Bump Release to 1.0.2 (#581)

* Bump version to take use snssql 1.0.2

* Show proper message and abort if mbi not installed

---------

Co-authored-by: Joshua <joshua-oss@users.noreply.github.com>

synth/HISTORY.md

@@ -1,3 +1,8 @@
+# SmartNoise Synth v1.0.2 Release Notes
+
+* Switch to use SmartNoise SQL v1.0.2
+* Synthesizers now convert integer epsilon to float rather than crashing (thanks, @lo2aayy)
+
 # SmartNoise Synth v1.0.1 Release Notes
 
 * Upgrade to OpenDP v0.7.0

synth/VERSION

@@ -1 +1 @@
-1.0.1
+1.0.2

synth/docs/source/index.rst

@@ -51,6 +51,10 @@ Preprocessor Hints
 
 When inferring a preprocessor, the synthesizer first attempts to determine whether each column is categorical, ordinal, or continuous.  To skip this step and tell the synthesizer how to treat each column, you can pass in the ``categorical_columns``, ``ordinal_columns``, and ``continuous_columns`` arguments to the ``fit()`` method.  Additionally, if you know that columns can have missing values, you can specify ``nullable=True``.
 
+.. note::
+
+  Before using the MST synthesizer, please install ``mbi`` by running ``pip install git+https://github.com/ryan112358/private-pgm.git``.
+
 .. code-block:: python
 
   from snsynth import Synthesizer

synth/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "smartnoise-synth"
-version = "1.0.1"
+version = "1.0.2"
 description = "Differentially Private Synthetic Data"
 authors = ["SmartNoise Team <smartnoise@opendp.org>"]
 license = "MIT"
@@ -15,7 +15,7 @@ opendp = "^0.7.0"
 opacus = "^0.14.0"
 torch = "<2.0.0"
 pac-synth = "^0.0.8"
-smartnoise-sql = "^1.0.1"
+smartnoise-sql = "^1.0.2"
 Faker = "^15.0.0"
 
 [tool.poetry.dev-dependencies]

synth/setup.py

@@ -25,7 +25,7 @@
 
 setup_kwargs = {
     'name': 'smartnoise-synth',
-    'version': '1.0.1',
+    'version': '1.0.2',
     'description': 'Differentially Private Synthetic Data',
     'long_description': '[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Python](https://img.shields.io/badge/python-3.7%20%7C%203.8-blue)](https://www.python.org/)\n\n<a href="https://smartnoise.org"><img src="https://github.com/opendp/smartnoise-sdk/raw/main/images/SmartNoise/SVG/Logo%20Mark_grey.svg" align="left" height="65" vspace="8" hspace="18"></a>\n\n# SmartNoise Synthesizers\n\nDifferentially private synthesizers for tabular data.  Package includes:\n* MWEM\n* MST\n* QUAIL\n* DP-CTGAN\n* PATE-CTGAN\n* PATE-GAN\n* AIM\n\n## Installation\n\n```\npip install smartnoise-synth\n```\n\n## Using\n\nPlease see the [SmartNoise synthesizers documentation](https://docs.smartnoise.org/synth/index.html) for usage examples.\n\n## Note on Inputs\n\nMWEM and MST require columns to be categorical. If you have columns with continuous values, you should discretize them before fitting.  Take care to discretize in a way that does not reveal information about the distribution of the data.\n\n## Communication\n\n- You are encouraged to join us on [GitHub Discussions](https://github.com/opendp/opendp/discussions/categories/smartnoise)\n- Please use [GitHub Issues](https://github.com/opendp/smartnoise-sdk/issues) for bug reports and feature requests.\n- For other requests, including security issues, please contact us at [smartnoise@opendp.org](mailto:smartnoise@opendp.org).\n\n## Releases and Contributing\n\nPlease let us know if you encounter a bug by [creating an issue](https://github.com/opendp/smartnoise-sdk/issues).\n\nWe appreciate all contributions. Please review the [contributors guide](../contributing.rst). We welcome pull requests with bug-fixes without prior discussion.\n\nIf you plan to contribute new features, utility functions or extensions to this system, please first open an issue and discuss the feature with us.\n',
     'author': 'SmartNoise Team',

synth/snsynth/mst/mst.py

@@ -5,7 +5,7 @@
     from mbi import FactoredInference, Dataset, Domain
 except ImportError:
     print("Please install mbi with:\n   pip install git+https://github.com/ryan112358/private-pgm.git")
-
+    raise ImportError
 from scipy import sparse
 from disjoint_set import DisjointSet
 import networkx as nx