From 42ffed675dd4eddd26234600912aef51a98e9ff1 Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Thu, 5 Sep 2024 11:11:03 +0200 Subject: [PATCH 01/17] Simplify CI & development experience (#316) * Improving / simplifying dev experience - only use ruff for linting, which is now in a really good state to replace pydocstyle, flake8, pylint, etc - separate github actions - separate dependencies into test, check, extra, docs, and dev (which is everything) - process: create changes, run ruff check, run mypy, run pytest, and you're good to commit! * Update actions titles & setup-python version There's a deprecation warning in setup-python@v4 * add lint and type check on PR matrix before test * another round of simplification but now using jobs * Allow all checks to run in parallel * split dev and scheduled CI * add pylint rules back in through ruff pyproject.toml * Add all pylint rules, move dev status to beta * Add pydocstyle checks * Update contributing guide for new dev flow Also update sphinx requirement in pyproject.toml * simplify doc build command * Make sphinx-build happy * no separate requirements.txt for docs * Fix weird formatting * Remove blank lines at the end of docstrings --- .github/workflows/core-ci.yml | 80 +++++++++++++++ .github/workflows/joss-paper-draft.yml | 2 +- .github/workflows/python-publish.yml | 9 +- .github/workflows/python-test.yml | 69 ------------- .github/workflows/release-dockerhub.yml | 2 +- .github/workflows/scheduled-ci.yml | 71 +++++++++++++ .readthedocs.yaml | 15 ++- docs/requirements.txt | 6 -- docs/source/conf.py | 38 ++++--- docs/source/developer/contributing.rst | 13 ++- examples/example_gmf_titanic.json | 4 +- examples/getting_started.ipynb | 7 +- metasyn/__main__.py | 2 +- metasyn/config.py | 2 +- metasyn/demo/dataset.py | 3 +- metasyn/distribution/__init__.py | 2 +- metasyn/distribution/base.py | 9 +- metasyn/distribution/continuous.py | 2 +- metasyn/distribution/datetime.py | 2 +- metasyn/distribution/na.py | 4 +- metasyn/distribution/string.py | 11 +- metasyn/metaframe.py | 4 +- metasyn/provider.py | 2 +- metasyn/validation.py | 2 +- metasyn/var.py | 4 +- pyproject.toml | 131 +++--------------------- 26 files changed, 240 insertions(+), 256 deletions(-) create mode 100644 .github/workflows/core-ci.yml delete mode 100644 .github/workflows/python-test.yml create mode 100644 .github/workflows/scheduled-ci.yml delete mode 100644 docs/requirements.txt diff --git a/.github/workflows/core-ci.yml b/.github/workflows/core-ci.yml new file mode 100644 index 00000000..c56a0fb1 --- /dev/null +++ b/.github/workflows/core-ci.yml @@ -0,0 +1,80 @@ +name: Continuous integration for development + +on: + pull_request: + branches: [ main, develop ] + +jobs: + check: + name: Lint and check types + strategy: + fail-fast: true + matrix: + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + runs-on: "ubuntu-latest" + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install ".[check]" + - name: Lint with Ruff + run: ruff check + - name: Check types with MyPy + run: mypy metasyn + + test: + name: Run test suite + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + include: + - os: macos-latest + python-version: "3.x" + - os: windows-latest + python-version: "3.x" + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install ".[test,extra]" + - name: Test with pytest + run: pytest tests + - name: Check notebook output + if: ${{ matrix.os != 'macos-latest' }} + run: pytest --nbval-lax examples + - name: Test basic example + run: python examples/basic_example.py + - name: Test CLI configuration example + run: | + pip install git+https://github.com/sodascience/metasyn-disclosure-control + pip install . + metasyn create-meta metasyn/demo/demo_titanic.csv --config examples/example_config.toml + + build-docs: + name: Build documentation + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install ".[docs]" + - name: Build docs + run: sphinx-build -M html docs/source docs/build diff --git a/.github/workflows/joss-paper-draft.yml b/.github/workflows/joss-paper-draft.yml index d8713456..3e4e089a 100644 --- a/.github/workflows/joss-paper-draft.yml +++ b/.github/workflows/joss-paper-draft.yml @@ -1,4 +1,4 @@ -name: Draft PDF +name: Build paper draft PDF on: push: paths: diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index db9f278b..43e20bd7 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -1,11 +1,6 @@ # This workflow will upload a Python Package using Twine when a release is created # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries -# This workflow uses actions that are not certified by GitHub. -# They are provided by a third-party and are governed by -# separate terms of service, privacy policy, and support -# documentation. - name: Upload Python Package on: @@ -17,13 +12,11 @@ permissions: jobs: deploy: - runs-on: ubuntu-latest - steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v4 + uses: actions/setup-python@v5 with: python-version: '3.x' - name: Install dependencies diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml deleted file mode 100644 index 38f0dd0e..00000000 --- a/.github/workflows/python-test.yml +++ /dev/null @@ -1,69 +0,0 @@ -# This workflow will install Python dependencies, run tests and lint with a variety of Python versions -# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions - -name: Lint, test, documentation, plugins - -on: - push: - branches: [ main ] - pull_request: - branches: [ develop ] - schedule: - - cron: "0 0 * * 0" - -jobs: - build: - strategy: - fail-fast: false - matrix: - os: [ubuntu-latest] - python-version: [3.8, 3.9, "3.10", "3.11", "3.12"] - include: - - os: macos-latest - python-version: "3.11" - - os: windows-latest - python-version: "3.11" - runs-on: ${{ matrix.os }} - - steps: - - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install ".[test]" - - name: Lint with pylint - run: | - pylint metasyn - - name: Lint with Ruff - run: | - ruff check metasyn - - name: Check docstrings with pydocstyle - run: | - pydocstyle metasyn --convention=numpy --add-select=D417 --add-ignore="D102,D105" - - name: Check types with MyPy - run: | - mypy metasyn - - name: Check if documentation builds. - run: | - cd docs; make html SPHINXOPTS="--keep-going" - - name: Test with pytest - run: | - pytest tests - - name: Check notebook output - if: ${{ matrix.os != 'macos-latest' }} - run: | - pytest --nbval-lax examples - - - name: Test basic example - run: | - python examples/basic_example.py - - - name: Test configuration example - run: | - pip install git+https://github.com/sodascience/metasyn-disclosure-control - pip install . - metasyn create-meta metasyn/demo/demo_titanic.csv --config examples/example_config.toml diff --git a/.github/workflows/release-dockerhub.yml b/.github/workflows/release-dockerhub.yml index 6fb97041..4839989e 100644 --- a/.github/workflows/release-dockerhub.yml +++ b/.github/workflows/release-dockerhub.yml @@ -1,4 +1,4 @@ -name: Release Dockerhub +name: Release dockerfile to Dockerhub on: push: diff --git a/.github/workflows/scheduled-ci.yml b/.github/workflows/scheduled-ci.yml new file mode 100644 index 00000000..24682482 --- /dev/null +++ b/.github/workflows/scheduled-ci.yml @@ -0,0 +1,71 @@ +name: Scheduled CI suite on main and dev branches + +on: + schedule: + - cron: "0 0 * * 0" + workflow_dispatch: # also allow manual trigger + +jobs: + check: + name: Lint and check types + strategy: + fail-fast: true + matrix: + branch: ["main", "develop"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + runs-on: "ubuntu-latest" + steps: + - uses: actions/checkout@v4 + with: + ref: ${{ matrix.branch }} + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install ".[check]" + - name: Lint with Ruff + run: ruff check + - name: Check types with MyPy + run: mypy metasyn + + test: + name: Run test suite + strategy: + fail-fast: false + matrix: + branch: ["main", "develop"] + os: [ubuntu-latest] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + include: + - os: macos-latest + python-version: "3.x" + - os: windows-latest + python-version: "3.x" + runs-on: ${{ matrix.os }} + steps: + - uses: actions/checkout@v4 + with: + ref: ${{ matrix.branch }} + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install ".[test,extra]" + - name: Test with pytest + run: pytest tests + - name: Check notebook output + if: ${{ matrix.os != 'macos-latest' }} + run: pytest --nbval-lax examples + - name: Test basic example + run: python examples/basic_example.py + - name: Test CLI configuration example + run: | + pip install git+https://github.com/sodascience/metasyn-disclosure-control + pip install . + metasyn create-meta metasyn/demo/demo_titanic.csv --config examples/example_config.toml diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 69882aeb..ae74fffc 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,18 +10,15 @@ build: os: ubuntu-20.04 tools: python: "3.9" - # You can also specify other tool versions: - # nodejs: "16" - # rust: "1.55" - # golang: "1.17" # Build documentation in the docs/ directory with Sphinx sphinx: - configuration: docs/source/conf.py + configuration: docs/source/conf.py # Optionally declare the Python requirements required to build your docs python: - install: - - requirements: docs/requirements.txt - - method: pip - path: . + install: + - method: pip + path: . + extra_requirements: + - docs \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index cd9da8f4..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -sphinx<7.0.0 -sphinx_inline_tabs -sphinx-rtd-theme -sphinxcontrib-napoleon -sphinx-autodoc-typehints -sphinx_copybutton diff --git a/docs/source/conf.py b/docs/source/conf.py index b1575295..fbb26e8b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,3 +1,4 @@ +# ruff: noqa # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full @@ -12,31 +13,37 @@ # import os import sys -import sphinx_rtd_theme -sys.path.insert(0, os.path.abspath('..')) + +sys.path.insert(0, os.path.abspath("..")) # -- Project information ----------------------------------------------------- -project = 'metasyn' -copyright = '2024, SODA Team' -author = 'SODA Team' +project = "metasyn" +copyright = "2024, SODA Team" +author = "SODA Team" # The full version, including alpha/beta/rc tags release = '1.0.3' - # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. -extensions = ['sphinx.ext.napoleon', "sphinx.ext.autodoc", "sphinx_autodoc_typehints", - 'sphinx_rtd_theme', "sphinx_inline_tabs", "sphinx_copybutton", "sphinx.ext.autosummary"] +extensions = [ + "sphinx.ext.napoleon", + "sphinx.ext.autodoc", + "sphinx_autodoc_typehints", + "sphinx_rtd_theme", + "sphinx_inline_tabs", + "sphinx_copybutton", + "sphinx.ext.autosummary", +] # Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] +templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. @@ -49,14 +56,13 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # -html_theme = 'sphinx_rtd_theme' -html_logo = 'images/logos/white_med.png' +html_theme = "sphinx_rtd_theme" +html_logo = "images/logos/white_med.png" html_theme_options = { - 'logo_only': True, - 'navigation_depth': -1, - 'display_version': True, - 'style_external_links': True, - + "logo_only": True, + "navigation_depth": -1, + "display_version": True, + "style_external_links": True, } # Add any paths that contain custom static files (such as style sheets) here, diff --git a/docs/source/developer/contributing.rst b/docs/source/developer/contributing.rst index 0116d1b7..cb3bbbc0 100644 --- a/docs/source/developer/contributing.rst +++ b/docs/source/developer/contributing.rst @@ -15,6 +15,7 @@ If you encounter a bug or have a feature request, you can report it in the `issu :alt: GitHub Issue Tracker Button :target: https://github.com/sodascience/metasyn/issues + Contribute directly to the code or documentation: -------------------------------------------------- You can contribute to the project by writing code to fix bugs, implement new features, or improve existing functionality. Alternatively, you can help improve the documentation by fixing errors, adding examples, or clarifying instructions. @@ -53,11 +54,17 @@ The following shows a basic overview of the steps needed to do so. Running local tests ------------------- -When a pull request is created, GitHub automatically runs `a series of tests `_ on the code to ensure it meets the projects standards and does not introduce any errors. You can run these tests locally to ensure your code passes before opening a pull request, using the `Tox `_ package. - -To do so, first install Tox to your environment following the `Tox installation guide `_. Then simply run the ``tox`` on the ``metasyn`` root directory to run all the tests. If you want to run a specific test, you can do so by specifying a (list of) test environment(s), e.g. ``tox -e ruff`` or ``tox -e ruff,pylint,pydocstyle,mypy,sphinx,pytest,nbval``. The available test environments can be found in the `pyproject.toml `_ file, under the ``[tool.tox]`` section. +When a pull request is created, GitHub automatically runs `a series of tests `_ on the code to ensure it meets the projects standards and does not introduce any errors. You can run these tests locally to ensure your code passes before opening a pull request as follows: +1. Make your changes +2. From the root folder of the project, run: ``pip install -e .