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test_ensemble.py
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test_ensemble.py
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"""
Unit tests of some functionality in ensemble.py when the parameters are named
"""
import string
from unittest import TestCase
import numpy as np
import pytest
from emcee.ensemble import EnsembleSampler, ndarray_to_list_of_dicts
class TestNP2ListOfDicts(TestCase):
def test_ndarray_to_list_of_dicts(self):
# Try different numbers of keys
for n_keys in [1, 2, 10, 26]:
keys = list(string.ascii_lowercase[:n_keys])
key_set = set(keys)
key_dict = {key: i for i, key in enumerate(keys)}
# Try different number of walker/procs
for N in [1, 2, 3, 10, 100]:
x = np.random.rand(N, n_keys)
LOD = ndarray_to_list_of_dicts(x, key_dict)
assert len(LOD) == N, "need 1 dict per row"
for i, dct in enumerate(LOD):
assert dct.keys() == key_set, "keys are missing"
for j, key in enumerate(keys):
assert dct[key] == x[i, j], f"wrong value at {(i, j)}"
class TestNamedParameters(TestCase):
"""
Test that a keyword-based log-probability function instead of
a positional.
"""
# Keyword based lnpdf
def lnpdf(self, pars) -> np.float64:
mean = pars["mean"]
var = pars["var"]
if var <= 0:
return -np.inf
return (
-0.5 * ((mean - self.x) ** 2 / var + np.log(2 * np.pi * var)).sum()
)
def lnpdf_mixture(self, pars) -> np.float64:
mean1 = pars["mean1"]
var1 = pars["var1"]
mean2 = pars["mean2"]
var2 = pars["var2"]
if var1 <= 0 or var2 <= 0:
return -np.inf
return (
-0.5
* (
(mean1 - self.x) ** 2 / var1
+ np.log(2 * np.pi * var1)
+ (mean2 - self.x - 3) ** 2 / var2
+ np.log(2 * np.pi * var2)
).sum()
)
def lnpdf_mixture_grouped(self, pars) -> np.float64:
mean1, mean2 = pars["means"]
var1, var2 = pars["vars"]
const = pars["constant"]
if var1 <= 0 or var2 <= 0:
return -np.inf
return (
-0.5
* (
(mean1 - self.x) ** 2 / var1
+ np.log(2 * np.pi * var1)
+ (mean2 - self.x - 3) ** 2 / var2
+ np.log(2 * np.pi * var2)
).sum()
+ const
)
def setUp(self):
# Draw some data from a unit Gaussian
self.x = np.random.randn(100)
self.names = ["mean", "var"]
def test_named_parameters(self):
sampler = EnsembleSampler(
nwalkers=10,
ndim=len(self.names),
log_prob_fn=self.lnpdf,
parameter_names=self.names,
)
assert sampler.params_are_named
assert list(sampler.parameter_names.keys()) == self.names
def test_asserts(self):
# ndim name mismatch
with pytest.raises(AssertionError):
_ = EnsembleSampler(
nwalkers=10,
ndim=len(self.names) - 1,
log_prob_fn=self.lnpdf,
parameter_names=self.names,
)
# duplicate names
with pytest.raises(AssertionError):
_ = EnsembleSampler(
nwalkers=10,
ndim=3,
log_prob_fn=self.lnpdf,
parameter_names=["a", "b", "a"],
)
# vectorize turned on
with pytest.raises(AssertionError):
_ = EnsembleSampler(
nwalkers=10,
ndim=len(self.names),
log_prob_fn=self.lnpdf,
parameter_names=self.names,
vectorize=True,
)
def test_compute_log_prob(self):
# Try different numbers of walkers
for N in [4, 8, 10]:
sampler = EnsembleSampler(
nwalkers=N,
ndim=len(self.names),
log_prob_fn=self.lnpdf,
parameter_names=self.names,
)
coords = np.random.rand(N, len(self.names))
lnps, _ = sampler.compute_log_prob(coords)
assert len(lnps) == N
assert lnps.dtype == np.float64
def test_compute_log_prob_mixture(self):
names = ["mean1", "var1", "mean2", "var2"]
# Try different numbers of walkers
for N in [8, 10, 20]:
sampler = EnsembleSampler(
nwalkers=N,
ndim=len(names),
log_prob_fn=self.lnpdf_mixture,
parameter_names=names,
)
coords = np.random.rand(N, len(names))
lnps, _ = sampler.compute_log_prob(coords)
assert len(lnps) == N
assert lnps.dtype == np.float64
def test_compute_log_prob_mixture_grouped(self):
names = {"means": [0, 1], "vars": [2, 3], "constant": 4}
# Try different numbers of walkers
for N in [8, 10, 20]:
sampler = EnsembleSampler(
nwalkers=N,
ndim=5,
log_prob_fn=self.lnpdf_mixture_grouped,
parameter_names=names,
)
coords = np.random.rand(N, 5)
lnps, _ = sampler.compute_log_prob(coords)
assert len(lnps) == N
assert lnps.dtype == np.float64
def test_run_mcmc(self):
# Sort of an integration test
n_walkers = 4
sampler = EnsembleSampler(
nwalkers=n_walkers,
ndim=len(self.names),
log_prob_fn=self.lnpdf,
parameter_names=self.names,
)
guess = np.random.rand(n_walkers, len(self.names))
n_steps = 50
results = sampler.run_mcmc(guess, n_steps)
assert results.coords.shape == (n_walkers, len(self.names))
chain = sampler.chain
assert chain.shape == (n_walkers, n_steps, len(self.names))