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Private Aggregate Seeded from PAC-Synth
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A differentially-private synthesizer that computes differentially private marginals to build synthetic data. It will aggregate n-way marginals up to and including a specified reporting length, and synthesize data based on the computed aggregated counts.

Based on the `Synthetic Data Showcase project <https://github.com/microsoft/synthetic-data-showcase>`_. DP documentation available `here <https://github.com/microsoft/synthetic-data-showcase/tree/main/docs/dp>`_.


.. code-block:: python

  import pandas as pd
  from snsynth import Synthesizer

  pums = pd.read_csv("PUMS.csv")

  synth = Synthesizer.create("pacsynth", epsilon=3.0, verbose=True)
  synth.fit(pums, preprocessor_eps=1.0)
  pums_synth = synth.sample(1000)


The pac-synth synthesizer will suppress marginal combinations that could uniquely fingerprint individuals.  Unlike the other synthesizers, however, this synthesizer attempts to minimize the number of spurious dimension combinations that are generated.  This may be desirable in some settings, where the goal is to generate synthetic data with dimensions that are as similar as possible to the original data.  To achieve this dimensional fidelity, the pac-synth synthesizer will sometimes generate rows with missing values.

.. code-block:: python

  from snsynth.aggregate_seeded import AggregateSeededSynthesizer

  # this generates a random pandas data frame with 5000 records
  # replace this with your own data
  sensitive_df = gen_data_frame(5000)

  # build synthesizer
  synth = AggregateSeededSynthesizer(epsilon=0.5)
  synth.fit(sensitive_df)

  # sample 5000 records and build a data frame
  synthetic_df = sensitive_df.sample(5000)

  # show 10 example records
  print(synthetic_df.sample(10))

  # this will output
  #      H1 H2  H3 H4 H5 H6 H7 H8 H9 H10
  # 2791  2         1  0  1  1  1  0   1
  # 2169  1  3   4  1  0  1  0  1  1   0
  # 4607     4   7  1  1  0  1  1  1   0
  # 4803  1      8  0  0  0  1  1  1   1
  # 2635         8  0  1  1  1  0  1   0
  # 537   1         1  1  1  1  1  0   0
  # 3495     6   7  0  0  1  0  0  1   0
  # 2009  1  3   3  0  0  1  0  1  1   0
  # 3214  1  5      1  1  1  1  1  0   1
  # 4879  2  5  10  0  1  1  1  1  1   1

For more, see the `samples notebook <https://github.com/opendp/smartnoise-sdk/tree/main/synth/samples/aggregate_seeded_sample>`_.

Parameters
----------

.. autoclass:: snsynth.aggregate_seeded.AggregateSeededSynthesizer