[dev]`` (this will install metasyn in editable form with all optional dependencies) +3. Then, run: + a. Linting: ``ruff check`` + b. Type checking: ``mypy metasyn`` + c. Testing: ``pytest`` + d. Notebook testing: ``pytest --nbval-lax examples`` + e. Documentation building: ``sphinx-build -M html docs/source docs/build`` Maintaining the package diff --git a/examples/example_gmf_titanic.json b/examples/example_gmf_titanic.json index 7981bfcc..4a9f370c 100644 --- a/examples/example_gmf_titanic.json +++ b/examples/example_gmf_titanic.json @@ -4,9 +4,9 @@ "provenance": { "created by": { "name": "metasyn", - "version": "1.0.1" + "version": "1.0.2.dev14+gc102905.d20240830" }, - "creation time": "2024-07-11T12:03:55.877693" + "creation time": "2024-09-04T14:45:48.696499" }, "vars": [ { diff --git a/examples/getting_started.ipynb b/examples/getting_started.ipynb index 4489256d..f05fa9aa 100644 --- a/examples/getting_started.ipynb +++ b/examples/getting_started.ipynb @@ -64,7 +64,8 @@ "source": [ "# import required packages\n", "import polars as pl\n", - "from metasyn import MetaFrame, demo_file, VarSpec" + "\n", + "from metasyn import MetaFrame, VarSpec, demo_file" ] }, { @@ -297,7 +298,7 @@ } ], "source": [ - "# Generate and fit a MetaFrame to the DataFrame \n", + "# Generate and fit a MetaFrame to the DataFrame\n", "mf = MetaFrame.fit_dataframe(df)" ] }, @@ -1679,7 +1680,7 @@ " VarSpec(\"Cabin\", distribution=cabin_distribution, description=\"The cabin number of the passenger.\"), # Use our previously defined distribution\n", "]\n", "\n", - "mf = MetaFrame.fit_dataframe(df, var_specs=var_specs) " + "mf = MetaFrame.fit_dataframe(df, var_specs=var_specs)" ] }, { diff --git a/metasyn/__main__.py b/metasyn/__main__.py index 864cd27e..3e7662e5 100644 --- a/metasyn/__main__.py +++ b/metasyn/__main__.py @@ -21,7 +21,7 @@ from metasyn.config import MetaConfig from metasyn.validation import create_schema -EXAMPLE_CREATE_META="metasyn create-meta your_dataset.csv -o your_gmf_file.json --config your_config.toml" # noqa # pylint: disable=line-too-long +EXAMPLE_CREATE_META="metasyn create-meta your_dataset.csv -o your_gmf_file.json --config your_config.toml" # noqa: E501 EXAMPLE_SYNTHESIZE="metasyn synthesize your_gmf_file.json -o your_synthetic_file.csv" MAIN_HELP_MESSAGE = f""" diff --git a/metasyn/config.py b/metasyn/config.py index b22aa6e2..30d86cb7 100644 --- a/metasyn/config.py +++ b/metasyn/config.py @@ -177,7 +177,7 @@ def iter_var(self, exclude: Optional[list[str]] = None) -> Iterable[VarSpecAcces yield VarSpecAccess(var_spec, self) -class VarSpecAccess(): # pylint: disable=too-few-public-methods +class VarSpecAccess(): """Access for variable configuration object. They take into account what the defaults are from the MetaConfig object. diff --git a/metasyn/demo/dataset.py b/metasyn/demo/dataset.py index cb203071..58bea1d6 100644 --- a/metasyn/demo/dataset.py +++ b/metasyn/demo/dataset.py @@ -76,6 +76,7 @@ def demo_file(name: str = "titanic") -> Path: - spaceship (CC-BY from https://www.kaggle.com/competitions/spaceship-titanic) - fruit (very basic example data from Polars) - survey (columns from ESS round 11 Human Values Scale questionnaire for the Netherlands) + Arguments --------- name: @@ -90,7 +91,6 @@ def demo_file(name: str = "titanic") -> Path: European Social Survey European Research Infrastructure (ESS ERIC). (2024). ESS11 integrated file, edition 1.0 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. https://doi.org/10.21338/ess11e01_0 - """ if name == "titanic": return files(__package__) / "demo_titanic.csv" @@ -129,7 +129,6 @@ def demo_dataframe(name: str = "titanic") -> pl.DataFrame: European Social Survey European Research Infrastructure (ESS ERIC). (2024). ESS11 integrated file, edition 1.0 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. https://doi.org/10.21338/ess11e01_0 - """ file_path = demo_file(name=name) if name == "spaceship": diff --git a/metasyn/distribution/__init__.py b/metasyn/distribution/__init__.py index bc881446..cea1ac65 100644 --- a/metasyn/distribution/__init__.py +++ b/metasyn/distribution/__init__.py @@ -8,7 +8,7 @@ distribution. Each distribution class also provides a way to calculate the information criterion, used for selecting the best distribution for a given set of values. -""" # pylint: disable=invalid-name +""" from metasyn.distribution.categorical import MultinoulliDistribution from metasyn.distribution.continuous import ( diff --git a/metasyn/distribution/base.py b/metasyn/distribution/base.py index feb9506b..49a5dc5e 100644 --- a/metasyn/distribution/base.py +++ b/metasyn/distribution/base.py @@ -1,5 +1,4 @@ -""" -Module serving as the basis for all metasyn distributions. +"""Module serving as the basis for all metasyn distributions. The base module contains the ``BaseDistribution`` class, which is the base class for all distributions. @@ -54,7 +53,7 @@ class BaseDistribution(ABC): """Version of the implemented distribution""" @classmethod - def fit(cls, series: Union[pl.Series, npt.NDArray], + def fit(cls, series: Union[pl.Series, npt.NDArray], # noqa: D417 *args, **kwargs) -> BaseDistribution: """Fit the distribution to the series. @@ -158,7 +157,7 @@ def from_dict(cls, dist_dict: dict) -> BaseDistribution: """Create a distribution from a dictionary.""" return cls(**dist_dict["parameters"]) - def information_criterion(self, values: Union[pl.Series, npt.NDArray]) -> float: # pylint: disable=unused-argument + def information_criterion(self, values: Union[pl.Series, npt.NDArray]) -> float: # noqa: ARG002 """Get the BIC value for a particular set of values. Parameters @@ -350,7 +349,7 @@ def draw(self) -> object: n_retry += 1 raise ValueError(f"Failed to draw unique string after {n_retry} tries.") - def information_criterion(self, values): + def information_criterion(self, values): # noqa: ARG002 return 9999999 diff --git a/metasyn/distribution/continuous.py b/metasyn/distribution/continuous.py index 8ee3cf0a..a5ea4d3d 100644 --- a/metasyn/distribution/continuous.py +++ b/metasyn/distribution/continuous.py @@ -120,7 +120,7 @@ class LogNormalDistribution(ScipyDistribution): dist_class = lognorm - def __init__(self, mean: float, sd: float): # pylint: disable=invalid-name + def __init__(self, mean: float, sd: float): self.par = {"mean": mean, "sd": sd} self.dist = lognorm(s=max(sd, 1e-8), scale=np.exp(mean)) diff --git a/metasyn/distribution/datetime.py b/metasyn/distribution/datetime.py index 93f38d4e..b885ec1c 100644 --- a/metasyn/distribution/datetime.py +++ b/metasyn/distribution/datetime.py @@ -97,7 +97,7 @@ def minimum_delta(self) -> dt.timedelta: """Get the minimum time delta.""" return dt.timedelta(**{self.precision: 1}) - def information_criterion(self, values): + def information_criterion(self, values): # noqa: ARG002 return 0.0 def _param_dict(self): diff --git a/metasyn/distribution/na.py b/metasyn/distribution/na.py index a5dfd3d3..05411546 100644 --- a/metasyn/distribution/na.py +++ b/metasyn/distribution/na.py @@ -14,7 +14,7 @@ class NADistribution(BaseDistribution): """Distribution that always returns NA values (None).""" @classmethod - def _fit(cls, values: pl.Series) -> BaseDistribution: + def _fit(cls, values: pl.Series) -> BaseDistribution: # noqa: ARG003 return cls() @classmethod @@ -31,5 +31,5 @@ def _param_dict(self): def _param_schema(cls): return {} - def information_criterion(self, values): # pylint: disable=unused-argument + def information_criterion(self, values): # noqa: ARG002 return 1e10 diff --git a/metasyn/distribution/string.py b/metasyn/distribution/string.py index bd44de3c..d0372221 100644 --- a/metasyn/distribution/string.py +++ b/metasyn/distribution/string.py @@ -6,7 +6,7 @@ # from lingua._constant import LETTERS, PUNCTUATION import regex from faker import Faker -from lingua import LanguageDetectorBuilder # pylint: disable=no-name-in-module +from lingua import LanguageDetectorBuilder from regexmodel import NotFittedError, RegexModel from scipy.stats import poisson @@ -40,6 +40,7 @@ class FakerDistribution(BaseDistribution): -------- >>> FakerDistribution(faker_type="city", locale="en_US") >>> FakerDistribution(faker_type="address", locale="nl_NL") + """ def __init__(self, faker_type: str, locale: str = "en_US"): @@ -48,15 +49,14 @@ def __init__(self, faker_type: str, locale: str = "en_US"): self.fake: Faker = Faker(locale=locale) @classmethod - def _fit(cls, values, faker_type: str = "city", locale: str = "en_US"): \ - # pylint: disable=arguments-differ + def _fit(cls, values, faker_type: str = "city", locale: str = "en_US"): # noqa: ARG003 """Select the appropriate faker function and locale.""" return cls(faker_type, locale) def draw(self): return getattr(self.fake, self.faker_type)() - def information_criterion(self, values: Iterable) -> float: + def information_criterion(self, values: Iterable) -> float: # noqa: ARG002 return 99999 @classmethod @@ -102,6 +102,7 @@ class FreeTextDistribution(BaseDistribution): if None do not make sentences. avg_words: Average number of words per (non-NA) row. + """ def __init__(self, locale: str, avg_sentences: Optional[float], avg_words: float): @@ -146,6 +147,7 @@ def detect_language(cls, values: Iterable) -> Optional[str]: ------- language: Two letter ISO code to represent the language, or None if it could not be determined. + """ detector = LanguageDetectorBuilder.from_all_languages().with_low_accuracy_mode().build() lang = detector.detect_language_of("\n".join(values)) @@ -209,6 +211,7 @@ class StringConstantDistribution(BaseConstantDistribution): Examples -------- >>> ConstantDistribution("some_string") + """ @classmethod diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index 959f34f4..9fb9ceae 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -1,4 +1,4 @@ -"""Module defining MetaFrames, used for creating MetaFrames from DataFrames.""" # pylint: disable=invalid-name +"""Module defining MetaFrames, used for