def test_random_distribution():
# Test with no alpha
pmf = np.array([2.48224944e-01, 5.86112396e-01, 5.26167518e-05, 1.65610043e-01])
outcomes = ((0, 0), (0, 1), (1, 0), (1, 1))
for prng in [None, dit.math.prng]:
dit.math.prng.seed(1)
d = dit.random_distribution(2, 2, prng=prng)
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with a single alphabet specified
dit.math.prng.seed(1)
d = dit.random_distribution(2, [[0, 1]])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with two alphabets specified
dit.math.prng.seed(1)
d = dit.random_distribution(2, [[0, 1], [0, 1]])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with invalid number of alphabets
assert_raises(TypeError, dit.random_distribution, 3, [3, 2])
assert_raises(TypeError, dit.random_distribution, 3, [3, 2, 3])
# Test with concentration parameters
pmf = np.array([0.15092872, 0.23236257, 0.05765063, 0.55905808])
dit.math.prng.seed(1)
d = dit.random_distribution(2, 2, alpha=[1, 2, 1, 3])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
assert_raises(ditException, dit.random_distribution, 2, 2, alpha=[1])
def test_random_distribution():
# Test with no alpha
pmf = np.array(
[2.48224944e-01, 5.86112396e-01, 5.26167518e-05, 1.65610043e-01])
outcomes = ((0, 0), (0, 1), (1, 0), (1, 1))
for prng in [None, dit.math.prng]:
dit.math.prng.seed(1)
d = dit.random_distribution(2, 2, prng=prng)
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with a single alphabet specified
dit.math.prng.seed(1)
d = dit.random_distribution(2, [[0, 1]])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with two alphabets specified
dit.math.prng.seed(1)
d = dit.random_distribution(2, [[0, 1], [0, 1]])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
# Test with invalid number of alphabets
assert_raises(TypeError, dit.random_distribution, 3, [3, 2])
assert_raises(TypeError, dit.random_distribution, 3, [3, 2, 3])
# Test with concentration parameters
pmf = np.array([0.15092872, 0.23236257, 0.05765063, 0.55905808])
dit.math.prng.seed(1)
d = dit.random_distribution(2, 2, alpha=[1, 2, 1, 3])
assert_equal(d.outcomes, outcomes)
np.testing.assert_allclose(d.pmf, pmf)
assert_raises(ditException, dit.random_distribution, 2, 2, alpha=[1])
def test_combined_variable():
"""Test that using a coalesced input gives the same result as constraining
on a bivariate marginal."""
d = dit.random_distribution(4, 2)
r1 = disclosure(d, cons=[[0, 1], [2]], output=[3])
r2 = disclosure(d.coalesce([[0, 1], [2], [3]]))
assert (np.isclose(r1, r2))
# Same, but on self-disclosure
d = dit.random_distribution(3, 2)
r1 = self_disclosure(d, cons=[[0, 1], [2]])
r2 = self_disclosure(d.coalesce([[0, 1], [2]]))
assert (np.isclose(r1, r2))
def test_simplex_grid3():
# Test with Distribution
d = dit.random_distribution(1, 2)
dists = np.asarray([x.pmf for x in dit.simplex_grid(2, 2**2, using=d)])
dists_ = np.asarray([(0.0, 1.0), (0.25, 0.75), (0.5, 0.5),
(0.75, 0.25), (1.0, 0.0)])
np.testing.assert_allclose(dists, dists_)
def test_all_constraints():
"""Test that fully constrained disclosure is zero."""
nb_reps = 10
for _ in range(nb_reps):
d = dit.random_distribution(3, 2)
syn = disclosure(d, cons=[[0, 1]])
assert (np.isclose(syn, 0))
def test_simplex_grid3():
# Test with Distribution
d = dit.random_distribution(1, 2)
dists = np.asarray([x.pmf for x in dit.simplex_grid(2, 2**2, using=d)])
dists_ = np.asarray([(0.0, 1.0), (0.25, 0.75), (0.5, 0.5), (0.75, 0.25),
(1.0, 0.0)])
np.testing.assert_allclose(dists, dists_)
def test_no_constraints_self_disclosure():
"""Test that unconstrained self-disclosure is joint entropy."""
