- def get_named_version(data):
- named_columns = ['age', 'workclass', 'fnlwgt', 'education', 'education-num',
- 'marital-status', 'occupation', 'relationship', 'race', 'sex',
- 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
- 'income']
- data_named = copy.deepcopy(data)
- data_named.columns = named_columns
- return data_named
- def eval_harness(real, synthetic, private_synthetic, random):
- real, synthetic, private_synthetic, random = [get_named_version(d) for d in [real, synthetic, private_synthetic, random]]
- def eval_single(real, synthetic, name='non-private'):
- print('#' * 10 + f' {name} ' + '#' * 10)
- print('chi-square =', CSTest.compute(real, synthetic), 'komolgorov-smirnov =', KSTest.compute(real, synthetic))
- print('train syn test real =', MulticlassDecisionTreeClassifier.compute(real, synthetic, target='income'))
- train = real.sample(int(len(real) * 0.75))
- test = real[~real.index.isin(train.index)]
- MulticlassDecisionTreeClassifier.compute(test, train, target='income')
- print('train real test real =', MulticlassDecisionTreeClassifier.compute(synthetic, synthetic, target='income'))
- print('1 - ROCAUC =', LogisticDetection.compute(real, synthetic))
- print()
- eval_single(real, random, name='random')
- eval_single(real, synthetic, name='non-private')
- eval_single(real, private_synthetic, name='private')
- eval_harness(data, synthetic_df, private_synthetic_df, random_df)
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