creating MetaFrames from DataFrames.""" from __future__ import annotations @@ -57,7 +57,7 @@ def n_columns(self) -> int: return len(self.meta_vars) @classmethod - def fit_dataframe( # pylint: disable=too-many-branches + def fit_dataframe( # noqa: PLR0912 cls, df: Optional[pl.DataFrame], var_specs: Optional[Union[list[VarSpec], pathlib.Path, str, MetaConfig]] = None, diff --git a/metasyn/provider.py b/metasyn/provider.py index 69a63a4a..a3d39411 100644 --- a/metasyn/provider.py +++ b/metasyn/provider.py @@ -296,7 +296,7 @@ def _find_best_fit(self, series: pl.Series, var_type: str, return dist_instances[np.argmin(dist_bic)] - def find_distribution(self, # pylint: disable=too-many-branches + def find_distribution(self, dist_name: str, var_type: str, privacy: Optional[BasePrivacy] = BasicPrivacy(), diff --git a/metasyn/validation.py b/metasyn/validation.py index 5a6c629b..4efa68b5 100644 --- a/metasyn/validation.py +++ b/metasyn/validation.py @@ -19,7 +19,7 @@ SCHEMA_BASE = { "$schema": "https://json-schema.org/draft/2020-12/schema", - "$id": "http://sodascience.github.io/generative_metadata_format/core/1.0.0/generative_metadata_format", # noqa # pylint: disable=line-too-long + "$id": "http://sodascience.github.io/generative_metadata_format/core/1.0.0/generative_metadata_format", # noqa: E501 "type": "object", "properties": { "n_rows": {"type": "number"}, diff --git a/metasyn/var.py b/metasyn/var.py index c2338d1c..9ba6e8dd 100644 --- a/metasyn/var.py +++ b/metasyn/var.py @@ -50,8 +50,8 @@ class MetaVar: it will be assumed to have been created by the user. """ - def __init__( - self, # pylint: disable=too-many-arguments + def __init__( # noqa: PLR0913 + self, name: str, var_type: str, distribution: BaseDistribution, diff --git a/pyproject.toml b/pyproject.toml index e9449a2f..76e4a6ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", - "Development Status :: 3 - Alpha", + "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", ] @@ -48,11 +48,15 @@ GitHub = "https://github.com/sodascience/metasyn" documentation = "https://metasyn.readthedocs.io/en/latest/index.html" [project.optional-dependencies] -test = [ - "ruff", "pytest", "pylint", "pydocstyle", "mypy", "flake8", "nbval", - "sphinx", "sphinx-rtd-theme", "sphinxcontrib-napoleon", +extra = ["xlsxwriter", "pandas"] +check = ["ruff", "mypy", "types-tqdm", "types-regex"] +test = ["pytest", "nbval"] +docs = [ + "sphinx<7.0.0", "sphinx-rtd-theme", "sphinxcontrib-napoleon", "sphinx-autodoc-typehints", "sphinx_inline_tabs", "sphinx_copybutton", - "XlsxWriter", "types-tqdm", "types-regex", "pandas" +] +dev = [ + "metasyn[extra,check,test,docs]" ] [project.scripts] @@ -85,119 +89,18 @@ module = [ ] ignore_missing_imports = true -[tool.pylint.'MASTER'] -ignore-patterns="_version.py" -ignore-paths = "^metasyn/distribution/legacy.*$" - -[tool.pylint.'FORMAT'] -max-line-length=100 -max-locals=35 -max-args=10 - -[tool.pylint.'BASIC'] -good-names=["a", "b", "mu"] - [tool.ruff] -exclude = ["_version.py", "metasyn/distribution/legacy.*"] +exclude = ["_version.py", "metasyn/distribution/legacy.*", "tests/*", "examples/*"] line-length = 100 -lint.select = ["E", "F", "W", "I", "N"] +lint.select = ["E", "F", "W", "I", "N", "D", "PL", "ARG"] +lint.ignore = ["PLR2004", "D107", "D105", "D102"] [tool.ruff.lint.pep8-naming] ignore-names = ["a", "b", "mu"] -[tool.tox] -legacy_tox_ini = """ - [tox] - env_list = - py311 - ruff - pylint - pydocstyle - mypy - sphinx - pytest - nbval - minversion = 4.11.3 - - [testenv] - description = run the tests with pytest - package = wheel - wheel_build_env = .pkg - deps = - pandas - polars>=0.14.17 - numpy>=1.20 - pyarrow - scipy - numpy>=1.20 - faker - lingua-language-detector - scikit-learn - jsonschema - importlib-metadata;python_version<'3.10' - importlib-resources;python_version<'3.9' - wget - regexmodel>=0.2.1 - - [testenv:ruff] - description = Lint with Ruff - deps = - ruff - commands = - ruff metasyn - - [testenv:pylint] - description = Lint with PyLint - deps = - pylint - commands = - pylint metasyn - - [testenv:pydocstyle] - description = Check docstrings with pydocstyle - deps = - pydocstyle - commands = - pydocstyle metasyn --convention=numpy --add-select=D417 --add-ignore="D102,D105" - - [testenv:mypy] - description = Check types with MyPy - deps = - mypy - types-tqdm - types-regex - commands = - mypy metasyn - - [testenv:sphinx] - description = Check if documentation builds. - allowlist_externals = - make - deps = - sphinx - sphinx-rtd-theme - sphinxcontrib-napoleon - sphinx-autodoc-typehints - sphinx_inline_tabs - sphinx_copybutton - changedir = docs - commands = - make html SPHINXOPTS="-W --keep-going" - - [testenv:pytest] - description = Test with pytest - deps = - pytest - XlsxWriter - commands = - pytest tests +[tool.ruff.lint.pylint] +max-args=10 +max-locals=35 - [testenv:nbval] - description = Check notebook output - deps = - pytest - nbval - XlsxWriter - commands = - pytest --nbval-lax examples -""" \ No newline at end of file +[tool.ruff.lint.pydocstyle] +convention="numpy" \ No newline at end of file From 93f7ab0db395cd4b56f5892d3b839452ba62f96e Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Mon, 9 Sep 2024 20:31:49 +0200 Subject: [PATCH 02/17] Improved demo datasets (#325) Adds more tests, and fixes several typing issues. Now, all data types should be correctly stored and be reusable. --- metasyn/demo/dataset.py | 253 +++++++++++++++++++++---------- metasyn/demo/demo_test.csv | 101 ++++++++++++ metasyn/distribution/discrete.py | 4 +- metasyn/metaframe.py | 2 + metasyn/var.py | 13 +- tests/test_dataset.py | 29 ++++ tests/test_demo.py | 33 ---- tests/test_var.py | 34 +++++ 8 files changed, 350 insertions(+), 119 deletions(-) create mode 100644 metasyn/demo/demo_test.csv delete mode 100644 tests/test_demo.py diff --git a/metasyn/demo/dataset.py b/metasyn/demo/dataset.py index 58bea1d6..aaa0524b 100644 --- a/metasyn/demo/dataset.py +++ b/metasyn/demo/dataset.py @@ -1,71 +1,195 @@ """Create and retrieve demo datasets.""" # import random +import string +from abc import ABC, abstractproperty +from datetime import date, datetime, time, timedelta from pathlib import Path +import numpy as np import polars as pl +from metasyn.varspec import VarSpec + try: from importlib_resources import files except ImportError: from importlib.resources import files # type: ignore -# import numpy as np -# import wget +_AVAILABLE_DATASETS = {} + + +def register(*args): + """Register a dataset so that it can be found by name.""" + def _wrap(cls): + _AVAILABLE_DATASETS[cls().name] = cls() + return cls + return _wrap(*args) + -# from metasyn.distribution.datetime import ( -# DateTimeUniformDistribution, -# DateUniformDistribution, -# TimeUniformDistribution, -# ) +class BaseDataset(ABC): + """Base class for demo datasets.""" + @abstractproperty + def name(self): + pass -# def create_titanic_demo(output_fp: Path) -> Path: -# """Create demo dataset for the titanic dataset. + @property + def file_location(self): + return files(__package__) / f"demo_{self.name}.csv" -# Arguments -# --------- -# output_fp: -# File to write the demonstration table to. + def get_dataframe(self): + return pl.read_csv(self.file_location, schema_overrides=self.schema, + try_parse_dates=True) -# Returns -# ------- -# Output file location. -# """ -# titanic_fp = Path("titanic.csv") -# if Path(output_fp).is_file(): -# return output_fp -# if not titanic_fp.is_file(): -# wget.download( -# "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv") -# dframe = pd.read_csv(titanic_fp) -# np.random.seed(1283742) -# random.seed(1928374) + @abstractproperty + def schema(self): + pass -# # Convert Age to a nullable integer. -# dframe["Age"] = dframe["Age"].round().astype("Int64") + @property + def var_specs(self): + return [] -# # Add a date column. -# date_dist = DateUniformDistribution.default_distribution() -# dframe["Birthday"] = [date_dist.draw() if np.random.rand() < 0.9 else pd.NA -# for _ in range(len(dframe))] -# # Add a time column. +@register +class TitanicDataset(BaseDataset): + """Included in pandas, but post-processed to contain more columns.""" -# time_dist = TimeUniformDistribution.default_distribution() -# dframe["Board time"] = [time_dist.draw() if np.random.rand() < 0.9 else pd.NA -# for _ in range(len(dframe))] + @property + def name(self): + return "titanic" -# # Add a datetime column -# time_dist = DateTimeUniformDistribution.default_distribution() -# dframe["Married since"] = [time_dist.draw() if np.random.rand() < 0.9 else pd.NA -# for _ in range(len(dframe))] + @property + def schema(self): + return {"Sex": pl.Categorical, "Embarked": pl.Categorical} -# dframe["all_NA"] = [pd.NA for _ in range(len(dframe))] -# # Remove some columns for brevity and write to a file. -# dframe = dframe.drop(["SibSp", "Pclass", "Survived"], axis=1) -# dframe.to_csv(output_fp, index=False) -# return output_fp + @property + def var_specs(self): + return [VarSpec("PassengerId", unique=True)] + +@register +class SpaceShipDataset(BaseDataset): + """CC-BY from https://www.kaggle.com/competitions/spaceship-titanic.""" + + @property + def name(self): + return "spaceship" + + @property + def schema(self): + return { + "HomePlanet": pl.Categorical, + "CryoSleep": pl.Categorical, + "VIP": pl.Categorical, + "Destination": pl.Categorical, + "Transported": pl.Categorical, + } + + +@register +class FruitDataset(BaseDataset): + """Very basic example data from Polars.""" + + @property + def name(self): + return "fruit" + + @property + def schema(self): + return {"fruits": pl.Categorical, "cars": pl.Categorical} + + @property + def var_specs(self): + return [VarSpec("ID", unique=True), VarSpec("B", unique=False)] + + +@register +class SurveyDataset(BaseDataset): + """Columns from ESS round 11 Human Values Scale questionnaire for the Netherlands.""" + + @property + def name(self): + return "survey" + + @property + def schema(self): + return {} + + +@register +class TestDataset(BaseDataset): + """Test dataset with all supported data types.""" + + @property + def name(self): + return "test" + + @property + def schema(self): + columns = pl.read_csv(self.file_location).columns + return {col_name: (getattr(pl, col_name[3:]) if col_name != "NA" else pl.String) + for col_name in columns} + + @classmethod + def create(cls, csv_file): + all_series = [] + n_rows = 100 + + for int_val in [8, 16, 32, 64]: + all_series.append(pl.Series(f"pl.Int{int_val}", + [np.random.randint(-10, 10) for _ in range(n_rows)], + dtype=getattr(pl, f"Int{int_val}"))) + all_series.append(pl.Series(f"pl.UInt{int_val}", + [np.random.randint(10) for _ in range(n_rows)], + dtype=getattr(pl, f"UInt{int_val}"))) + + for float_val in [32, 64]: + all_series.append(pl.Series(f"pl.Float{float_val}", + np.random.randn(n_rows), + dtype=getattr(pl, f"Float{float_val}"))) + + all_series.append(pl.Series("pl.Date", [date(2024, 9, 4) + timedelta(days=i) + for i in range(n_rows)], + dtype=pl.Date)) + all_series.append(pl.Series("pl.Datetime", + [datetime(2024, 9, 4, 12, 30, 12) + + timedelta(hours=i, minutes=i*2, seconds=i*3) + for i in range(n_rows)], + dtype=pl.Datetime)) + all_series.append(pl.Series("pl.Time", + [time(3+i//20, 6+i//12, 12+i//35) for i in range(n_rows)], + dtype=pl.Time)) + all_series.append(pl.Series("pl.String", + np.random.choice(list(string.printable), size=n_rows), + dtype=pl.String)) + all_series.append(pl.Series("pl.Utf8", + np.random.choice(list(string.printable), size=n_rows), + dtype=pl.Utf8)) + all_series.append(pl.Series("pl.Categorical", + np.random.choice(list(string.ascii_uppercase[:5]), size=n_rows), + dtype=pl.Categorical)) + all_series.append(pl.Series("pl.Boolean", + np.random.choice([True, False], size=n_rows), + dtype=pl.Boolean)) + all_series.append(pl.Series("NA", [None