def test_binary(self):
dom = Domain(['b', 'd', 'e'], [3, 5, 6])
vals = torch.rand(3, 5, 6)
factor = Factor(dom, vals)
res = self.factor * factor
ans = Domain(['a', 'b', 'c', 'd', 'e'], [2, 3, 4, 5, 6])
self.assertEqual(res.domain, ans)
res = self.factor + factor
self.assertEqual(res.domain, ans)
res = self.factor * 2.0
self.assertEqual(res.domain, self.factor.domain)
res = self.factor + 2.0
self.assertEqual(res.domain, self.factor.domain)
res = self.factor - 2.0
self.assertEqual(res.domain, self.factor.domain)
res = self.factor.exp().log()
self.assertEqual(res.domain, self.factor.domain)
self.assertTrue(np.allclose(res.datavector(),
self.factor.datavector()))
def __init__(self, dataset, specs):
self.dataset = dataset
self.specs = json.load(open(specs, 'r'))
domain_info = json.load(open('domain.json'))
# check consistency for codebook information
for col in list(domain_info):
if domain_info[col][-1] < self.specs[col]['maxval']:
print('Codebook inconsistent for', col)
del domain_info[col]
## look at ground truth data to obtain possible values for state-dependent columns
df = pd.read_csv(dataset)
for col in ['SEA', 'METAREA', 'COUNTY', 'CITY', 'METAREAD']:
domain_info[col] = sorted(df[col].unique())
## done using ground truth data
domain = { }
for col in self.specs:
if col in domain_info:
domain[col] = len(domain_info[col])
else:
domain[col] = self.specs[col]['maxval'] + 1
domain['INCWAGE_A'] = 52
domain['INCWAGE_B'] = 8
del domain['INCWAGE']
#domain['INCWAGE'] = 5002
domain['VALUEH'] = 5003
self.domain_info = domain_info
self.domain = Domain.fromdict(domain)
def setUp(self):
if skip: raise unittest.SkipTest('PyTorch not installed')
attrs = ['a', 'b', 'c']
shape = [2, 3, 4]
domain = Domain(attrs, shape)
values = torch.rand(*shape)
self.factor = Factor(domain, values)
def test_expand(self):
domain = Domain(['a', 'b', 'c', 'd'], [2, 3, 4, 5])
res = self.factor.expand(domain)
self.assertEqual(res.domain, domain)
self.assertEqual(res.values.shape, domain.shape)
res = res.sum(['d']) * 0.2
self.assertTrue(torch.allclose(res.values, self.factor.values))
def test_project(self):
res = self.factor.project(['c', 'a'], agg='sum')
ans = Domain(['c', 'a'], [4, 2])
self.assertEqual(res.domain, ans)
self.assertEqual(res.values.shape, (4, 2))
res = self.factor.project(['c', 'a'], agg='logsumexp')
self.assertEqual(res.domain, ans)
self.assertEqual(res.values.shape, (4, 2))
def load(path, domain):
""" Load data into a dataset object
:param path: path to csv file
:param domain: path to json file encoding the domain information
"""
df = pd.read_csv(path)
config = json.load(open(domain))
domain = Domain(config.keys(), config.values())
return Dataset(df, domain)
def reverse_data(data, supports):
df = data.df.copy()
newdom = {}
for col in data.domain:
support = supports[col]
mx = support.sum()
newdom[col] = int(support.size)
idx, extra = np.where(support)[0], np.where(~support)[0]
mask = df[col] == mx
if extra.size == 0:
pass
else:
df.loc[mask, col] = np.random.choice(extra, mask.sum())
df.loc[~mask, col] = idx[df.loc[~mask, col]]
newdom = Domain.fromdict(newdom)
return Dataset(df, newdom)
def postprocess(self):
#use noisy measurements to fit PGM inference
#and generate synthetic data
iters = self.iters
domain = self.domain
temp_domain = Domain.fromdict(domain)
engine = FactoredInference(temp_domain,
structural_zeros=None,
iters=10000,
log=True,
warm_start=False,
elim_order=self.elimination_order)
self.engine = engine
engine.estimate(self.measurements)
self.synthetic = self.engine.model.synthetic_data()
self.synthetic = reverse_data(self.synthetic, self.supports)
def synthesize(self, file_path, eps, seed):
# setup random state
prng = np.random.RandomState(seed)
# load data vector
relation = Relation(self.config)
relation.load_csv(file_path)
self._numerize(relation._df)
# perform measurement
attributes = [field_name for field_name in self.config.keys()]
measurements = []
w_sum = sum(Ai.weight for Ai in self.strategy.matrices)
for Ai in self.strategy.matrices:
w = Ai.weight
proj = [
attributes[i] for i, B in enumerate(Ai.base.matrices)
if type(B).__name__ != 'Ones'
]
matrix = [
B for B in Ai.base.matrices if type(B).__name__ != 'Ones'
]
matrix = EkteloMatrix(np.ones(
(1, 1))) if len(matrix) == 0 else Kronecker(matrix)
proj_rel = copy.deepcopy(relation)
proj_rel.project(proj)
if proj_rel.df.shape[1] == 0:
x = np.array([proj_rel.df.shape[0]])
else:
x = Vectorize('').transform(proj_rel).flatten()
y = Laplace(matrix, w * eps / w_sum).measure(x, prng)
measurements.append((matrix.sparse_matrix(), y, 1.0 / w, proj))
# generate synthetic data
sizes = [field['bins'] for field in self.config.values()]
dom = Domain(attributes, sizes)
engine = FactoredInference(dom)
model = engine.estimate(measurements)
df = model.synthetic_data().df
self._denumerize(df)
