def test_importance_sampling():
mu_sampler = 5.
sigma_sampler = 4.
mu_pdf = 4.
sigma_pdf = 1.
obs_sampler = zfit.Space(obs='obs1',
limits=(4.5, 5.5)) # smaller, so pdf is bigger
obs_pdf = zfit.Space(obs='obs1', limits=(1, 7))
gauss_sampler = GaussNoAnalyticSampling(mu=mu_sampler,
sigma=sigma_sampler,
obs=obs_sampler)
gauss_pdf = GaussNoAnalyticSampling(mu=mu_pdf,
sigma=sigma_pdf,
obs=obs_pdf)
importance_sampling_called = [False]
class GaussianSampleAndWeights(SessionHolderMixin):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# self.n_to_produce = tf.Variable(initial_value=-42, dtype=tf.int64, use_resource=True,
# trainable=False, validate_shape=False)
# self.sess.run(self.n_to_produce.initializer)
# self.dtype = dtype
# self.limits = limits
def __call__(self, n_to_produce, limits, dtype):
importance_sampling_called[0] = True
n_to_produce = tf.cast(n_to_produce, dtype=tf.int32)
# assign_op = self.n_to_produce.assign(n_to_produce)
# with tf.control_dependencies([assign_op]):
gaussian_sample = gauss_sampler._create_sampler_tensor(
n=n_to_produce, limits=limits, fixed_params=False,
name='asdf')[2]
weights = gauss_sampler.pdf(gaussian_sample)
weights_max = None
thresholds = tf.random.uniform(shape=(n_to_produce, ), dtype=dtype)
return gaussian_sample, thresholds, weights, weights_max, n_to_produce
sample = accept_reject_sample(prob=gauss_pdf.unnormalized_pdf,
n=30000,
limits=obs_pdf)
gauss_pdf._sample_and_weights = GaussianSampleAndWeights
sample2 = gauss_pdf.sample(n=30000, limits=obs_pdf)
assert importance_sampling_called[0]
sample_np, sample_np2 = zfit.run([sample, sample2])
mean = np.mean(sample_np)
mean2 = np.mean(sample_np2)
std = np.std(sample_np)
std2 = np.std(sample_np2)
assert mean == pytest.approx(mu_pdf, rel=0.02)
assert mean2 == pytest.approx(mu_pdf, rel=0.02)
assert std == pytest.approx(sigma_pdf, rel=0.02)
assert std2 == pytest.approx(sigma_pdf, rel=0.02)
def test_importance_sampling():
mu_sampler = 5.
sigma_sampler = 4.
mu_pdf = 4.
sigma_pdf = 1.
obs_sampler = zfit.Space(obs='obs1',
limits=(4.5, 5.5)) # smaller, so pdf is bigger
obs_pdf = zfit.Space(obs='obs1', limits=(1, 7))
gauss_sampler = GaussNoAnalyticSampling(mu=mu_sampler,
sigma=sigma_sampler,
obs=obs_sampler)
gauss_pdf = GaussNoAnalyticSampling(mu=mu_pdf,
sigma=sigma_pdf,
obs=obs_pdf)
importance_sampling_called = [False]
class GaussianSampleAndWeights:
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@z.function
def __call__(self, n_to_produce, limits, dtype):
importance_sampling_called[0] = True
n_to_produce = tf.cast(n_to_produce, dtype=tf.int32)
gaussian_sample = gauss_sampler.sample(n=n_to_produce,
limits=limits,
name='asdf')
weights = gauss_sampler.pdf(gaussian_sample)
weights_max = None
thresholds = tf.random.uniform(shape=(n_to_produce, ), dtype=dtype)
return gaussian_sample, thresholds, weights, weights_max, n_to_produce
sample = accept_reject_sample(prob=gauss_pdf.unnormalized_pdf,
n=30000,
limits=obs_pdf)
gauss_pdf._sample_and_weights = GaussianSampleAndWeights
sample2 = gauss_pdf.sample(n=30000, limits=obs_pdf)
assert importance_sampling_called[0]
sample_np, sample_np2 = [sample.numpy(), sample2.numpy()]
mean = np.mean(sample_np)
mean2 = np.mean(sample_np2)
std = np.std(sample_np)
std2 = np.std(sample_np2)
assert mean == pytest.approx(mu_pdf, rel=0.02)
assert mean2 == pytest.approx(mu_pdf, rel=0.02)
assert std == pytest.approx(sigma_pdf, rel=0.02)
assert std2 == pytest.approx(sigma_pdf, rel=0.02)
def test_importance_sampling():
import tensorflow as tf
mu_sampler = 5.
sigma_sampler = 4.
mu_pdf = 4.
sigma_pdf = 1.
obs_sampler = zfit.Space(obs='obs1',
limits=(4.5, 5.5)) # smaller, so pdf is bigger
obs_pdf = zfit.Space(obs='obs1', limits=(1, 9))
gauss_sampler = zfit.pdf.Gauss(mu=mu_sampler,
sigma=sigma_sampler,
obs=obs_sampler)
gauss_pdf = zfit.pdf.Gauss(mu=mu_pdf, sigma=sigma_pdf, obs=obs_pdf)
def gaussian_sample_and_weights(n_to_produce, limits, dtype):
gaussian_sample = gauss_sampler.sample(n=n_to_produce, limits=limits)
weights = gauss_sampler.pdf(gaussian_sample)
weights_max = tf.reduce_max(weights) * 0.7
thresholds = tf.random_uniform(shape=(n_to_produce, ))
return gaussian_sample, thresholds, weights, weights_max, n_to_produce
sample = accept_reject_sample(prob=gauss_pdf.unnormalized_pdf,
n=30000,
limits=obs_pdf)
gauss_pdf._sample_and_weights = gaussian_sample_and_weights
sample2 = gauss_pdf.sample(n=30000, limits=obs_pdf)
sample_np, sample_np2 = zfit.run([sample, sample2])
mean = np.mean(sample_np)
mean2 = np.mean(sample_np2)
std = np.std(sample_np)
std2 = np.std(sample_np2)
assert mean == pytest.approx(mu_pdf, rel=0.01)
assert mean2 == pytest.approx(mu_pdf, rel=0.01)
assert std == pytest.approx(sigma_pdf, rel=0.01)
assert std2 == pytest.approx(sigma_pdf, rel=0.01)
