def test_multilayer_analysis(run=True): with wn.Analysis() as analysis: PUMS = wn.Dataset(path=TEST_CSV_PATH, column_names=test_csv_names) age = wn.to_float(PUMS['age']) sex = wn.to_bool(PUMS['sex'], true_label="TRUE") age_clamped = wn.clamp(age, lower=0., upper=150.) age_resized = wn.resize(age_clamped, n=1000) mean_age = wn.dp_mean(data=wn.to_float(PUMS['race']), privacy_usage={'epsilon': .65}, data_lower=0., data_upper=100., data_n=500) analysis.release() sex_plus_22 = wn.add(wn.to_float(sex), 22., left_n=1000, left_lower=0., left_upper=1.) wn.dp_mean(age_resized / 2. + sex_plus_22, privacy_usage={'epsilon': .1}, data_lower=mean_age - 5.2, data_upper=102., data_n=500) + 5. wn.dp_variance(data=wn.to_float(PUMS['educ']), privacy_usage={'epsilon': .15}, data_n=1000, data_lower=0., data_upper=12.) # wn.dp_moment_raw( # wn.to_float(PUMS['married']), # privacy_usage={'epsilon': .15}, # data_n=1000000, # data_lower=0., # data_upper=12., # order=3 # ) # # wn.dp_covariance( # left=wn.to_float(PUMS['age']), # right=wn.to_float(PUMS['married']), # privacy_usage={'epsilon': .15}, # left_n=1000, # right_n=1000, # left_lower=0., # left_upper=1., # right_lower=0., # right_upper=1. # ) if run: analysis.release() return analysis
def test_raw_dataset(run=True): with wn.Analysis() as analysis: wn.dp_mean( data=wn.Dataset(value=[1., 2., 3., 4., 5.], num_columns=1), privacy_usage={'epsilon': 1}, data_lower=0., data_upper=10., data_n=10, ) if run: analysis.release() return analysis
def test_dp_mean(): with wn.Analysis(): data = wn.Dataset(**generate_synthetic(float, variants=['Random'])) mean = wn.dp_mean( data['F_Random'], # privacy_usage={'epsilon': 0.1}, accuracy={ 'value': .2, 'alpha': .05 }, data_lower=0., data_upper=10., data_n=10) print("accuracy", mean.get_accuracy(0.05)) print(mean.from_accuracy(2.3, .05))
def create_dicts(data, non_income_data, plausible_variable_combinations): count_dict = dict() priv_count_dict = dict() mean_income_dict = dict() priv_mean_income_dict = dict() median_income_dict = dict() priv_median_income_dict = dict() min_income_dict = dict() priv_min_income_dict = dict() max_income_dict = dict() priv_max_income_dict = dict() # get number of data elements with each set of variable values for i, combination in enumerate(plausible_variable_combinations): # print('run {0} of {1}'.format(i+1, len(plausible_variable_combinations))) if len(combination) == 1: dt = data[non_income_data[combination[0]] == 1] elif len(combination) == 2: dt = data[(non_income_data[combination[0]] == 1) & (non_income_data[combination[1]] == 1)] elif len(combination) == 3: dt = data[(non_income_data[combination[0]] == 1) & (non_income_data[combination[1]] == 1) & (non_income_data[combination[2]] == 1)] elif len(combination) == 4: dt = data[(non_income_data[combination[0]] == 1) & (non_income_data[combination[1]] == 1) & (non_income_data[combination[2]] == 1) & (non_income_data[combination[3]] == 1)] elif len(combination) == 5: dt = data[(non_income_data[combination[0]] == 1) & (non_income_data[combination[1]] == 1) & (non_income_data[combination[2]] == 1) & (non_income_data[combination[3]] == 1) & (non_income_data[combination[4]] == 