def test_histogram():
import numpy as np
# establish data information
data = np.genfromtxt(TEST_PUMS_PATH, delimiter=',', names=True)
education_categories = [
"1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17"
]
income = list(data[:]['income'])
income_edges = list(range(0, 100_000, 10_000))
print('actual', np.histogram(income, bins=income_edges)[0])
with sn.Analysis() as analysis:
data = sn.Dataset(path=TEST_PUMS_PATH, column_names=TEST_PUMS_NAMES)
income = sn.to_int(data['income'], lower=0, upper=0)
sex = sn.to_bool(data['sex'], true_label="1")
income_histogram = sn.dp_histogram(income,
edges=income_edges,
privacy_usage={'epsilon': 1.})
analysis.release()
print("Income histogram Geometric DP release: " +
str(income_histogram.value))
def analyze(data):
educ = sn.clamp(sn.to_int(sn.index(data, indices=0),
lower=0,
upper=15),
categories=list(range(15)),
null_value=-1)
income = sn.index(data, indices=1)
repartitioned = sn.partition(income, by=educ)
inner_count = {}
inner_means = {}
for key in [5, 8, 12]:
educ_level_part = repartitioned[key]
inner_count[key] = sn.dp_count(educ_level_part,
privacy_usage={"epsilon": 0.4})
inner_means[key] = sn.mean(
sn.resize(educ_level_part,
number_rows=sn.row_min(1, inner_count[key] * 4 //
5)))
return sn.union(inner_means), sn.union(inner_count)
def test_dp_linear_stats(run=True):
with sn.Analysis() as analysis:
dataset_pums = sn.Dataset(path=TEST_PUMS_PATH,
column_names=TEST_PUMS_NAMES)
age = dataset_pums['age']
analysis.release()
num_records = sn.dp_count(age,
privacy_usage={'epsilon': .5},
lower=0,
upper=10000)
analysis.release()
print("number of records:", num_records.value)
vars = sn.to_float(dataset_pums[["age", "income"]])
covariance = sn.dp_covariance(data=vars,
privacy_usage={'epsilon': .5},
data_lower=[0., 0.],
data_upper=[150., 150000.],
data_rows=num_records)
print("covariance released")
num_means = sn.dp_mean(data=vars,
privacy_usage={'epsilon': .5},
data_lower=[0., 0.],
data_upper=[150., 150000.],
data_rows=num_records)
analysis.release()
print("covariance:\n", covariance.value)
print("means:\n", num_means.value)
age = sn.to_float(age)
age_variance = sn.dp_variance(age,
privacy_usage={'epsilon': .5},
data_lower=0.,
data_upper=150.,
data_rows=num_records)
analysis.release()
print("age variance:", age_variance.value)
# If I clamp, impute, resize, then I can reuse their properties for multiple statistics
clamped_age = sn.clamp(age, lower=0., upper=100.)
imputed_age = sn.impute(clamped_age)
preprocessed_age = sn.resize(imputed_age, number_rows=num_records)
# properties necessary for mean are statically known
mean = sn.dp_mean(preprocessed_age, privacy_usage={'epsilon': .5})
# properties necessary for variance are statically known
variance = sn.dp_variance(preprocessed_age,
privacy_usage={'epsilon': .5})
# sum doesn't need n, so I pass the data in before resizing
age_sum = sn.dp_sum(imputed_age, privacy_usage={'epsilon': .5})
# mean with lower, upper properties propagated up from prior bounds
transformed_mean = sn.dp_mean(-(preprocessed_age + 2.),
privacy_usage={'epsilon': .5})
analysis.release()
print("age transformed mean:", transformed_mean.value)
# releases may be pieced together from combinations of smaller components
custom_mean = sn.laplace_mechanism(sn.mean(preprocessed_age),
privacy_usage={'epsilon': .5})
custom_maximum = sn.laplace_mechanism(sn.maximum(preprocessed_age),
privacy_usage={'epsilon': .5})
custom_maximum = sn.laplace_mechanism(sn.maximum(preprocessed_age),
privacy_usage={'epsilon': .5})
custom_quantile = sn.laplace_mechanism(sn.quantile(preprocessed_age,
alpha=.5),
privacy_usage={'epsilon': 500})
income = sn.to_float(dataset_pums['income'])
income_max = sn.laplace_mechanism(sn.maximum(income,
data_lower=0.,
data_upper=1000000.),
privacy_usage={'epsilon': 10})
# releases may also be postprocessed and reused as arguments to more components
age_sum + custom_maximum * 23.