for _ in range(n_rows)], dtype=pl.String)) + + # Add NA's for all series except the categorical + for series in all_series: + if series.name != "pl.Categorical": + none_idx = np.random.choice(np.arange(n_rows), size=n_rows//10, replace=False) + none_idx.sort() + series[none_idx] = None + + # Write to a csv file + pl.DataFrame(all_series).write_csv(csv_file) + + +def _get_demo_class(name): + if name in _AVAILABLE_DATASETS: + return _AVAILABLE_DATASETS[name] + raise ValueError( + f"No demonstration dataset with name '{name}'. Options: {list(_AVAILABLE_DATASETS)}." + ) def demo_file(name: str = "titanic") -> Path: @@ -92,18 +216,7 @@ def demo_file(name: str = "titanic") -> Path: file, edition 1.0 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. https://doi.org/10.21338/ess11e01_0 """ - if name == "titanic": - return files(__package__) / "demo_titanic.csv" - if name == "spaceship": - return files(__package__) / "demo_spaceship.csv" - if name == "fruit": - return files(__package__) / "demo_fruit.csv" - if name == "survey": - return files(__package__) / "demo_survey.csv" - - raise ValueError( - f"No demonstration dataset with name '{name}'. Options: titanic, spaceship, fruit, survey." - ) + return _get_demo_class(name).file_location def demo_dataframe(name: str = "titanic") -> pl.DataFrame: @@ -130,28 +243,4 @@ def demo_dataframe(name: str = "titanic") -> pl.DataFrame: file, edition 1.0 [Data set]. Sikt - Norwegian Agency for Shared Services in Education and Research. https://doi.org/10.21338/ess11e01_0 """ - file_path = demo_file(name=name) - if name == "spaceship": - # the Kaggle spaceship data (CC-BY) - data_types = { - "HomePlanet": pl.Categorical, - "CryoSleep": pl.Categorical, - "VIP": pl.Categorical, - "Destination": pl.Categorical, - "Transported": pl.Categorical, - } - return pl.read_csv(file_path, schema_overrides=data_types, try_parse_dates=True) - if name == "titanic": - # our edited titanic data - data_types = {"Sex": pl.Categorical, "Embarked": pl.Categorical} - return pl.read_csv(file_path, schema_overrides=data_types, try_parse_dates=True) - if name == "fruit": - # basic fruit data from polars example - data_types = {"fruits": pl.Categorical, "cars": pl.Categorical} - return pl.read_csv(file_path, schema_overrides=data_types) - if name == "survey": - return pl.read_csv(file_path) - - raise ValueError( - f"No demonstration dataset with name '{name}'. 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+1,5,6,5,,3,-7,6,-1.753425,0.4949426135438837,2024-12-11,2024-09-08T17:51:06.000000,07:14:14.000000000,p,G,A,, +,1,-4,8,-6,,0,0,,0.7661054038317754,2024-12-12,2024-09-08T18:53:09.000000,07:14:14.000000000,_,y,C,true, diff --git a/metasyn/distribution/discrete.py b/metasyn/distribution/discrete.py index 3eb52dd8..c9ada5bc 100644 --- a/metasyn/distribution/discrete.py +++ b/metasyn/distribution/discrete.py @@ -130,7 +130,9 @@ def _information_criterion(self, values): @classmethod def _fit(cls, values): - return cls(values.mean()) + mean_values = values.mean() + mean_values = mean_values if mean_values >= 0 else 0 + return cls(mean_values) @classmethod def default_distribution(cls): diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index 9fb9ceae..5114539e 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -331,6 +331,8 @@ def _jsonify(data): if isinstance(data, (np.int8, np.int16, np.int32, np.int64)): return int(data) + if isinstance(data, np.float32): + return float(data) if isinstance(data, np.ndarray): return _jsonify(data.tolist()) return data diff --git a/metasyn/var.py b/metasyn/var.py index 9ba6e8dd..6ae081e7 100644 --- a/metasyn/var.py +++ b/metasyn/var.py @@ -74,6 +74,8 @@ def __init__( # noqa: PLR0913 f"Cannot create variable '{self.name}' with proportion missing " "outside range [0, 1]" ) + if self.dtype == "unknown": + self.dtype = str(pl.Series([self.draw()]).dtype) @staticmethod def get_var_type(series: pl.Series) -> str: @@ -232,9 +234,14 @@ def draw_series(self, n: int) -> pl.Series: """ self.distribution.draw_reset() value_list = [self.draw() for _ in range(n)] - if "Categorical" in self.dtype: - return pl.Series(value_list, dtype=pl.Categorical) - return pl.Series(value_list) + pl_type = self.dtype.split("(")[0] + + # Workaround for polars issue with numpy 2.0 + if pl_type == "Boolean": + value_list = [None if x is None else bool(x) for x in value_list] + + # Some dtypes have extra information, discard that + return pl.Series(value_list, dtype=getattr(pl, pl_type)) @classmethod def from_dict( diff --git a/tests/test_dataset.py b/tests/test_dataset.py index c4684d41..819c40d7 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -6,6 +6,7 @@ import pytest from pytest import mark +from metasyn.demo.dataset import _get_demo_class, demo_dataframe, demo_file from metasyn.metaframe import MetaFrame from metasyn.provider import get_distribution_provider from metasyn.var import MetaVar @@ -114,3 +115,31 @@ def test_distributions(tmp_path): prop_missing=random()) dataset = MetaFrame([var], n_rows=10) dataset.to_json(tmp_fp) + +@mark.parametrize( + "dataset_name", ["spaceship", "titanic", "fruit", "survey", "test"] +) +def test_demo_datasets(tmp_path, dataset_name): + demo_fp = demo_file(dataset_name) + demo_df = demo_dataframe(dataset_name) + demo_class = _get_demo_class(dataset_name) + + assert demo_fp.is_file() + assert isinstance(demo_df, pl.DataFrame) + + mf = MetaFrame.fit_dataframe(demo_df, var_specs=demo_class.var_specs) + assert isinstance(mf, MetaFrame) + + tmp_file = tmp_path / "gmf.json" + mf.to_json(tmp_file) + mf = MetaFrame.from_json(tmp_file) + + df_syn = mf.synthesize(100) + + for col, dtype in demo_class.schema.items(): + assert dtype == df_syn[col].dtype + + +def test_demo_non_exist(): + with pytest.raises(ValueError): + demo_file("non-existing-dataset") diff --git a/tests/test_demo.py b/tests/test_demo.py deleted file mode 100644 index d6f60303..00000000 --- a/tests/test_demo.py +++ /dev/null @@ -1,33 +0,0 @@ -from pathlib import Path - -import polars as pl -from pytest import mark, raises - -from metasyn.demo.dataset import demo_file - - -@mark.parametrize("dataset", ["titanic"]) -def test_demo_files(dataset): - data_fp = demo_file(dataset) - assert data_fp.is_file() - - -def test_demo_non_exist(): - with raises(ValueError): - demo_file("non-existing-dataset") - - -def check_titan(output_fp): - df = pl.read_csv(output_fp) - assert len(df.columns) == 13 - assert "Board time" in df.columns - - -# def test_create_titanic(tmpdir): -# output_fp = Path(tmpdir / "titanic.csv") -# new_output_fp = create_titanic_demo(output_fp) -# assert new_output_fp == output_fp -# check_titan(output_fp) -# new_output_fp = create_titanic_demo(output_fp) -# assert new_output_fp == output_fp -# check_titan(output_fp) diff --git a/tests/test_var.py b/tests/test_var.py index b7b3f1fe..3ba56331 100644 --- a/tests/test_var.py +++ b/tests/test_var.py @@ -1,5 +1,6 @@ import datetime as dt import json +import string import numpy as np import pandas as pd @@ -117,6 +118,39 @@ def test_nullable_integer(dtype, tmp_path): assert new_series.min() >= 0 assert new_series.max() < 10 +@mark.parametrize( + "dtype", [pl.Int8, pl.Int16, pl.Int32, pl.Int32, pl.Int64, + pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64] +) +def test_polars_discrete(dtype, tmp_path): + series = pl.Series("ser", [np.random.randint(0, 10) for _ in range(100)], dtype=dtype) + new_series = check_var(series, "discrete", tmp_path) + assert new_series.min() >= 0 + assert new_series.max() < 10 + +@mark.parametrize( + "dtype", [pl.Float32, pl.Float64] +) +def test_polars_continuous(dtype, tmp_path): + series = pl.Series("ser", np.random.rand(100), dtype=dtype) + new_series = check_var(series, "continuous", tmp_path) + assert new_series.min() > -0.5 + assert new_series.max() < 1.5 + + +@mark.parametrize( + "dtype", [pl.String, pl.Utf8] +) +def test_polars_string(dtype, tmp_path): + series = pl.Series("series", string.printable, dtype=dtype) + check_var(series, "string", tmp_path) + +@mark.parametrize( + "dtype", [pl.Categorical] +) +def test_polars_categorical(dtype, tmp_path): + series = pl.Series("series", string.printable, dtype=dtype) + check_var(series, "categorical", tmp_path) @mark.parametrize( "series_type", From 39383031b408f1477a23c6cf91287769ccd0242a Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Wed, 9 Oct 2024 15:50:10 +0200 Subject: [PATCH 03/17] Add new dataset and make regex more used (#330) * Add new dataset and make regex more used * Fix linting issue * Automatically add new datasets to tests --------- Co-authored-by: Erik-Jan van Kesteren --- metasyn/demo/dataset.py | 27 +- metasyn/demo/demo_synthea_imaging.csv | 368 ++++++++++++++++++++++++++ metasyn/distribution/string.py | 6 + tests/test_dataset.py | 4 +- 4 files changed, 401 insertions(+), 4 deletions(-) create mode 100644 metasyn/demo/demo_synthea_imaging.csv diff --git a/metasyn/demo/dataset.py b/metasyn/demo/dataset.py index aaa0524b..6f820d29 100644 --- a/metasyn/demo/dataset.py +++ b/metasyn/demo/dataset.py @@ -116,6 +116,25 @@ def schema(self): return {} +@register +class SyntheaImagingDataset(BaseDataset): + """Synthetic medical health dataset from Synthea. + + Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, + Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, + Synthea: An approach, method, and software mechanism for generating synthetic patients + and the synthetic electronic health care record, Journal of the American Medical Informatics + Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079 + """ + + @property + def name(self): + return "synthea_imaging" + + @property + def schema(self): + return {"SOP_DESCRIPTION": pl.Categorical, "BODYSITE_DESCRIPTION": pl.Categorical} + @register class TestDataset(BaseDataset): """Test dataset with all supported data types.""" @@ -195,11 +214,13 @@ def _get_demo_class(name): def demo_file(name: str = "titanic") -> Path: """Get the path for a demo data file. - There are four options: + There are six options: - titanic (Included in pandas, but post-processed to contain more columns) - spaceship (CC-BY from https://www.kaggle.com/competitions/spaceship-titanic) + - synthea_imaging (CC-BY from https://synthea.mitre.org/downloads) - fruit (very basic example data from Polars) - survey (columns from ESS round 11 Human Values Scale questionnaire for the Netherlands) + - test (columns with all supported data types) Arguments --------- @@ -222,11 +243,13 @@ def demo_file(name: str = "titanic") -> Path: def demo_dataframe(name: str = "titanic") -> pl.DataFrame: """Get a demonstration dataset as a prepared polars dataframe. - There are four options: + There are six options: - titanic (Included in pandas, but post-processed to contain more columns) - spaceship (CC-BY from https://www.kaggle.com/competitions/spaceship-titanic) + - synthea_imaging (CC-BY from https://synthea.mitre.org/downloads) - fruit (very basic example data from Polars) - survey (columns from ESS round 11 Human Values Scale questionnaire for the Netherlands) + - test (columns with all supported data types) Arguments --------- diff --git a/metasyn/demo/demo_synthea_imaging.csv b/metasyn/demo/demo_synthea_imaging.csv new file mode 100644 index 00000000..972044e5 --- /dev/null +++ b/metasyn/demo/demo_synthea_imaging.csv @@ -0,0 +1,368 @@ +Id,DATE,PATIENT,ENCOUNTER,SERIES_UID,BODYSITE_CODE,BODYSITE_DESCRIPTION,MODALITY_CODE,MODALITY_DESCRIPTION,INSTANCE_UID,SOP_CODE,SOP_DESCRIPTION,PROCEDURE_CODE +42958cbc-dc44-96f3-8bc7-88de8eb219c9,1991-03-14T14:17:09Z,6b0b0021-df03-cbb6-305a-11ec40da7af4,38c0ae05-587a-15f4-7e0a-4a38a9f27351,1.2.840.99999999.1.72796407.668960229186,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.11466877.668960229186,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e8ae2e16-e5ef-bddf-a4f5-ff1c0f4d5322,2015-05-01T22:28:35Z,6b0b0021-df03-cbb6-305a-11ec40da7af4,c61d6c05-7268-9ef2-c69d-5fa90f994690,1.2.840.99999999.1.12709069.1430519315186,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.72065638.1430519315186,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray 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Radiography,1.2.840.99999999.1.1.39467642.1629386370186,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9eff1cd0-196c-8dbb-34b1-fbd1bfea3260,2023-05-20T23:40:20Z,6b0b0021-df03-cbb6-305a-11ec40da7af4,494f5678-83b6-eb74-96da-b72391e72d82,1.2.840.99999999.1.34817570.1684626020186,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.67569600.1684626020186,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +91a44b44-23d2-a5e6-3f7b-db0f6625f722,2021-08-12T20:48:40Z,87f6c14f-38d1-966f-4109-ac29736ec22f,10be577c-40d8-91bc-9522-5df6ce6ec6b8,1.2.840.99999999.1.53104523.1628801320033,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58944126.1628801320033,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 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+5a2d3351-13f8-bed6-5378-a59a473e27d4,2018-03-22T04:11:18Z,f2bd135b-5cc4-9f39-73f2-c90c5ce19288,b70bc17e-a521-34a5-1e0c-28905888eebd,1.2.840.99999999.1.54370547.1521691878576,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.56924361.1521691878576,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +04685cc2-ca0e-e04c-9c9c-b32c7a8afb44,2020-04-02T03:57:52Z,f2bd135b-5cc4-9f39-73f2-c90c5ce19288,2127cf39-aaca-61ef-4c1f-ff64419f7699,1.2.840.99999999.1.66873004.1585799872576,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.39220646.1585799872576,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5d1ae1a8-041c-566c-4d93-6d9bcf8239a1,2021-07-12T05:59:21Z,1415b79b-ec25-3728-027e-d9a8f4048441,054a628b-2521-c7cb-09d7-d025656c5d58,1.2.840.99999999.1.91563043.1626069561695,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.27262140.1626069561695,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +df2fcd0d-d4a3-c261-1e44-e3e043d6c8a3,2023-07-24T06:33:49Z,1415b79b-ec25-3728-027e-d9a8f4048441,370d3ef5-5905-4ac9-f430-927d6362ba06,1.2.840.99999999.1.31886843.1690180429695,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.33569297.1690180429695,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +3aa58ecf-31ca-ddd0-ae18-c9462fe0a260,2015-06-16T17:19:46Z,8ff88069-572b-a60a-a119-e3e74fe41b71,33a5dc7c-7ee0-700f-24d5-4ccbcdf68f98,1.2.840.99999999.1.20206020.1434475186429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.76556242.1434475186429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a8b9e930-589d-258e-3a3f-b4deb14888eb,2017-06-27T16:49:18Z,8ff88069-572b-a60a-a119-e3e74fe41b71,67ff097b-9a61-6f32-de73-eaff8fdfaa5b,1.2.840.99999999.1.16135345.1498582158429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42765729.1498582158429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f2cc7dbc-5d71-e9f2-c6e3-ed5b02ccccd1,2021-07-06T16:15:47Z,8ff88069-572b-a60a-a119-e3e74fe41b71,29ef1fc9-85ce-9632-1665-15650945d868,1.2.840.99999999.1.74348042.1625588147429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.69538726.1625588147429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7087301f-d1c3-c91d-791b-f31d38eb1cf2,2018-05-19T04:43:04Z,20bcef21-1d0a-b1f3-e5cf-013400d73614,29aab06c-7230-c0fb-5d23-0cd2ff82f92a,1.2.840.99999999.1.95808428.1526704984254,8205005,Wrist,DX,Digital Radiography,1.2.840.99999999.1.1.47151885.1526704984254,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,60027007 +da90e515-f6d1-c6c7-9ae6-4488e436b312,2017-01-09T12:47:03Z,75c8628d-bace-81e5-fa6a-77717a1ab484,cdc39b27-61f3-f398-29b8-ba7b89a0e2a7,1.2.840.99999999.1.32470509.1483966023846,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.69339756.1483966023846,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +55476edb-2e25-e7d4-4184-6b6f05b87375,2021-06-07T13:47:33Z,75c8628d-bace-81e5-fa6a-77717a1ab484,57a7e081-9ba0-b380-f01c-e497001b8d53,1.2.840.99999999.1.40954816.1623073653846,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24830298.1623073653846,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ba55faa0-275f-73a7-5ed4-e06d014fb8c7,1955-05-09T18:36:44Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,b0319b21-acb1-80d6-62dc-acd0deb9ce9a,1.2.840.99999999.1.97154680.462259395872,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.13423532.462259395872,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +f8bb0176-b357-61e0-624a-988616187a96,1980-06-21T20:01:51Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,1c452c02-d837-475f-dfc7-8f64ce5049b5,1.2.840.99999999.1.35349369.330465711128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.63061990.330465711128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e0cd31c1-4e20-5702-8db2-706340693d22,1981-04-18T19:40:56Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,ddd37ee1-d778-b1df-5eff-decb4d90e676,1.2.840.99999999.1.71842115.356470856128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.31536829.356470856128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c6a5cf5f-2dd7-5e9e-3ee8-5cacf5293289,1982-11-06T20:00:16Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,464491c6-a2f2-9532-1416-8be43171fed3,1.2.840.99999999.1.97787803.405460816128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89656634.405460816128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ac30756b-1f48-7915-3046-5cce2b35d4a5,1983-02-05T19:48:11Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,72609cd9-1113-bf4f-7750-678253cde229,1.2.840.99999999.1.60378018.413322491128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99512602.413322491128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f41e7b05-d58a-364a-d3d5-279f3fbb0b3c,1983-09-17T20:25:22Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,838e5531-638c-add4-2d3c-f221fcd1040d,1.2.840.99999999.1.84524209.432678322128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.32813041.432678322128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0b99f9bf-6d9f-6e64-8e72-e510df9a98a5,1984-09-22T19:34:52Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,a94aecab-36a7-1a62-8d56-5bace8bc5625,1.2.840.99999999.1.50015733.464729692128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24027740.464729692128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7ed2ae82-4baa-b34d-a0fb-75dd36f7f252,1984-10-27T19:28:18Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,dd78b439-d5a7-a877-63e1-192eb767243e,1.2.840.99999999.1.45634069.467753298128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58985104.467753298128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5296cf6d-b9b7-eafb-0eb8-bd3666c47b2d,1984-11-24T20:02:17Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,a02f4ca6-ba16-e603-abbd-aa5aad049ad3,1.2.840.99999999.1.88694812.470174537128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.83251273.470174537128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +922cd10e-93cf-7560-b8b2-ed9aefe009bf,1986-10-04T20:11:07Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,f29f6527-9611-0487-e2dc-4ca9f36fb102,1.2.840.99999999.1.87496905.528840667128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.30517003.528840667128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +99cd0e7f-9365-09ef-d593-08855d5de346,1988-05-07T20:03:22Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,d1342412-57ea-adca-f7ea-762beb1bca73,1.2.840.99999999.1.55514114.579038602128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.88879327.579038602128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ac2facaf-beaf-5eb1-6528-5b8434aeef4b,1989-02-11T18:28:58Z,c6047dfa-7b8c-3001-7ce0-09b17fc73339,f99a4c3f-196c-ea27-7f55-0739029e72c3,1.2.840.99999999.1.85943079.603224938128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.12253817.603224938128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c07c1eeb-650d-df40-c72e-b2e73c72ea76,2018-05-02T21:10:10Z,7a30894a-e796-7923-c099-0d9c275d49c1,62e3cc55-f8c6-5719-e414-2903bdc085ff,1.2.840.99999999.1.47246141.1525295410708,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99845130.1525295410708,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +afa89820-8d69-5fb7-fcdd-6378e125f889,2019-05-08T21:21:49Z,7a30894a-e796-7923-c099-0d9c275d49c1,c08348f2-5ca9-faf2-e8a2-bc6dbff90df5,1.2.840.99999999.1.14879482.1557350509708,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.71300563.1557350509708,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b640d0e0-b5de-7b69-219d-07b6c01dc1d5,2024-06-05T21:09:25Z,7a30894a-e796-7923-c099-0d9c275d49c1,9422801e-e161-f5c5-29c3-59af5c01e9d6,1.2.840.99999999.1.99673639.1717621765708,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.64920864.1717621765708,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +36cf8a9c-e68a-2cf4-912f-91fbae1d81d0,2016-06-14T01:04:36Z,48ba2eae-3079-92c7-82e3-31b7993958e8,ddad29b7-bd69-8491-ce43-535cb368eb76,1.2.840.99999999.1.58311545.1465866276130,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.88848446.1465866276130,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2525389d-e976-113b-5fa5-a74b8dfb5cd8,2018-05-13T23:30:57Z,48ba2eae-3079-92c7-82e3-31b7993958e8,bc9fea8a-0b6a-2bbc-618f-987a7bbf3805,1.2.840.99999999.1.54217649.1526254257130,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.78890662.1526254257130,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +cbf87f46-b3ce-bc49-614f-4cbe55b1b7c6,2018-05-28T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,b059b3a5-55d6-d19d-7d33-6b54e4f9acff,1.2.840.99999999.1.99722111.1527551522130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.15764597.1527551522130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +3352f009-d555-8621-2a96-a91a2cd4ea35,2018-11-24T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,94051011-94c0-739f-234d-95f0396baf9d,1.2.840.99999999.1.29285701.1543103522130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.41283399.1543103522130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +3d5e1fc0-5602-e830-6ddb-e88488e43ff6,2019-05-23T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,37de9e61-16c6-51cb-2163-93ae9b6a8e62,1.2.840.99999999.1.93281104.1558655522130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.94707294.1558655522130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +cb3b3c3e-1715-69c5-92f2-9a03d86721f7,2019-11-19T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,6280418e-9412-5a2b-0687-be29e34f19fa,1.2.840.99999999.1.86905890.1574207522130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.44335388.1574207522130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +7243e445-8a5b-a51e-4435-dec94bb53858,2020-01-14T01:18:19Z,48ba2eae-3079-92c7-82e3-31b7993958e8,6afc00c9-27d0-37f4-875d-c7a6979a3135,1.2.840.99999999.1.32842199.1578964699130,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.49343449.1578964699130,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e4f0ca31-477e-cb4f-03dd-1b7f3592f65d,2020-05-17T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,b2a97f33-db20-e79d-2ce6-8982e1ba1175,1.2.840.99999999.1.26953878.1589759522130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.40928661.1589759522130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +208bc0c4-3b7e-bf80-5b35-6708c155ce