self._sample_numerical(df)
return df
def transform_data(data, supports):
df = data.df.copy()
newdom = {}
for col in data.domain:
support = supports[col]
size = support.sum()
newdom[col] = int(size)
if size < support.size:
newdom[col] += 1
mapping = {}
idx = 0
for i in range(support.size):
mapping[i] = size
if support[i]:
mapping[i] = idx
idx += 1
assert idx == size
df[col] = df[col].map(mapping)
newdom = Domain.fromdict(newdom)
return Dataset(df, newdom)
def randomKway(name,
number,
marginal,
proj=None,
seed=0,
filter=None,
root_path='./',
args=None):
check_size = name in ['adult_orig', 'loans']
path = os.path.join(root_path, "Datasets/{}.csv".format(name))
df = pd.read_csv(path)
domain = os.path.join(root_path, "Datasets/{}-domain.json".format(name))
config = json.load(open(domain))
domain = Domain(config.keys(), config.values())
if name == 'adult':
if args.adult_seed is not None:
prng = np.random.RandomState(args.adult_seed)
mask = prng.binomial(1, 0.9, size=len(df))
df.loc[:, '_split'] = mask
else:
df.loc[:, '_split'] = 1
if filter is not None:
col, val = filter
df = df[df[col] == val].reset_index(drop=True)
del df[col]
domain_max = max(domain.config.values())
dtype = get_min_dtype(domain_max)
df = df.astype(dtype)
data = Dataset(df, domain)
if proj is not None:
data = data.project(proj)
return data, randomKwayData(data,
number,
marginal,
seed,
check_size=check_size)
def krondot(self, matrices):
""" Compute the answer to the set of queries Q1 x Q2 X ... x Qd, where
Qi is a query matrix on the ith attribute and "x" is the Kronecker product
This may be more efficient than computing a supporting marginal then multiplying that by Q.
In particular, if each Qi has only a few rows.
:param matrices: a list of matrices for each attribute in the domain
:return: the vector of query answers
"""
assert all(M.shape[1] == n for M, n in zip(matrices, self.domain.shape)), \
'matrices must conform to the shape of the domain'
logZ = self.belief_propagation(self.potentials, logZ=True)
factors = [self.potentials[cl].exp() for cl in self.cliques]
Factor = type(factors[0]) # infer the type of the factors
elim = self.domain.attrs
for attr, Q in zip(elim, matrices):
d = Domain(['%s-answer' % attr, attr], Q.shape)
factors.append(Factor(d, Q))
result = variable_elimination(factors, elim)
result = result.transpose(['%s-answer' % a for a in elim])
return result.datavector(flatten=False) * self.total / np.exp(logZ)
from hdmm.templates import DefaultKron, Marginals, DefaultUnionKron
from hdmm import workload
from mbi import FactoredInference, Domain, Dataset
import numpy as np
from IPython import embed
# set up domain and workload
attributes = [
'A', 'B', 'C'
] # should be the names of the columns, for now just using 0 and 1
sizes = [32, 32, 32]
dom = Domain(attributes, sizes)
#W = workload.Prefix2D(32)
W = workload.DimKMarginals(sizes, 1)
data = Dataset.synthetic(dom, 1000)
# optimize strategy using HDMM
#template = DefaultKron(sizes)
#template = Marginals(sizes)
template = DefaultUnionKron(sizes, 3)
template.optimize(W)
A = template.strategy()
def take_measurements(A, data):
""" Efficiently take measurements from HDMM strategy and convert to a PGM-compatable form """
A = workload.union_kron_canonical(A)
measurements = []
for Ai in A.matrices:
w = Ai.weight
proj = [
from mbi import Dataset, FactoredInference, Domain
import numpy as np
# discrete domain with attributes A, B, C and corresponding size 4 x 5 x 6
domain = Domain(['A','B','C'], [2, 3, 4])
# synthetic dataset with 1000 rows
data = Dataset.synthetic(domain, 1000)
# project data onto subset of cols, and vectorize
ab = data.project(['A','B']).datavector()
bc = data.project(['B','C']).datavector()
# add noise to preserve differential privacy
epsilon = np.sqrt(2)
sigma = np.sqrt(2.0) / epsilon
np.random.seed(0)
yab = ab + np.random.laplace(loc=0, scale=sigma, size=ab.size)
ybc = bc + np.random.laplace(loc=0, scale=sigma, size=bc.size)
# record the measurements in a form needed by inference
Iab = np.eye(ab.size)
Ibc = np.eye(bc.size)
measurements = [(Iab, yab, sigma, ['A', 'B']),
(Ibc, ybc, sigma, ['B', 'C'])]
# estimate the data distribution
engine = FactoredInference(domain)
model = engine.estimate(measurements, engine='MD')
def test_logsumexp(self):
res = self.factor.logsumexp(['a', 'c'])
values = self.factor.values
ans = torch.log(torch.sum(torch.exp(values), dim=(0, 2)))
self.assertEqual(res.domain, Domain(['b'], [3]))
self.assertTrue(torch.allclose(res.values, ans))
def test_sum(self):
res = self.factor.sum(['a', 'b'])
self.assertEqual(res.domain, Domain(['c'], [4]))
self.assertTrue(
torch.allclose(res.values, self.factor.values.sum(dim=(0, 1))))
def test_transpose(self):
attrs = ['b', 'c', 'a']
tr = self.factor.transpose(attrs)
ans = Domain(attrs, [3, 4, 2])
self.assertEqual(tr.domain, ans)