nb_reps = 10
for _ in range(nb_reps):
d = dit.random_distribution(3, 2)
H = entropy(d)
syn = self_disclosure(d, cons=[])
assert (np.isclose(H, syn))
def test_no_constraints():
"""Test that unconstrained disclosure is MI."""
nb_reps = 10
for _ in range(nb_reps):
d = dit.random_distribution(3, 2)
MI = coinformation(d, [[0, 1], [2]])
syn = disclosure(d, cons=[])
assert (np.isclose(MI, syn))
def test_constraint_marginal():
"""Test that the marginals are actually preserved."""
nb_reps = 10
u = dit.distconst.uniform_distribution(2,2)
for _ in range(nb_reps):
Pjoint = dit.random_distribution(2, 2)
marg = np.random.choice([[0], [1], [0,1]])
P = build_constraint_matrix([marg], u)
Pmarg = Pjoint.marginal(marg)
Pmarg.make_dense()
assert(np.allclose(Pmarg.pmf, P @ Pjoint.pmf))
def test_channel():
nb_samples = 5
for _ in range(nb_samples):
# Build random dist but with I(X1; X2) = 0
r = dit.random_distribution(3,2)
pX, pYgX = r.condition_on([0,1])
u = dit.distconst.uniform_distribution(2,2)
dist = dit.cdisthelpers.joint_from_factors(u, pYgX, strict=False)
# Compute disclosure and extract optimal synergistic channel
S, C = disclosure_channel(dist)
# Optimal synergistic channel is XOR and disclosure is I(X1 xor X2; Y)
xorfun = lambda outcome: (np.mod(outcome[0] + outcome[1], 2),)
dist = dit.insert_rvf(dist, xorfun)
assert(np.allclose(C['pVgX'], [[1,0,0,1],[0,1,1,0]]))
assert(np.isclose(S, coinformation(dist, [[2],[3]])))
def test_simplex_grid4():
# Test with Distribution but with wrong length specified.
d = dit.random_distribution(2, 2)
g = dit.simplex_grid(5, 2**2, using=d)
np.testing.assert_raises(Exception, next, g)
def test_simplex_grid4():
# Test with Distribution but with wrong length specified.
d = dit.random_distribution(2, 2)
g = dit.simplex_grid(5, 2**2, using=d)
np.testing.assert_raises(Exception, next, g)
"""
Tests for the various mutual informations.
"""
import pytest
from dit import random_distribution
from dit.multivariate import (coinformation as I,
total_correlation as T,
dual_total_correlation as B,
caekl_mutual_information as J,
interaction_information as II,
)
@pytest.mark.parametrize('d', [random_distribution(2, 4) for _ in range(10)])
def test_mis1(d):
"""
Test that all the mutual informations match for bivariate distributions.
"""
i = I(d)
t = T(d)
b = B(d)
j = J(d)
ii = II(d)
assert i == pytest.approx(t)
assert t == pytest.approx(b)
assert b == pytest.approx(j)
assert j == pytest.approx(ii)
def test_simplex_grid4():
# Test with Distribution but with wrong length specified.
d = dit.random_distribution(2, 2)
g = dit.simplex_grid(5, 2**2, using=d)
with pytest.raises(Exception):
next(g)
def test_simplex_grid4():
# Test with Distribution but with wrong length specified.
d = dit.random_distribution(2, 2)
g = dit.simplex_grid(5, 2**2, using=d)
with pytest.raises(Exception):
next(g)
"""
Tests for the various mutual informations.
"""
import pytest
from dit import random_distribution
from dit.multivariate import (
coinformation as I,
total_correlation as T,
dual_total_correlation as B,
caekl_mutual_information as J,
interaction_information as II,
)
@pytest.mark.parametrize('d', [random_distribution(2, 4) for _ in range(10)])
def test_mis1(d):
"""
Test that all the mutual informations match for bivariate distributions.
"""
i = I(d)
t = T(d)
b = B(d)
j = J(d)
ii = II(d)
assert i == pytest.approx(t)
assert t == pytest.approx(b)
assert b == pytest.approx(j)
assert j == pytest.approx(ii)