60,2020-11-13T23:52:02Z,48ba2eae-3079-92c7-82e3-31b7993958e8,8ee11868-a982-dfe3-0aed-2b5980bf8212,1.2.840.99999999.1.49009146.1605311522130,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.10999865.1605311522130,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +7b38fbf6-49d2-a2d5-b25c-881b1e6d6df7,2021-05-16T00:34:33Z,48ba2eae-3079-92c7-82e3-31b7993958e8,70c7f4b7-ed35-12ec-fc2b-d5690719e9bc,1.2.840.99999999.1.72819976.1621125273130,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.19468497.1621125273130,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +4b90df79-5bfc-f1ac-bff7-b1c571867406,2021-06-04T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,1de90f53-a9df-1304-9ca0-0046d34a7a2d,1.2.840.99999999.1.95524325.1622769734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.42380025.1622769734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +f09ce2ad-247a-07f1-a26e-c4763d636098,2021-12-01T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,8aba4652-5d45-f598-6330-a2e8b75a4cb6,1.2.840.99999999.1.41367613.1638321734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.31439285.1638321734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +81243819-4c86-9bde-fea5-2137fa2a0515,2022-01-25T01:02:33Z,48ba2eae-3079-92c7-82e3-31b7993958e8,1016a0c4-109c-d680-1fe2-4b121dde4541,1.2.840.99999999.1.46356259.1643072553130,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.91178689.1643072553130,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4aa7c0c2-6795-7355-ef9d-e2f68c02209a,2022-05-30T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,48594abd-9f55-cd18-ec90-492c431e037e,1.2.840.99999999.1.30909795.1653873734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.42850716.1653873734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +39934a3d-f3db-f9bc-5b19-528c2163d4ca,2022-11-26T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,46e65188-2621-7b31-e5f6-a5c9a328bec6,1.2.840.99999999.1.64617644.1669425734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.54539421.1669425734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +58ad14c8-4a36-562e-c08d-a559722711fc,2023-05-25T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,fb5281be-fc71-264c-7244-0bca3e70ab8f,1.2.840.99999999.1.67989274.1684977734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.50657740.1684977734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +b19d58e3-f991-398a-336a-7ae00a1040d9,2023-11-21T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,afecc3ed-78aa-1b53-a4a2-7088677cb639,1.2.840.99999999.1.89178976.1700529734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.64198152.1700529734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +70026d4e-78e2-2786-21f6-0ec995704940,2024-05-19T01:22:14Z,48ba2eae-3079-92c7-82e3-31b7993958e8,1fb56834-07d4-4ad6-0195-1dd8a27b2f1d,1.2.840.99999999.1.36055017.1716081734130,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.26286610.1716081734130,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +6631c4d9-dd0f-3138-528f-30f34ee60c1a,2016-04-01T01:41:00Z,08230260-e919-5227-1ee9-cccc39648a34,e074d0f0-10de-a1bb-b704-9b1712ccf745,1.2.840.99999999.1.76294419.1459474860057,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.92804501.1459474860057,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +32bde1ba-65c9-e3d1-2401-32667fde33e5,2024-09-05T23:31:58Z,08230260-e919-5227-1ee9-cccc39648a34,5df31fd9-d8be-91df-84a4-3afe6d64b32d,1.2.840.99999999.1.12200095.1725579118057,72696002,Knee,DX,Digital Radiography,1.2.840.99999999.1.1.57247101.1725579118057,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,74016001 +e35e0101-7736-6970-d104-fe8774238ab0,2014-11-11T13:08:42Z,310d55af-c5c1-5bd6-0887-b945a8f5b9c0,9d3863fd-74de-cf8b-5da1-e2f60f019398,1.2.840.99999999.1.80960492.1415711322095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.39657988.1415711322095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +3fb032c4-c44c-201e-232e-86a698b19dce,2017-11-28T13:21:18Z,310d55af-c5c1-5bd6-0887-b945a8f5b9c0,9d7a1d2e-9c08-61ea-90f9-9f19b7801255,1.2.840.99999999.1.82724852.1511875278095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.66397335.1511875278095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a2385cdc-4757-9690-72ff-d4e311ea74d0,2021-03-18T11:03:39Z,310d55af-c5c1-5bd6-0887-b945a8f5b9c0,31a040c1-88cc-7d30-5f65-55abc8cfecb3,1.2.840.99999999.1.26285243.1616065419095,8205005,Wrist,DX,Digital Radiography,1.2.840.99999999.1.1.51628310.1616065419095,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,60027007 +9e3b449a-62d9-9676-d1f1-dd1f0c12cee4,2024-01-09T13:11:33Z,310d55af-c5c1-5bd6-0887-b945a8f5b9c0,24f32234-fd0c-9f8c-ab0a-8df28085a150,1.2.840.99999999.1.81256132.1704805893095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.51404780.1704805893095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +200e699e-cbcd-21e4-a934-a2581236bb85,2015-07-06T20:45:15Z,d9399d07-b259-e07f-00c9-2258844a1ed1,9cde18ac-aa64-bc27-adc2-975a424de852,1.2.840.99999999.1.95495586.1436215515175,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.14437820.1436215515175,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b5f333fe-36ca-52b3-f40b-ad06da31e5ff,2019-09-23T20:29:54Z,d9399d07-b259-e07f-00c9-2258844a1ed1,caba4642-8acd-0ea3-7820-685e0c4d545a,1.2.840.99999999.1.40024462.1569270594175,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24414260.1569270594175,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d73991ae-b4fe-c48e-06a5-5d479cad741c,2020-09-28T20:14:43Z,d9399d07-b259-e07f-00c9-2258844a1ed1,4260d367-f141-1f2f-8e92-5d4f958afbf0,1.2.840.99999999.1.11151731.1601324083175,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.88521256.1601324083175,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +dc9937d8-2393-c78f-f4e8-ce655ec9ef56,2023-03-20T20:09:26Z,d9399d07-b259-e07f-00c9-2258844a1ed1,73a6462f-3949-d710-fe9a-9d9ebbb3080e,1.2.840.99999999.1.25341701.1679342966175,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95052206.1679342966175,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0d9c2fd7-a0dc-4203-ab37-cfab8b4ca77b,2023-10-23T20:41:41Z,d9399d07-b259-e07f-00c9-2258844a1ed1,57c88137-4e48-b914-4309-08fbe2f8ccb1,1.2.840.99999999.1.42544758.1698093701175,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.92166694.1698093701175,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +dc28f90d-6ef2-4c70-1bb6-21eef7e4bdf4,2018-09-08T06:07:23Z,b6a2deb5-8e5f-673d-d56e-90f2eb4b46a1,3f4ddd61-bc88-44ed-50e9-a48e03d12519,1.2.840.99999999.1.88931235.1536386843202,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.67364486.1536386843202,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +82eead9c-dad8-df7a-b9a3-4285f7f02728,2020-09-12T06:14:34Z,b6a2deb5-8e5f-673d-d56e-90f2eb4b46a1,e2c82b1a-7da4-44f6-5a76-2b84a9abe381,1.2.840.99999999.1.98571307.1599891274202,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89021456.1599891274202,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9fb6ce89-74dd-94f1-d64e-4b7fe939bcf7,2022-09-17T05:40:49Z,b6a2deb5-8e5f-673d-d56e-90f2eb4b46a1,f78cfbff-7694-e0ca-1d69-8a6167ad8b68,1.2.840.99999999.1.58962116.1663393249202,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80132998.1663393249202,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5fb527f9-b727-da13-b0fb-02b1da567881,2010-10-17T15:35:14Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,1b293be7-aedf-c8e1-1d5c-c85da7f3706f,1.2.840.99999999.1.67952248.1287329714154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.39504286.1287329714154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a9627a04-80f2-89f3-fb24-e61f77dc4a9a,2014-11-09T16:48:36Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,f648aafd-e3c8-ecbe-2c3e-f864c6935f7d,1.2.840.99999999.1.17775130.1415551716154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41087572.1415551716154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a1bb0d26-9977-5d3a-0fcb-db7910509275,2016-11-13T17:19:54Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,b4c1c07f-0013-86e0-19b4-3d0bda521b2e,1.2.840.99999999.1.54624929.1479057594154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58342317.1479057594154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +91b5f8a1-bd68-a66a-383d-3901c7875c96,2017-12-03T15:13:36Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,7797a910-2d2c-28e2-0682-47896fb96bc9,1.2.840.99999999.1.38880329.1512314016154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.96491253.1512314016154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +05d514c3-f9e8-2a78-1d02-bc13c4a64ce1,2021-04-21T13:29:10Z,a0b2c6b1-a80d-aad2-be32-53c729b3a6f5,16e212d6-a499-31c4-e19f-7d3655f2f26d,1.2.840.99999999.1.39194373.1619011750529,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.47003253.1619011750529,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +81f34cea-e159-5e0d-c577-1ec74a1d5f5e,2018-05-13T17:21:24Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,88527552-e0c8-d6aa-fdfc-3187a033aa97,1.2.840.99999999.1.86403372.1526232084154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.90417241.1526232084154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0de608ed-d83b-c0be-8ce0-89aa677acf77,2022-12-18T17:11:27Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,a277f726-3ba0-11e7-a279-fa4fc528c253,1.2.840.99999999.1.31686412.1671383487154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10703311.1671383487154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b8145b64-484e-4e35-5529-70ae4396c588,2023-12-24T17:13:00Z,f838af93-12a0-6440-63ae-ac3c28fb4fa6,91b9dea3-b0c7-41f4-db2c-6cf6aa7936ed,1.2.840.99999999.1.47423171.1703437980154,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80312623.1703437980154,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a5133559-fd58-f9a7-2bf2-621ec3b8e0df,2010-01-27T10:49:58Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,d2d51d1e-69aa-3594-3667-8be8c2411b19,1.2.840.99999999.1.58877670.1264589398233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.62417172.1264589398233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +59ea9464-7cd0-d26d-0c66-594f972a7f97,2016-07-20T12:20:38Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,70312b84-f299-d3b5-3ff6-c5044ce476bd,1.2.840.99999999.1.63452765.1469017238233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80361002.1469017238233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8f0afbbb-24fe-1605-b980-9741b2dd35f3,2017-04-12T12:29:10Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,7b733fa9-f796-39b9-be9f-e6d8e4908cbf,1.2.840.99999999.1.22912141.1492000150233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.96335296.1492000150233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +442ab650-0c67-9062-3ed5-a4388f31074a,2019-03-29T10:59:45Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,8a9b7665-f144-dfdf-aee3-ce28a0b47ef2,1.2.840.99999999.1.28262425.1553857185233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.46234132.1553857185233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d6da4355-fbb0-80ec-41ca-074fd6cd11a3,2019-04-24T12:18:44Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,f7737583-5a86-a5d4-d3b4-8b2e9e242dbb,1.2.840.99999999.1.99019425.1556108324233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.35171715.1556108324233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +afe92ffc-3c37-a547-b4bf-2023add83691,2022-05-11T12:53:17Z,536ca078-e7a9-5a9e-d414-8bb84d25318f,aca8782d-49c0-5693-6a2b-41000a5168c1,1.2.840.99999999.1.43926358.1652273597233,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.70599261.1652273597233,