analysis.release()
print("laplace quantile:", custom_quantile.value)
age_histogram = sn.dp_histogram(sn.to_int(age, lower=0, upper=100),
edges=list(range(0, 100, 25)),
null_value=150,
privacy_usage={'epsilon': 2.})
sex_histogram = sn.dp_histogram(sn.to_bool(dataset_pums['sex'],
true_label="1"),
privacy_usage={'epsilon': 2.})
education_histogram = sn.dp_histogram(dataset_pums['educ'],
categories=["5", "7", "10"],
null_value="-1",
privacy_usage={'epsilon': 2.})
analysis.release()
print("age histogram: ", age_histogram.value)
print("sex histogram: ", sex_histogram.value)
print("education histogram: ", education_histogram.value)
if run:
analysis.release()
# get the mean computed when release() was called
print(mean.value)
print(variance.value)
return analysis
def test_everything(run=True):
with sn.Analysis() as analysis:
data = sn.Dataset(path=TEST_PUMS_PATH, column_names=TEST_PUMS_NAMES)
age_int = sn.to_int(data['age'], 0, 150)
sex = sn.to_bool(data['sex'], "1")
educ = sn.to_float(data['educ'])
race = data['race']
income = sn.to_float(data['income'])
married = sn.to_bool(data['married'], "1")
numerics = sn.to_float(data[['age', 'income']])
# intentionally busted component
# print("invalid component id ", (sex + "a").component_id)
# broadcast scalar over 2d, broadcast scalar over 1d, columnar broadcasting, left and right mul
numerics * 2. + 2. * educ
# add different values for each column
numerics + [[1., 2.]]
# index into first column
age = sn.index(numerics, indices=0)
income = sn.index(numerics, mask=[False, True])
# boolean ops and broadcasting
mask = sex & married | (~married ^ False) | (age > 50.) | (age_int
== 25)
# numerical clamping
sn.clamp(numerics, 0., [150., 150_000.])
sn.clamp(data['educ'],
categories=[str(i) for i in range(8, 10)],
null_value="-1")
sn.count(mask)
sn.covariance(age, income)
sn.digitize(educ, edges=[1., 3., 10.], null_value=-1)
# checks for safety against division by zero
income / 2.
income / sn.clamp(educ, 5., 20.)
sn.dp_count(data, privacy_usage={"epsilon": 0.5})
sn.dp_count(mask, privacy_usage={"epsilon": 0.5})
sn.dp_histogram(mask, privacy_usage={"epsilon": 0.5})
age = sn.impute(sn.clamp(age, 0., 150.))
sn.dp_maximum(age, privacy_usage={"epsilon": 0.5})
sn.dp_minimum(age, privacy_usage={"epsilon": 0.5})
sn.dp_median(age, privacy_usage={"epsilon": 0.5})
age_n = sn.resize(age, number_rows=800)
sn.dp_mean(age_n, privacy_usage={"epsilon": 0.5})
sn.dp_raw_moment(age_n, order=3, privacy_usage={"epsilon": 0.5})
sn.dp_sum(age, privacy_usage={"epsilon": 0.5})
sn.dp_variance(age_n, privacy_usage={"epsilon": 0.5})
sn.filter(income, mask)
race_histogram = sn.histogram(race,
categories=["1", "2", "3"],
null_value="3")
sn.histogram(income, edges=[0., 10000., 50000.], null_value=-1)
sn.dp_histogram(married, privacy_usage={"epsilon": 0.5})
sn.gaussian_mechanism(race_histogram,
privacy_usage={
"epsilon": 0.5,
"delta": .000001
})
sn.laplace_mechanism(race_histogram,
privacy_usage={
"epsilon": 0.5,
"delta": .000001
})
sn.raw_moment(educ, order=3)
sn.log(sn.clamp(educ, 0.001, 50.))
sn.maximum(educ)
sn.mean(educ)
sn.minimum(educ)
educ % 2.
educ**2.
sn.quantile(educ, .32)
sn.resize(educ, number_rows=1200, lower=0., upper=50.)
sn.resize(race,
number_rows=1200,
categories=["1", "2"],
weights=[1, 2])
sn.resize(data[["age", "sex"]],
1200,
categories=[["1", "2"], ["a", "b"]],
weights=[1, 2])
sn.resize(data[["age", "sex"]],
1200,
categories=[["1", "2"], ["a", "b", "c"]],
weights=[[1, 2], [3, 7, 2]])
sn.sum(educ)
sn.variance(educ)
if run:
analysis.release()
return analysis