1)] count_dict['__'.join(combination)] = dt.shape[0] mean_income_dict['__'.join(combination)] = np.mean(dt['income']) median_income_dict['__'.join(combination)] = np.median(dt['income']) min_income_dict['__'.join(combination)] = np.min(dt['income']) max_income_dict['__'.join(combination)] = np.max(dt['income']) with wn.Analysis() as analysis: # load data priv_data = wn.Dataset(value=list(dt['income']), num_columns=1) # estimate sample size count = wn.dp_count(data=wn.cast(priv_data, 'FLOAT'), privacy_usage={'epsilon': .05}, lower=0, upper=1000) analysis.release() priv_count_dict['__'.join(combination)] = max(0, count.value) with wn.Analysis() as analysis: # load data priv_data = wn.Dataset(value=list(dt['income']), num_columns=1) # get mean mean = wn.dp_mean(data=wn.cast(priv_data, 'FLOAT'), privacy_usage={'epsilon': 0.1}, data_lower=0., data_upper=100_000., data_n=max(1, count.value)) # get median median = wn.dp_median(data=wn.cast(priv_data, 'FLOAT'), privacy_usage={'epsilon': 0.1}, data_lower=0., data_upper=100_000., data_n=max(1, count.value)) # get min _min = wn.dp_minimum(data=wn.cast(priv_data, 'FLOAT'), privacy_usage={'epsilon': 0.1}, data_lower=0., data_upper=100_000., data_n=max(1, count.value)) # get max _max = wn.dp_maximum(data=wn.cast(priv_data, 'FLOAT'), privacy_usage={'epsilon': 0.1}, data_lower=0., data_upper=100_000., data_n=max(1, count.value)) analysis.release() priv_mean_income_dict['__'.join(combination)] = min( max(0, mean.value), 100_000) priv_median_income_dict['__'.join(combination)] = min( max(0, median.value), 100_000) priv_min_income_dict['__'.join(combination)] = min( max(0, _min.value), 100_000) priv_max_income_dict['__'.join(combination)] = min( max(0, _max.value), 100_000) return (count_dict, priv_count_dict, mean_income_dict, priv_mean_income_dict, median_income_dict, priv_median_income_dict, min_income_dict, priv_min_income_dict, max_income_dict, priv_max_income_dict)
def test_dp_linear_stats(run=True): with wn.Analysis() as analysis: dataset_pums = wn.Dataset(path=TEST_CSV_PATH, column_names=test_csv_names) age = dataset_pums['age'] analysis.release() num_records = wn.dp_count(age, privacy_usage={'epsilon': .5}, lower=0, upper=10000) analysis.release() print("number of records:", num_records.value) vars = wn.to_float(dataset_pums[["age", "income"]]) covariance = wn.dp_covariance(data=vars, privacy_usage={'epsilon': .5}, data_lower=[0., 0.], data_upper=[150., 150000.], data_n=num_records) print("covariance released") num_means = wn.dp_mean(data=vars, privacy_usage={'epsilon': .5}, data_lower=[0., 0.], data_upper=[150., 150000.], data_n=num_records) analysis.release() print("covariance:\n", covariance.value) print("means:\n", num_means.value) age = wn.to_float(age) age_variance = wn.dp_variance(age, privacy_usage={'epsilon': .5}, data_lower=0., data_upper=150., data_n=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 = wn.clamp(age, lower=0., upper=100.) imputed_age = wn.impute(clamped_age) preprocessed_age = wn.resize(imputed_age, n=num_records) # properties necessary for mean are statically known mean = wn.dp_mean(preprocessed_age, privacy_usage={'epsilon': .5}) # properties necessary for variance are statically known variance = wn.dp_variance(preprocessed_age, privacy_usage={'epsilon': .5}) # sum doesn't need n, so I pass the data in before resizing age_sum = wn.dp_sum(imputed_age, privacy_usage={'epsilon': .5}) # mean with lower, upper properties propagated up from prior bounds transformed_mean = wn.