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a4cb6fea-ec64-c6d6-e73c-4c1626d868ac,2015-11-10T15:20:28Z,b263294e-5c15-5414-115b-017fa63d425f,747de13d-0a0e-bae8-eae2-2b7d7ea2a5d8,1.2.840.99999999.1.48875817.1447168828208,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.66495946.1447168828208,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9a321665-745c-d6a3-1bcb-7edce3908728,2024-01-02T15:36:01Z,b263294e-5c15-5414-115b-017fa63d425f,6a3498da-9786-8057-0110-18bb1e4f27f5,1.2.840.99999999.1.16840005.1704209761208,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.35493371.1704209761208,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f806aa25-6aaf-88a5-465c-52cd2a96659a,2019-12-21T05:37:41Z,cb5df8b1-199e-6d35-1ff0-c0e0801edf17,81d922a6-f0ee-142f-0a75-00b257dec532,1.2.840.99999999.1.21933055.1576906661848,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.67621734.1576906661848,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c25ca814-f802-1d8d-a59d-578a39b66a08,2020-12-26T05:09:15Z,cb5df8b1-199e-6d35-1ff0-c0e0801edf17,7f68f5ac-0aaa-b7e6-7990-1170f521d392,1.2.840.99999999.1.30327516.1608959355848,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.17504659.1608959355848,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f88b75c6-7d97-9b32-e2b0-7885552ffc54,1986-08-09T19:58:43Z,5d2185fc-dae6-6e2d-9240-92235817ddce,6aa70480-8165-e82d-6b57-4db7e793ab83,1.2.840.99999999.1.11138108.524001523128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.36707808.524001523128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +33843b50-2839-422c-8b37-ff7c793bc852,1989-11-11T20:05:23Z,5d2185fc-dae6-6e2d-9240-92235817ddce,cca8ce84-c210-f371-f6fc-70cac08ab106,1.2.840.99999999.1.81821929.626817923128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10031793.626817923128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6f65f253-c8d2-697e-d999-5079de0e9e40,1990-02-10T20:00:33Z,5d2185fc-dae6-6e2d-9240-92235817ddce,1771d88a-c6ee-ca12-8b8e-02ec6f19b737,1.2.840.99999999.1.23330925.634680033128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.17489109.634680033128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +eb6d1a91-9882-7319-3d97-c82f2c2bb23f,1991-10-05T19:35:57Z,5d2185fc-dae6-6e2d-9240-92235817ddce,2ac86a49-42c9-1780-7fcb-3478bd1eef71,1.2.840.99999999.1.65759247.686691357128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.52627126.686691357128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4a7dd098-6a4c-34c5-83df-4411a56d9971,2003-03-07T08:26:36Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,86427c04-fc66-48b6-cd4f-03dfe362aa9b,1.2.840.99999999.1.85047445.1047025596478,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.66067373.1047025596478,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +35a3dc81-62b7-82d5-873a-0640e403edd1,2005-02-20T06:44:25Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,fb875549-ca72-23f9-4d1e-1744ad094c2d,1.2.840.99999999.1.94747071.1108881865478,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.19634277.1108881865478,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +ee2ceb82-1c73-5df4-bd98-78405665059d,2005-03-07T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,fc063de0-c57d-86fb-e9b0-dbeec5b2975a,1.2.840.99999999.1.51194374.1110179267478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.46960450.1110179267478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +fc96abb7-8ec9-a17b-ca5a-e544257052ba,2005-04-01T08:27:41Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,5998774d-2e9c-9bf4-9dbb-14903c0117ac,1.2.840.99999999.1.52534465.1112344061478,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.83384038.1112344061478,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0884d673-80d9-3441-64d8-1a38ec753abe,2005-09-03T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,c4cf6091-5398-4d94-94c3-846a0b5890c4,1.2.840.99999999.1.93479006.1125731267478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.32059668.1125731267478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +9c083bfb-b6f0-ee87-5b62-42ccfaf84bde,2006-03-02T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,46c44874-d8b6-03d2-74f7-86ba12e70687,1.2.840.99999999.1.48605267.1141283267478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.22779795.1141283267478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +17bcb684-bc12-ec54-f893-f50e63877b46,2006-08-29T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,40dd37e9-59e5-08dd-5cc2-2e8ace731ed9,1.2.840.99999999.1.61044641.1156835267478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.55118775.1156835267478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +2edc0a2b-8d92-01d3-3f31-85a474063fd4,2007-02-25T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,7c6302f0-682f-6dda-2926-eec4dff7c735,1.2.840.99999999.1.46491876.1172387267478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.94210931.1172387267478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +db2a7b73-6033-ce74-937c-302dcede0364,2007-08-24T07:07:47Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,8629c049-cab8-eefe-9f77-900dec7dc010,1.2.840.99999999.1.55327419.1187939267478,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.77634601.1187939267478,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +c94fd2c6-03a1-e5e1-68f6-7007fd114277,2008-02-22T08:12:00Z,d4d35db8-ac84-b080-95db-cd4efe0d4fe3,403d4b8b-bfd9-0cc6-e9bd-2eaea6a3526d,1.2.840.99999999.1.73806547.1203667920478,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.57872013.1203667920478,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +bd499aee-ee4b-c809-3dd8-23d67eda4f3a,1988-02-04T02:01:06Z,3a2df2e8-81d3-d474-ac3e-d217c75be94b,4b16fe51-60ec-74e8-2283-7e47229fa09a,1.2.840.99999999.1.37559967.570938466429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.43901861.570938466429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b9d3e4f3-26c2-2354-095f-da59816d4e3d,2015-03-19T03:30:25Z,3a2df2e8-81d3-d474-ac3e-d217c75be94b,a1fd563d-fb77-6152-3746-e6e8b589371e,1.2.840.99999999.1.84028942.1426735825429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.49534555.1426735825429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +64223f63-0d7d-3fdc-f279-15f914544672,2021-04-15T02:22:17Z,3a2df2e8-81d3-d474-ac3e-d217c75be94b,a536afe3-488a-d34e-73a4-84170b7af46e,1.2.840.99999999.1.90156238.1618453337429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.30114212.1618453337429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +17161f58-c3c3-ff21-1523-91d84dfbe2fe,2022-04-28T02:57:50Z,3a2df2e8-81d3-d474-ac3e-d217c75be94b,56fb5461-4a73-b16b-1048-877b0808a530,1.2.840.99999999.1.43724105.1651114670429,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.22870068.1651114670429,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f0cd72bb-7e3a-25b3-ab6f-42b335930d89,2016-04-13T14:41:35Z,9caf76bd-13ed-1b4f-7a08-fb9c4bb17ecd,105c80e8-baec-dfba-affe-4a163d68fd64,1.2.840.99999999.1.28142083.1460558495610,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.49006268.1460558495610,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e7074069-0047-e1a6-d9ef-08b647d75280,2017-04-19T14:45:48Z,9caf76bd-13ed-1b4f-7a08-fb9c4bb17ecd,7489eabe-6d27-31d4-f8ae-2cd256a0a69e,1.2.840.99999999.1.28454509.1492613148610,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.98409310.1492613148610,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +02fede4d-99b2-984c-f143-88f19c127c41,2015-07-10T08:35:01Z,549735c8-ce2c-f286-6afb-006b97566eff,3dc8fc3e-eff8-a674-7e18-b85c664fb636,1.2.840.99999999.1.36702736.1436517301478,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.92157091.1436517301478,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +04877909-235c-c8ec-6eae-4b3d541ea654,2017-07-21T08:33:25Z,549735c8-ce2c-f286-6afb-006b97566eff,596f8d6c-d132-95d0-0163-1e56abf5ae70,1.2.840.99999999.1.81925932.1500626005478,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42232978.1500626005478,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ce94e625-e567-78ad-d1e8-b832944917b1,2024-08-30T08:37:22Z,549735c8-ce2c-f286-6afb-006b97566eff,74db4c0a-35eb-78cf-a7c0-04e48034e511,1.2.840.99999999.1.21373875.1725007042478,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.72961850.1725007042478,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f4864ccf-100b-7d19-4352-a8d3de972afe,2016-06-14T11:15:04Z,5dd384e4-379f-b391-4ab0-01516eabf11e,559ada1f-0988-1d48-71c6-6744ad1e19e9,1.2.840.99999999.1.36692339.1465902904131,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.17961519.1465902904131,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +254ef49b-e484-e008-4008-9bbf3b3dd19d,2018-05-22T11:43:59Z,5dd384e4-379f-b391-4ab0-01516eabf11e,d8b1d38f-64e1-f842-7024-fdd45fca3944,1.2.840.99999999.1.27534880.1526989439131,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.66586761.1526989439131,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +56b99384-6ab3-f92c-292d-589aef6528b9,2024-03-24T11:21:04Z,ef0b0bfb-3d7a-6ff0-ffab-a1af3c1d6b2e,d25c2d49-f9e0-19f9-2d49-5c9d9339c129,1.2.840.99999999.1.97558357.1711279264126,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.90352731.1711279264126,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0c499aba-992a-55a8-9eef-a30f94ba0d2e,2021-06-22T11:06:43Z,422eb795-6eb4-4a51-0b89-d2af5c89ea94,94b46ef8-a983-a704-c082-ed6eb0aa4acf,1.2.840.99999999.1.15417050.1624360003599,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89542692.1624360003599,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6ea58c20-82ed-bc16-b8e7-b8fd877097df,2016-12-08T21:58:41Z,f4a37fe4-8e9b-ef15-50fd-43abf6259dce,866f459a-4aa2-36d8-66a0-e216a4dac63d,1.2.840.99999999.1.82277328.1481234321817,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.16702516.1481234321817,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +b9290b5d-774a-5ea2-51e2-9ac1421890d2,2021-11-21T23:55:04Z,f4a37fe4-8e9b-ef15-50fd-43abf6259dce,7702c3d2-18a2-2e4f-f801-04194c83a15e,1.2.840.99999999.1.91380238.1637538904817,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.94417746.1637538904817,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +df88756d-7933-7960-871e-ce6b5e7b1c06,2015-06-11T16:35:51Z,ba74d03c-466c-2236-c6af-0f656140bb28,6dadcf12-ec55-c347-6c7c-9ccaf8ddc90f,1.2.840.99999999.1.33146729.1434040551392,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.51803340.1434040551392,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1292a01c-d39d-f035-342a-56f443c5997c,2021-07-15T16:49:08Z,ba74d03c-466c-2236-c6af-0f656140bb28,19ea0beb-914d-2c49-d2c0-a9c5a2dbfa3d,1.2.840.99999999.1.33541681.1626367748392,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80327083.1626367748392,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6c8dc369-b7b7-b388-3fd4-8122400aa38a,2015-03-12T12:46:04Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,d7222983-b82c-470d-0609-e87496d708ec,1.2.840.99999999.1.93360173.1426164364122,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.81555744.1426164364122,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0912459c-6d04-7061-4f6f-72c30f3771b2,2019-04-04T12:35:56Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,035626a4-4364-54fc-4fd3-e53a511d099f,1.2.840.99999999.1.23026819.1554381356122,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.52115460.1554381356122,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +31360266-a338-55a2-3395-c14671910d9f,2020-04-09T12:38:17Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,7773a477-f176-8a38-ba04-ccd64e629c96,1.2.840.99999999.1.58360616.1586435897122,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.82937382.1586435897122,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +547de4f7-acb8-6f50-1954-ff77030c47bd,2023-05-04T13:11:26Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,4c0bc1ba-4741-c034-b2bc-0e2f37fbaf61,1.2.840.99999999.1.41679884.1683205886122,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.31595964.1683205886122,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f65274a6-fefc-2905-49c1-3ea4aa04757b,2024-03-04T11:20:36Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,28cc1a0f-43b8-d9f7-9947-889931e73bd0,1.2.840.99999999.1.37751129.1709551236122,40983000,Structure