def test_hhgq_va(data_answers_name, cons_name):
## Get the constraint, data with answers and schema
c, data_answers, schema = ctu.getConstraintDataAnswersSchema(
schemaname, data_answers_name,
ctu.getAllImplementedInvariantNames(schemaname), cons_name)
# Check sign and right hand side
assert c.sign == "le" if cons_name == 'hhgq_va_ub' else "ge"
assert np.array_equal(c.rhs, data_answers[cons_name])
if c.sign == "le":
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) <= c.rhs)
else:
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) >= c.rhs)
# Check Kronecker factors
# Each column of the HHGQ matrix selects that GQ type (so only one True value -- on the diagonal)
ctu.assertKronFactorDiagonal(c, CC.ATTR_HHGQ, schema)
# For non-voting, select first AGECAT_VA_CUT categories, for voting select the other categories
ctu.assertKronFactor(
c, CC.ATTR_AGECAT,
np.array([[True] * AGECAT_VA_CUT + [False] *
(ctu.dimSch(CC.ATTR_AGECAT, schema) - AGECAT_VA_CUT),
[False] * AGECAT_VA_CUT + [True] *
(ctu.dimSch(CC.ATTR_AGECAT, schema) - AGECAT_VA_CUT)]),
schema)
# Selecting everyone on all other axes
for dimname in (CC.ATTR_SEX, CC.ATTR_HISP, CC.ATTR_CENRACE):
ctu.assertKronFactorSelectAll(c, dimname, schema)
def test_householder_ub(data_answers_name):
cons_name = "householder_ub"
inv_names = list(ctu.getAllImplementedInvariantNames(schemaname))
c, data_answers, schema = ctu.getConstraintDataAnswersSchema(schemaname, data_answers_name, inv_names, cons_name)
# Check right hand side
assert np.all(c.rhs == data_answers[cons_name])
# Check left hand side
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) == data_answers[cons_name + "_lhs"])
# Check sign
assert c.sign == "le"
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) <= c.rhs)
dim_hhgq = ctu.dimSch(CC.ATTR_RELGQ, schema)
kf_hhgq = c.query.queries[0].kronFactors()[ctu.indSch(CC.ATTR_RELGQ, schema)]
assert kf_hhgq.shape == (1, dim_hhgq)
# True for 0,1 -- householder living alone and householder not living alone
assert np.array_equal(kf_hhgq[0, :], np.array([True] * 2 + [False] * (dim_hhgq - 2)))
# Selecting everyone on all other axes
for dimname in ctu.excludeAx((CC.ATTR_RELGQ,), schema):
ctu.assertKronFactorSelectAll(c, dimname, schema)
def test_no_refhh_under_15(data_answers_name):
cons_name = 'no_refhh_under_15'
# Get the constraint, data with answers and schema
c, data_answers, schema = ctu.getConstraintDataAnswersSchema(schemaname, data_answers_name, ctu.getAllImplementedInvariantNames(schemaname), cons_name)
assert c.sign == "="
assert c.rhs[0] == 0
assert ctu.lhsFromMatrix(c, data_answers["hist"][0])[0] == data_answers[cons_name]
# Selecting relatives
# 'Householder (0)', 'Husband/Wife (1)', 'Father/Mother (6)', 'Parent-in-law (8)', 'Son/Daughter-in-law (9)', 'Housemate, Roommate (12)',
# 'Unmarried Partner (13)'
age_cut = 15
ans = np.array([[False] * ctu.dimSch(CC.ATTR_REL, schema)])
ans[0, [0, 1, 6, 8, 9, 12, 13]] = True
ctu.assertKronFactor(c, CC.ATTR_REL, ans, schema)
# Selecting people under age_cut (First age_cut True, the rest are False)
ctu.assertKronFactor(c, CC.ATTR_AGE, np.array([[True] * age_cut + [False] * (ctu.dimSch(CC.ATTR_AGE, schema) - age_cut)]), schema)
# Everyone on all other axes
for dimname in ctu.excludeAx((CC.ATTR_REL, CC.ATTR_AGE), schema):
ctu.assertKronFactorSelectAll(c, dimname, schema)
def test_hhgq_total(data_answers_name, tot_inv, cons_name):
# Create invariant names, remove 'tot' if checking without total
inv_names = list(ctu.getAllImplementedInvariantNames(schemaname))
if not tot_inv:
inv_names.remove('tot')
# Get the constraint, data with answers and schema
c, data_answers, schema = ctu.getConstraintDataAnswersSchema(schemaname, data_answers_name, inv_names, cons_name)
# Check sign, <= for upper bound, >= for lower
assert c.sign == "le" if cons_name == 'hhgq_total_ub' else "ge"
# Check right hand side
if tot_inv:
assert np.array_equal(c.rhs, data_answers[cons_name])
else:
assert np.array_equal(c.rhs, data_answers[cons_name + "_no_tot"])
# Check right hand side from matrix application to the data
if c.sign == "le":
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) <= c.rhs)
else:
assert np.all(ctu.lhsFromMatrix(c, data_answers["hist"][0]) >= c.rhs)
# Should get 1 + (schDim(HHGQ) - NUM_HH_CATS) rows x schDim(HHGQ) columns
dim_hhgq = ctu.dimSch(CC.ATTR_REL, schema)
dim_gq = dim_hhgq - NUM_HH_CATS
kf_hhgq = c.query.queries[0].kronFactors()[ctu.indSch(CC.ATTR_REL, schema)]
assert kf_hhgq.shape == (dim_gq + 1, dim_hhgq)
# First row is selecting first NUM_HH_CATS, and dropping all the GQs, so first NUM_HH_CATS values True, then False
assert np.array_equal(kf_hhgq[0, :], np.array([True] * NUM_HH_CATS + [False] * dim_gq))
# The rest rows drop first NUM_HH_CATS values
assert np.array_equal(kf_hhgq[1:, :NUM_HH_CATS], np.broadcast_to(np.array([False]), (dim_gq, NUM_HH_CATS)))
# and select only the corresponding GQ type, leaving a diagonal of True in lower right corner of the matrix
assert np.array_equal(kf_hhgq[1:, NUM_HH_CATS:], np.diag([True]*dim_gq))
# Selecting everyone on all other axes
for dimname in ctu.excludeAx((CC.ATTR_REL, ), schema):
ctu.assertKronFactorSelectAll(c, dimname, schema)
def test_voting_age(data_answers_name):
cons_name = 'voting_age'
## Get the constraint, data with answers and schema
c, data_answers, schema = ctu.getConstraintDataAnswersSchema(
schemaname, data_answers_name,
ctu.getAllImplementedInvariantNames(schemaname), cons_name)
# Check sign and right hand side, in constraint and from query matrix applied to the data
assert c.sign == "="
assert c.rhs[0] == data_answers[cons_name]
assert ctu.lhsFromMatrix(
c, data_answers["hist"][0])[0] == data_answers[cons_name]
# Check Kronecker factors
# The values below AGECAT_VA_CUT are non-voting, selecting the other ones
ctu.assertKronFactorMiltiple(
c, CC.ATTR_AGECAT,
range(AGECAT_VA_CUT, ctu.dimSch(CC.ATTR_AGECAT, schema)), schema)
# Selecting everyone on other axes
for dimname in (CC.ATTR_HHGQ, CC.ATTR_SEX, CC.ATTR_HISP, CC.ATTR_CENRACE):
ctu.assertKronFactorSelectAll(c, dimname, schema)