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‎.github/ISSUE_TEMPLATE/feature_request.md

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name: Feature request
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about: Request a new feature that you would like to see implemented in SDV
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title: ''
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labels: new feature, new
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labels: feature request, new
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assignees: ''
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---

‎.github/workflows/integration.yml

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name: Integration Tests
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on:
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- push
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- pull_request
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push:
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pull_request:
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types: [opened, reopened]
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jobs:
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unit:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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os: [ubuntu-latest, macos-10.15, windows-latest]
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os: [ubuntu-latest, macos-latest, windows-latest]
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steps:
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- uses: actions/checkout@v1
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- name: Set up Python ${{ matrix.python-version }}

‎.github/workflows/lint.yml

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name: Style Checks
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on:
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- push
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- pull_request
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push:
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pull_request:
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types: [opened, reopened]
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jobs:
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lint:

‎.github/workflows/minimum.yml

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name: Unit Tests Minimum Versions
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on:
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- push
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push:
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pull_request:
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types: [opened, reopened]
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jobs:
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minimum:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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os: [ubuntu-latest, macos-10.15, windows-latest]
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os: [ubuntu-latest, macos-latest, windows-latest]
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steps:
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- uses: actions/checkout@v1
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- name: Set up Python ${{ matrix.python-version }}

‎.github/workflows/readme.yml

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name: Test README
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on:
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pull_request:
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jobs:
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readme:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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os: [ubuntu-latest, macos-10.15] # skip windows bc rundoc fails
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os: [ubuntu-latest, macos-latest] # skip windows bc rundoc fails
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steps:
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- uses: actions/checkout@v1
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- name: Set up Python ${{ matrix.python-version }}

‎.github/workflows/tutorials.yml

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name: Run Tutorials
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on:
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jobs:
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tutorials:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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os: [ubuntu-latest, macos-10.15, windows-latest]
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os: [ubuntu-latest, macos-latest, windows-latest]
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steps:
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- uses: actions/checkout@v1
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- name: Set up Python ${{ matrix.python-version }}
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run: python -m pip install pywinpty==2.0.1
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- name: Install package and dependencies
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run: pip install invoke jupyter .
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- if: matrix.python-version != 3.6
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name: Install NBConvert
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run: pip install nbconvert==6.4.5 nbformat==5.4.0
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- name: invoke tutorials
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run: invoke tutorials

‎.github/workflows/unit.yml

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name: Unit Tests
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on:
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- push
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- pull_request
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push:
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pull_request:
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types: [opened, reopened]
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jobs:
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unit:
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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python-version: [3.6, 3.7, 3.8, 3.9]
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os: [ubuntu-latest, macos-10.15, windows-latest]
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os: [ubuntu-latest, macos-latest, windows-latest]
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steps:
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- uses: actions/checkout@v1
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- name: Set up Python ${{ matrix.python-version }}

‎HISTORY.md

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# Release Notes
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## 0.17.0 - 2022-09-09
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This release updates the code to use RDT version 1.2.0 and greater, so that those new features are now available in SDV. This changes the transformers that are available in SDV models to be those that are in RDT version 1.2.0. As a result, some arguments for initializing models have changed.
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Additionally, this release fixes bugs related to loading models with custom constraints. It also fixes a bug that added `NaNs` to the index of sampled data when using `sample_remaining_columns`.
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### Bugs Fixed
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* Incorrect rounding in Custom Constraint example - Issue [#941](https://github.com/sdv-dev/SDV/issues/941) by @amontanez24
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* Can't save the model if use the custom constraint - Issue [#928](https://github.com/sdv-dev/SDV/issues/928) by @pvk-developer
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* User Guide code fixes - Issue [#983](https://github.com/sdv-dev/SDV/issues/983) by @amontanez24
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* Index contains NaNs when using sample_remaining_columns - Issue [#985](https://github.com/sdv-dev/SDV/issues/985) by @amontanez24
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* Cannot sample after loading a model with custom constraints: TypeError - Issue [#984](https://github.com/sdv-dev/SDV/issues/984) by @pvk-developer
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* Set HyperTransformer config manually, based on Metadata if given - Issue [#982](https://github.com/sdv-dev/SDV/issues/982) by @pvk-developer
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### New Features
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* Change default metrics for evaluate - Issue [#949](https://github.com/sdv-dev/SDV/issues/949) by @fealho
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### Maintenance
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* Update the RDT version to 1.0 - Issue [#897](https://github.com/sdv-dev/SDV/issues/897) by @pvk-developer
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## 0.16.0 - 2022-07-21
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This release brings user friendly improvements and bug fixes on the `SDV` constraints, to help

