def test_fail_groupby(): with sn.Analysis() as analysis: data = sn.Dataset(path=TEST_PUMS_PATH, column_names=TEST_PUMS_NAMES) is_male = sn.to_bool(data['sex'], true_label="1") educ_inc = sn.impute( sn.clamp(sn.to_float(data[['educ', 'income']]), lower=[0., 0.], upper=[15., 200_000.])) partitioned = sn.partition(educ_inc, by=is_male) bounds = { "data_lower": [0., 0.], "data_upper": [15., 200_000.], "data_rows": 500 } union = sn.union({ True: sn.mean(partitioned[True], privacy_usage={"epsilon": 0.1}, **bounds), False: sn.mean(partitioned[False], **bounds), }) sn.laplace_mechanism(union, privacy_usage={"epsilon": 1.0}) print(analysis.privacy_usage)
def test_groupby_4(): # now union private data, and apply mechanism after with sn.Analysis() as analysis: data = sn.Dataset(path=TEST_PUMS_PATH, column_names=TEST_PUMS_NAMES) is_male = sn.to_bool(data['sex'], true_label="1") educ_inc = sn.impute( sn.clamp(sn.to_float(data[['educ', 'income']]), lower=[0., 0.], upper=[15., 200_000.])) partitioned = sn.partition(educ_inc, by=is_male) means = {} for cat in is_male.categories: part = partitioned[cat] part = sn.resize(part, number_rows=500) part = sn.mean(part) means[cat] = part union = sn.union(means) noised = sn.laplace_mechanism(union, privacy_usage={"epsilon": 1.0}) # analysis.plot() analysis.release() print(analysis.privacy_usage) print(noised.value)
def test_snapping(): with sn.Analysis(): PUMS = sn.Dataset(path=TEST_PUMS_PATH, column_names=TEST_PUMS_NAMES) statistic = sn.mean(sn.to_float(PUMS['age']), data_lower=0., data_upper=100., data_rows=1000) privatized = sn.snapping_mechanism(statistic, lower=30., upper=70., binding_probability=0.4, privacy_usage={"epsilon": 0.1}) print(privatized.value) print(privatized.get_accuracy(0.5)) necessary_usage = privatized.from_accuracy(4., 0.5) print("usage: ", necessary_usage) print("accuracy: ", privatized.get_accuracy(0.5, necessary_usage))
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
def analyze(data): return sn.mean(sn.resize(data, number_rows=500))