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 = wn.laplace_mechanism(wn.mean(preprocessed_age), privacy_usage={'epsilon': .5}) custom_maximum = wn.laplace_mechanism(wn.maximum(preprocessed_age), privacy_usage={'epsilon': .5}) custom_maximum = wn.laplace_mechanism(wn.maximum(preprocessed_age), privacy_usage={'epsilon': .5}) custom_quantile = wn.laplace_mechanism(wn.quantile(preprocessed_age, alpha=.5), privacy_usage={'epsilon': 500}) income = wn.to_float(dataset_pums['income']) income_max = wn.laplace_mechanism(wn.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 = wn.dp_histogram(wn.to_int(age, lower=0, upper=100), edges=list(range(0, 100, 25)), null_value=150, privacy_usage={'epsilon': 2.}) sex_histogram = wn.dp_histogram(wn.to_bool(dataset_pums['sex'], true_label="1"), privacy_usage={'epsilon': 2.}) education_histogram = wn.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 wn.Analysis(dynamic=True) as analysis: data = wn.Dataset(path=TEST_CSV_PATH, column_names=test_csv_names) age_int = wn.to_int(data['age'], 0, 150) sex = wn.to_bool(data['sex'], "1") educ = wn.to_float(data['educ']) race = data['race'] income = wn.to_float(data['income']) married = wn.to_bool(data['married'], "1") numerics = wn.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 = numerics[0] income = numerics[[False, True]] # boolean ops and broadcasting mask = sex & married | (~married ^ False) | (age > 50.) | (age_int == 25) # numerical clamping wn.clamp(numerics, 0., [150., 150_000.]) wn.clamp(data['educ'], categories=[str(i) for i in range(8, 10)], null_value="-1") wn.count(mask) wn.covariance(age, income) wn.digitize(educ, edges=[1., 3., 10.], null_value=-1) # checks for safety against division by zero income / 2. income / wn.clamp(educ, 5., 20.) wn.dp_count(data, privacy_usage={"epsilon": 0.5}) wn.dp_count(mask, privacy_usage={"epsilon": 0.5}) wn.dp_histogram(mask, privacy_usage={"epsilon": 0.5}) age = wn.impute(wn.clamp(age, 0., 150.)) wn.dp_maximum(age, privacy_usage={"epsilon": 0.5}) wn.dp_minimum(age, privacy_usage={"epsilon": 0.5}) wn.dp_median(age, privacy_usage={"epsilon": 0.5}) age_n = wn.resize(age, n=800) wn.dp_mean(age_n, privacy_usage={"epsilon": 0.5}) wn.dp_moment_raw(age_n, order=3, privacy_usage={"epsilon": 0.5}) wn.dp_sum(age, privacy_usage={"epsilon": 0.5}) wn.dp_variance(age_n, privacy_usage={"epsilon": 0.5}) wn.filter(income, mask) race_histogram = wn.histogram(race, categories=["1", "2", "3"], null_value="3") wn.histogram(income, edges=[0., 10000., 50000.], null_value=-1) wn.dp_histogram(married, privacy_usage={"epsilon": 0.5}) wn.gaussian_mechanism(race_histogram, privacy_usage={ "epsilon": 0.5, "delta": .000001 }) wn.laplace_mechanism(race_histogram, privacy_usage={ "epsilon": 0.5, "delta": .000001 }) wn.kth_raw_sample_moment(educ, k=3) wn.log(wn.clamp(educ, 0.001, 50.)) wn.maximum(educ) wn.mean(educ) wn.minimum(educ) educ % 2. educ**2. wn.quantile(educ, .32) wn.resize(educ, 1200, 0., 50.) wn.resize(race, 1200, categories=["1", "2"], weights=[1, 2]) wn.resize(data[["age", "sex"]], 1200, categories=[["1", "2"], ["a", "b"]], weights=[1, 2]) wn.resize(data[["age", "sex"]], 1200, categories=[["1", "2"], ["a", "b", "c"]], weights=[[1, 2], [3, 7, 2]]) wn.sum(educ) wn.variance(educ) if run: analysis.release() return analysis