‎Makefile

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invoke tutorials
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.PHONY: test
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test: test-unit test-readme test-tutorials ## test everything that needs test dependencies
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test: test-unit test-integration test-readme test-tutorials ## test everything that needs test dependencies
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.PHONY: test-all
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test-all: ## run tests on every Python version with tox

‎conda/meta.yaml

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{% set name = 'sdv' %}
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{% set version = '0.16.0' %}
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{% set version = '0.17.0.dev3' %}
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package:
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name: "{{ name|lower }}"
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- pytest-runner
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- graphviz
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- python >=3.6,<3.10
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- faker >=3.0.0,<10
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- faker >=10,<15
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- python-graphviz >=0.13.2,<1
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- numpy >=1.18.0,<2
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- pandas >=1.1.3,<2
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- tqdm >=4.15,<5
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- copulas >=0.6.0,<0.7
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- ctgan >=0.5.0,<0.6
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- copulas >=0.7.0,<0.8
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- ctgan >=0.5.2,<0.6
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- deepecho >=0.3.0.post1,<0.4
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- rdt >=0.6.1,<0.7
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- sdmetrics >=0.4.1,<0.5
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- rdt >=1.2.0,<2
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- sdmetrics >=0.6.0,<0.7
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run:
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- graphviz
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- python >=3.6,<3.10
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- faker >=3.0.0,<10
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- faker >=10,<15
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- python-graphviz >=0.13.2,<1
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- numpy >=1.18.0,<2
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- pandas >=1.1.3,<2
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- tqdm >=4.15,<5
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- copulas >=0.6.0,<0.7
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- ctgan >=0.5.0,<0.6
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- copulas >=0.7.0,<0.8
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- ctgan >=0.5.2,<0.6
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- deepecho >=0.3.0.post1,<0.4
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- rdt >=0.6.1,<0.7
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- sdmetrics >=0.4.1,<0.5
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- rdt >=1.2.0,<2
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- sdmetrics >=0.6.0,<0.7
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about:
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home: "https://sdv.dev"

‎docs/api_reference/metrics/relational.rst

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CSTest
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CSTest.get_subclasses
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CSTest.compute
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KSTest
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KSTest.get_subclasses
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KSTest.compute
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KSTestExtended
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KSTestExtended.get_subclasses
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KSTestExtended.compute
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KSComplement
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KSComplement.get_subclasses
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KSComplement.compute
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Multi Table Detection Metrics
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

‎docs/api_reference/metrics/tabular.rst

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CSTest
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CSTest.get_subclasses
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CSTest.compute
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KSTest
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KSTest.get_subclasses
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KSTest.compute
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KSTestExtended
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KSTestExtended.get_subclasses
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KSTestExtended.compute
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KSComplement
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KSComplement.get_subclasses
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KSComplement.compute
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ContinuousKLDivergence
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ContinuousKLDivergence.get_subclasses
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ContinuousKLDivergence.compute

‎docs/developer_guides/sdv/tabular.rst

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functionalities:
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* ``get_metadata``: Returns the Table metadata object that has been fitted to the data.
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* ``save``: Saves the complete Tabular Model in a file using ``pickle``.
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* ``load``: Loads a previously saved model from a ``pickle`` file.
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* ``save``: Saves the complete Tabular Model in a file using ``cloudpickle``.
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* ``load``: Loads a previously saved model from a ``cloudpickle`` file.
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Implementing a Tabular Model
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----------------------------

