def test_disequilibrium2():
"""
Test that while the XOR distribution has non-zero disequilibrium, all its
marginals have zero disequilibrium (are uniform).
"""
assert disequilibrium(d2) > 0
for rvs in combinations(flatten(d2.rvs), 2):
assert disequilibrium(d2, rvs) == pytest.approx(0)
def test_disequilibrium2():
"""
Test that while the XOR distribution has non-zero disequilibrium, all its
marginals have zero disequilibrium (are uniform).
"""
assert disequilibrium(d2) > 0
for rvs in combinations(flatten(d2.rvs), 2):
assert disequilibrium(d2, rvs) == pytest.approx(0)
def test_disequilibrium2():
"""
Test that while the XOR distribution has non-zero disequilibrium, all its
marginals have zero disequilibrium (are uniform).
"""
assert_true(disequilibrium(d2) > 0)
for rvs in combinations(flatten(d2.rvs), 2):
assert_almost_equal(disequilibrium(d2, rvs), 0)
def __init__(self,
dist,
inputs=None,
output=None,
reds=None,
pis=None,
**kwargs):
"""
Parameters
----------
dist : Distribution
The distribution to compute the decomposition on.
inputs : iter of iters, None
The set of input variables. If None, `dist.rvs` less indices
in `output` is used.
output : iter, None
The output variable. If None, `dist.rvs[-1]` is used.
reds : dict, None
Redundancy values pre-assessed.
pis : dict, None
Partial information values pre-assessed.
"""
self._dist = dist
if output is None:
output = dist.rvs[-1]
if inputs is None:
inputs = [var for var in dist.rvs if var[0] not in output]
self._inputs = tuple(map(tuple, inputs))
self._output = tuple(output)
self._kwargs = kwargs
self._lattice = full_constraint_lattice(self._inputs)
# To compute the Mobius inversion reusing dit's code, we reverse the
# lattice, compute Mobius, and reverse it back again.
self._lattice = self._lattice.reverse()
self._lattice.root = next(iter(nx.topological_sort(self._lattice)))
self._total = coinformation(
self._dist, [list(flatten(self._inputs)), self._output])
self._reds = {} if reds is None else reds
self._pis = {} if pis is None else pis
self._compute()
self._lattice = self._lattice.reverse()
self._lattice.root = next(iter(nx.topological_sort(self._lattice)))
def _total_correlation_ksg_scipy(data, rvs, crvs=None, k=4, noise=1e-10):
"""
Compute the total correlation from observations. The total correlation is computed between the columns
specified in `rvs`, given the columns specified in `crvs`. This utilizes the KSG kNN density estimator,
and works on discrete, continuous, and mixed data.
Parameters
----------
data : np.array
A set of observations of a distribution.
rvs : iterable of iterables
The columns for which the total correlation is to be computed.
crvs : iterable
The columns upon which the total correlation should be conditioned.
k : int
The number of nearest neighbors to use in estimating the local kernel density.
noise : float
The standard deviation of the normally-distributed noise to add to the data.
Returns
-------
tc : float
The total correlation of `rvs` given `crvs`.
Notes
-----
The total correlation is computed in bits, not nats as most KSG estimators do.
"""
# KSG suggest adding noise (to break symmetries?)
data = _fuzz(data, noise)
if crvs is None:
crvs = []
digamma_N = digamma(len(data))
log_2 = np.log(2)
all_rvs = list(flatten(rvs)) + crvs
rvs = [rv + crvs for rv in rvs]
d_rvs = [len(data[0, rv]) for rv in rvs]
tree = cKDTree(data[:, all_rvs])
tree_rvs = [cKDTree(data[:, rv]) for rv in rvs]
epsilons = tree.query(data[:, all_rvs], k + 1,
p=np.inf)[0][:, -1] # k+1 because of self
n_rvs = [
np.array([
len(t.query_ball_point(point, epsilon, p=np.inf))
for point, epsilon in zip(data[:, rv], epsilons)
]) for rv, t in zip(rvs, tree_rvs)
]
log_epsilons = np.log(epsilons)
h_rvs = [-digamma(n_rv).mean() for n_rv, d in zip(n_rvs, d_rvs)]
h_all = -digamma(k)
if crvs:
tree_crvs = cKDTree(data[:, crvs])
n_crvs = np.array([
len(tree_crvs.query_ball_point(point, epsilon, p=np.inf))
for point, epsilon in zip(data[:, crvs], epsilons)
])
h_crvs = -digamma(n_crvs).mean()
else:
h_rvs = [
h_rv + digamma_N + d * (log_2 - log_epsilons).mean()
for h_rv, d in zip(h_rvs, d_rvs)
]
h_all += digamma_N + sum(d_rvs) * (log_2 - log_epsilons).mean()
h_crvs = 0
tc = sum(h_rv - h_crvs for h_rv in h_rvs) - (h_all - h_crvs)
return tc / log_2
def _total_correlation_ksg_scipy(data, rvs, crvs=None, k=4, noise=1e-10):
"""
Compute the total correlation from observations. The total correlation is computed between the columns
specified in `rvs`, given the columns specified in `crvs`. This utilizes the KSG kNN density estimator,
and works on discrete, continuous, and mixed data.
Parameters
----------
data : np.array
A set of observations of a distribution.
rvs : iterable of iterables
The columns for which the total correlation is to be computed.
crvs : iterable
The columns upon which the total correlation should be conditioned.
k : int
The number of nearest neighbors to use in estimating the local kernel density.
noise : float
The standard deviation of the normally-distributed noise to add to the data.
Returns
-------
tc : float
The total correlation of `rvs` given `crvs`.
Notes
-----
The total correlation is computed in bits, not nats as most KSG estimators do.
"""
# KSG suggest adding noise (to break symmetries?)
data = _fuzz(data, noise)
if crvs is None:
crvs = []
digamma_N = digamma(len(data))
log_2 = np.log(2)
all_rvs = list(flatten(rvs)) + crvs
rvs = [rv + crvs for rv in rvs]
d_rvs = [len(data[0, rv]) for rv in rvs]
tree = cKDTree(data[:, all_rvs])
tree_rvs = [cKDTree(data[:, rv]) for rv in rvs]
epsilons = tree.query(data[:, all_rvs], k + 1, p=np.inf)[0][:, -1] # k+1 because of self
n_rvs = [
np.array([len(t.query_ball_point(point, epsilon, p=np.inf)) for point, epsilon in zip(data[:, rv], epsilons)])
for rv, t in zip(rvs, tree_rvs)]
log_epsilons = np.log(epsilons)
h_rvs = [-digamma(n_rv).mean() for n_rv, d in zip(n_rvs, d_rvs)]
h_all = -digamma(k)
if crvs:
tree_crvs = cKDTree(data[:, crvs])
n_crvs = np.array([len(tree_crvs.query_ball_point(point, epsilon, p=np.inf)) for point, epsilon in
zip(data[:, crvs], epsilons)])
h_crvs = -digamma(n_crvs).mean()
else:
h_rvs = [h_rv + digamma_N + d * (log_2 - log_epsilons).mean() for h_rv, d in zip(h_rvs, d_rvs)]
h_all += digamma_N + sum(d_rvs) * (log_2 - log_epsilons).mean()
h_crvs = 0
tc = sum([h_rv - h_crvs for h_rv in h_rvs]) - (h_all - h_crvs)
return tc / log_2