmds, post_err = util.io.load(res_file)
else:
exp_dir = os.path.join(root, 'experiments/seed_'+str(seed), exp_desc.get_dir(), '0')
print(exp_dir)
if not os.path.exists(exp_dir):
raise misc.NonExistentExperiment(exp_desc)
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
all_proposals, posterior, _, _ = util.io.load(os.path.join(exp_dir, 'results'))
all_prop_mmds = []
for i, proposal in enumerate(all_proposals[1:]):
samples = proposal.gen(n_mcmc_samples, rng=rng)
prop_err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
all_prop_mmds.append(prop_err)
samples = posterior.gen(n_mcmc_samples, rng=rng)
post_err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
util.io.save((all_prop_mmds, post_err), res_file)
return all_prop_mmds, post_err
def get_mmd_snl(exp_desc, seed):
"""
Calculates the MMD for a given SNL experiment.
"""
assert isinstance(exp_desc.inf, ed.SNL_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
all_mmds = util.io.load(res_file)
else:
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
all_samples = get_samples_snl(exp_desc, seed)
all_mmds = []
for samples in all_samples:
err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
all_mmds.append(err)
util.io.save(all_mmds, res_file)
return all_mmds
def get_mmd_snpe(exp_desc, seed):
"""
Calculates the MMD for a given SNPE experiment.
"""
assert isinstance(exp_desc.inf, ed.SNPE_MDN_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
all_mmds = util.io.load(res_file)
else:
exp_dir = os.path.join(root, 'experiments/seed_'+str(seed), exp_desc.get_dir(), '0')
if not os.path.exists(exp_dir):
raise misc.NonExistentExperiment(exp_desc)
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
all_posteriors, _, _, _ = util.io.load(os.path.join(exp_dir, 'results'))
all_mmds = []
for posterior in all_posteriors[1:]:
samples = posterior.gen(n_mcmc_samples, rng=rng)
err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
all_mmds.append(err)
util.io.save(all_mmds, res_file)
return all_mmds
def get_mmd_sl(exp_desc, seed):
"""
Calculates the MMD for a given synth likelihood experiment.
"""
assert isinstance(exp_desc.inf, ed.SynthLik_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
err, n_sims = util.io.load(res_file)
else:
exp_dir = os.path.join(root, 'experiments/seed_'+str(seed), exp_desc.get_dir(), '0')
if not os.path.exists(exp_dir):
raise misc.NonExistentExperiment(exp_desc)
samples, n_sims = util.io.load(os.path.join(exp_dir, 'results'))
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
util.io.save((err, n_sims), res_file)
return err, n_sims
def get_mmd_smc(exp_desc, seed):
"""
Calculates the MMD for a given SMC ABC experiment.
"""
assert isinstance(exp_desc.inf, ed.SMC_ABC_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
all_mmds, all_n_sims = util.io.load(res_file)
else:
exp_dir = os.path.join(root, 'experiments/seed_'+str(seed), exp_desc.get_dir(), '0')
if not os.path.exists(exp_dir):
raise misc.NonExistentExperiment(exp_desc)
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
all_samples, all_log_weights, _, _, all_n_sims = util.io.load(os.path.join(exp_dir, 'results'))
all_mmds = []
for samples, log_weights in zip(all_samples, all_log_weights):
weights = np.exp(log_weights)
err = two_sample.sq_maximum_mean_discrepancy(xs=samples, ys=true_samples, wxs=weights, scale=scale)
all_mmds.append(err)
util.io.save((all_mmds, all_n_sims), res_file)
return all_mmds, all_n_sims
def calc_mmd(model):
"""
Calculates MMD between true samples and a given likelihood model.
"""
_, true_samples, scale = get_truth()
samples = model.gen(true_samples.shape[0], rng=np.random.RandomState(42))
return two_sample.sq_maximum_mean_discrepancy(samples,
true_samples,
scale=scale)
def calc_mmd_cond(net):
"""
Calculates MMD between true samples and a given conditional likelihood model.
"""
true_ps, true_samples, scale = get_truth()
samples = net.gen(true_ps,
true_samples.shape[0],
rng=np.random.RandomState(42))
return two_sample.sq_maximum_mean_discrepancy(samples,
true_samples,
scale=scale)
def get_mmd_nde(exp_desc, seed):
"""
Calculates the MMD for a given NDE experiment.
"""
assert isinstance(exp_desc.inf, ed.NDE_Descriptor)
res_file = os.path.join(root, 'results/seed_'+str(seed), exp_desc.get_dir(), 'mmd')
if os.path.exists(res_file + '.pkl'):
err = util.io.load(res_file)
else:
samples = get_samples_nde(exp_desc, seed)
true_samples = get_true_samples(seed)
scale = util.math.median_distance(true_samples)
err = two_sample.sq_maximum_mean_discrepancy(samples, true_samples, scale=scale)
util.io.save(err, res_file)
return err