‎docs/user_guides/evaluation/evaluation_framework.rst

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metrics that are applied can be controlled by passing a list with the
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For example, if you were interested on obtaining only the ``CSTest`` and
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``KSTest`` metrics you can call the ``evaluate`` function as follows:
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For example, if you were interested on obtaining only the ``CSTest``
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metric you can call the ``evaluate`` function as follows:
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.. ipython:: python
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:okwarning:
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evaluate(synthetic_data, real_data, metrics=['CSTest', 'KSTest'])
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evaluate(synthetic_data, real_data, metrics=['CSTest'])
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.. ipython:: python
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:okwarning:
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evaluate(synthetic_data, real_data, metrics=['CSTest', 'KSTest'], aggregate=False)
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evaluate(synthetic_data, real_data, metrics=['CSTest'], aggregate=False)
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For more details about all the metrics that exist for the different data modalities

‎docs/user_guides/evaluation/multi_table_metrics.rst

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The list of such metrics is:
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* ``CSTest``: Multi Single Table metric based on the Single Table CSTest metric.
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* ``KSTest``: Multi Single Table metric based on the Single Table KSTest metric.
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* ``KSTestExtended``: Multi Single Table metric based on the Single Table KSTestExtended metric.
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* ``KSComplement``: Multi Single Table metric based on the Single Table KSComplement metric.
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* ``LogisticDetection``: Multi Single Table metric based on the Single Table LogisticDetection metric.
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* ``SVCDetection``: Multi Single Table metric based on the Single Table SVCDetection metric.
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* ``BNLikelihood``: Multi Single Table metric based on the Single Table BNLikelihood metric.
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* ``BNLogLikelihood``: Multi Single Table metric based on the Single Table BNLogLikelihood metric.
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Let's try to use the ``KSTestExtended`` metric:
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Let's try to use the ``KSComplement`` metric:
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.. ipython::
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:verbatim:
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In [6]: from sdv.metrics.relational import KSTestExtended
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In [6]: from sdv.metrics.relational import KSComplement
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In [7]: KSTestExtended.compute(real_data, synthetic_data)
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In [7]: KSComplement.compute(real_data, synthetic_data)
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Out[7]: 0.8194444444444443
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Parent Child Detection Metrics

‎docs/user_guides/evaluation/single_table_metrics.rst

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Such metrics are:
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* ``sdv.metrics.tabular.KSTest``: This metric uses the two-sample Kolmogorov–Smirnov test
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* ``sdv.metrics.tabular.KSComplement``: This metric uses the two-sample Kolmogorov–Smirnov test
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to compare the distributions of continuous columns using the empirical CDF.
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In [6]: from sdv.metrics.tabular import CSTest, KSComplement
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In [7]: CSTest.compute(real_data, synthetic_data)
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Out[7]: 0.8078084931103922
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In [8]: KSTest.compute(real_data, synthetic_data)
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In [8]: KSComplement.compute(real_data, synthetic_data)
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Out[8]: 0.6372093023255814
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In each case, the statistical test will be executed on all the compatible column (so, categorical
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or boolean columns for ``CSTest`` and numerical columns for ``KSTest``), and report the average
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or boolean columns for ``CSTest`` and numerical columns for ``KSComplement``), and report the average
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score obtained.
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.. note:: If your table does not contain any column of the compatible type, the output of
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In [10]: evaluate(synthetic_data, real_data, metrics=['CSTest', 'KSComplement'], aggregate=False)
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Out[10]:
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metric name raw_score normalized_score min_value max_value goal
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0 CSTest Chi-Squared 0.807808 0.807808 0.0 1.0 MAXIMIZE
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1 KSTest Inverted Kolmogorov-Smirnov D statistic 0.637209 0.637209 0.0 1.0 MAXIMIZE
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1 KSComplement Inverted Kolmogorov-Smirnov D statistic 0.637209 0.637209 0.0 1.0 MAXIMIZE
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Likelihood Metrics

‎docs/user_guides/relational/hma1.rst

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Note that the extension of the filename is not relevant, but we will be
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`pickle <https://docs.python.org/3/library/pickle.html>`__.
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`cloudpickle <https://github.com/cloudpipe/cloudpickle>`__.
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.. ipython:: python
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:okwarning:

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