of upper extremity between shoulder and elbow (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.64494802.1709551236122,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,1225002 +e244c922-a234-8e9a-6a3f-4e94d77dad02,2024-05-09T13:30:21Z,1a654b50-5c1d-ec96-1d56-8d7c12140983,25554669-3a1c-f0e5-3f4f-543fba22ac36,1.2.840.99999999.1.92590172.1715261421122,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.52103175.1715261421122,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e9a37ab6-c428-fd19-f6c5-72cd45acd77c,2010-03-12T21:17:31Z,61478f8f-7e82-d8a9-383d-0ae9eb77d34b,14d659d3-e6e3-68d1-b81e-e8820c2173ac,1.2.840.99999999.1.48965181.1268428651707,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42807765.1268428651707,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4bc6b054-60c5-39dc-b38d-ba77f48544bc,2015-04-03T22:59:25Z,61478f8f-7e82-d8a9-383d-0ae9eb77d34b,a91364df-f781-77f9-45e4-8a59d1bdc9cd,1.2.840.99999999.1.42280443.1428101965707,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.55868070.1428101965707,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ea9ce263-7355-2c03-18db-93c164bcf2c0,2018-03-28T02:46:49Z,61478f8f-7e82-d8a9-383d-0ae9eb77d34b,f622e1a1-ed06-1bc4-4c6e-047799529823,1.2.840.99999999.1.23960895.1522205209707,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24742242.1522205209707,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9989d629-f131-9e0c-c8ee-1ddb0e6d201d,2019-06-16T21:05:08Z,61478f8f-7e82-d8a9-383d-0ae9eb77d34b,a9e57e96-3f54-a5b7-4a2a-906852b8f4ea,1.2.840.99999999.1.89522349.1560719108707,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.48202894.1560719108707,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +937eb4f7-8d75-c97f-54e6-9874b58a00ed,2020-05-01T23:07:08Z,61478f8f-7e82-d8a9-383d-0ae9eb77d34b,557652b4-fb17-d65b-b274-3a7561864c87,1.2.840.99999999.1.18951889.1588374428707,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24365939.1588374428707,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +14feabd8-f53a-1769-494e-2780b6c70853,2016-06-29T04:40:05Z,2004b31b-42fb-ece9-d6b3-77f073846e6c,69d4938d-3437-34c9-7980-ac88db1faecf,1.2.840.99999999.1.52631357.1467175205242,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10814873.1467175205242,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7a83de76-a0a2-39c7-11fc-b41a83dfaa16,2017-03-15T04:09:19Z,2004b31b-42fb-ece9-d6b3-77f073846e6c,0f4a4016-39e5-318f-2713-51ad7fb2c1e9,1.2.840.99999999.1.11171454.1489550959242,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.78686467.1489550959242,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1f1a1645-e746-e60d-7b7d-8f893aa5a7bc,2022-04-13T05:13:16Z,2004b31b-42fb-ece9-d6b3-77f073846e6c,a92479fd-129c-cfd8-f0dc-89cbe7054ed1,1.2.840.99999999.1.82019578.1649826796242,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44430440.1649826796242,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b9a73310-d52e-89f6-1312-7b92024410b7,2015-04-08T05:56:22Z,06a2cf9c-d766-72ef-8869-449dd2b18534,2e7236fa-e04b-f0ab-00e1-4d89a84115df,1.2.840.99999999.1.40756476.1428472582000,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80573832.1428472582000,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8aeb8ab0-7dff-93b6-1ba5-4e78fc16c029,2017-04-19T05:57:46Z,06a2cf9c-d766-72ef-8869-449dd2b18534,4bccedf0-6953-38f8-7914-8267bc6fcc8f,1.2.840.99999999.1.87375963.1492581466000,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.21291501.1492581466000,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +161c4eae-52c3-ea77-2f90-e3e10d44271e,2018-04-25T05:36:51Z,06a2cf9c-d766-72ef-8869-449dd2b18534,23d275e4-9c5f-5665-1e57-1ce780392dae,1.2.840.99999999.1.34835605.1524634611000,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.74875213.1524634611000,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d4fba22a-2f0d-4420-7a5b-ed84b67d36c7,2020-05-06T05:31:18Z,06a2cf9c-d766-72ef-8869-449dd2b18534,edfc3754-0abf-a581-1f19-bce9c70df2aa,1.2.840.99999999.1.53499246.1588743078000,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.94166475.1588743078000,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +562c2d00-d09d-e16f-d8da-f1d18e82adc6,2020-08-13T08:21:08Z,7935fd9e-34bb-b8ba-c8bb-a613a89351cf,aa47d96a-3319-64e6-d2e0-bc8de08e083e,1.2.840.99999999.1.87242005.1597306868832,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.74430370.1597306868832,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +687c53c6-e762-bde0-3f97-4602cd4d3af6,2020-05-15T10:23:18Z,38d1c87e-1aa7-f721-ffd2-1372f96307de,ffb927d8-e52a-f51b-305a-723f8d9c00c4,1.2.840.99999999.1.77451305.1589538198877,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.76891986.1589538198877,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e6d231da-a2be-ef0a-5cab-23fdc7b26e23,2015-10-18T20:00:20Z,da3d8f26-d819-bb80-4540-a5f4a2fcfb2e,9e51cb73-b75a-6967-343e-106e4dae456a,1.2.840.99999999.1.28329900.1445198420228,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.70498958.1445198420228,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +e9af9242-1b2d-7846-e538-d54917987b1e,2016-09-05T20:33:10Z,da3d8f26-d819-bb80-4540-a5f4a2fcfb2e,0b9df3b2-2007-05ba-2c58-eada00179d25,1.2.840.99999999.1.45394756.1473107590228,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.63694064.1473107590228,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d035db3d-ef59-e472-010b-aec31e95e602,2018-09-17T21:17:26Z,da3d8f26-d819-bb80-4540-a5f4a2fcfb2e,d3364bd0-968f-1f40-d391-7d42d3e36e10,1.2.840.99999999.1.15279089.1537219046228,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.30721601.1537219046228,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7b281e63-b5dc-1f67-326b-d8af0c055675,2019-06-03T21:04:55Z,da3d8f26-d819-bb80-4540-a5f4a2fcfb2e,83c018a9-f6da-d355-d1cb-9bd1491e0363,1.2.840.99999999.1.49136461.1559595895228,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.54083737.1559595895228,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0650d69a-352c-bd1b-02f0-436fa08d474a,2024-03-04T21:36:30Z,da3d8f26-d819-bb80-4540-a5f4a2fcfb2e,0e2c8381-06bf-cc6c-a16f-c8751bd2d747,1.2.840.99999999.1.50510788.1709588190228,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86632629.1709588190228,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +12f42797-8895-3710-8ad1-0ce55814463d,2015-03-28T15:58:10Z,6055c1da-4d63-8ba6-8565-9aa4985bc019,b738c302-9698-df6a-1259-27daebfca60f,1.2.840.99999999.1.57323892.1427558290809,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.15992130.1427558290809,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +66dd2066-78a1-6bab-a4c0-ddf82ed0d2d8,2019-06-29T17:39:38Z,6055c1da-4d63-8ba6-8565-9aa4985bc019,268f16db-8863-abb1-afb3-66e891e3ae39,1.2.840.99999999.1.99007305.1561829978809,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.65269064.1561829978809,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1f59965a-ccbd-59e6-aec8-6b7fb9aae276,2019-11-16T18:22:37Z,6055c1da-4d63-8ba6-8565-9aa4985bc019,81132f1a-efa7-a77b-87c3-c76c0918554b,1.2.840.99999999.1.96538128.1573928557809,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42294922.1573928557809,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +16fd250c-69df-9f4f-8b5c-bfc9bf3ac526,2021-12-19T01:05:00Z,829eb3d9-54fd-2516-d17e-a0fb596e1946,7280f06e-013b-fb4f-91be-79e05421804d,1.2.840.99999999.1.19531121.1639875900384,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89060581.1639875900384,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +34caf1f1-bbc2-736d-c01a-082380627f7d,2017-07-10T07:09:08Z,3590b25f-c5a2-5645-b300-5b0a0e5e8b10,159593a0-434d-0b60-10bf-15e4b7d833c1,1.2.840.99999999.1.38703763.1499670548840,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10172888.1499670548840,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9f72a751-b5de-a818-e680-749d0f62e64b,2021-08-02T07:15:20Z,3590b25f-c5a2-5645-b300-5b0a0e5e8b10,6a23d243-2260-9b1e-91dd-2649085c89d1,1.2.840.99999999.1.73801164.1627888520840,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10809366.1627888520840,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a70758e3-3ae5-799a-b533-4b403d513f55,2022-08-08T07:23:51Z,3590b25f-c5a2-5645-b300-5b0a0e5e8b10,756c9678-e21e-b77f-0d97-0e5b65222f88,1.2.840.99999999.1.74551271.1659943431840,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.37085656.1659943431840,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +042937f8-e78e-91bd-2936-025ca2caee98,2022-09-07T17:39:57Z,d91eca41-c480-7938-7572-a7489d620c3c,e8587309-eced-81ce-2334-a095cf20ee7f,1.2.840.99999999.1.83338166.1662572397631,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.71607058.1662572397631,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6a372e7d-a88d-82fa-3d82-5f0d1ef5a731,2015-02-13T18:25:51Z,48e55523-3280-079c-249d-c8d408a2876c,1874ec80-c136-a0b0-08b0-0fa8e805fe75,1.2.840.99999999.1.87268482.1423851951278,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.10699463.1423851951278,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4bdf483c-2540-228c-8f1d-303d1a4b5675,2017-04-09T16:16:46Z,48e55523-3280-079c-249d-c8d408a2876c,a384260c-4352-76bd-c3d6-93c44fdbda9a,1.2.840.99999999.1.38451637.1491754606278,344001,Ankle,DX,Digital Radiography,1.2.840.99999999.1.1.21129505.1491754606278,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,19490002 +955062c6-fdbc-dec6-800c-3a447e70a2f4,2021-03-26T18:12:36Z,48e55523-3280-079c-249d-c8d408a2876c,392472f4-6df2-1d8d-a14a-05b2264e1303,1.2.840.99999999.1.53851840.1616782356278,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.11648745.1616782356278,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6e73f505-c704-4637-8c43-53bf5be132f2,2023-04-07T18:18:44Z,48e55523-3280-079c-249d-c8d408a2876c,a2936cf8-d486-0210-6f68-03278766733a,1.2.840.99999999.1.42405906.1680891524278,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.96293075.1680891524278,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6a5f4f5e-853b-de2c-e69b-4c997b99010d,1970-09-26T19:52:38Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,cae6a2e2-bf20-67f1-fc74-bde0905e97b9,1.2.840.99999999.1.36247781.23226758128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42162228.23226758128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +3d5b4126-9095-a6f6-c2a0-5775d18d96ae,1971-07-24T19:30:50Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,0c244515-8af9-47ec-5a3f-0aae2596b60c,1.2.840.99999999.1.15629543.49231850128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44652407.49231850128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +16763024-ee0c-209a-5978-0483c4321585,1971-12-18T19:27:07Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,232fb9da-a383-53ad-6292-de4dcd26502b,1.2.840.99999999.1.90127985.61932427128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.81796755.61932427128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +bb01d0c1-b3bd-ea9d-be92-36e5e8e97550,1972-01-15T20:11:46Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,26c6421c-d5e7-22e6-0cd4-c73b7a4b503c,1.2.840.99999999.1.37710175.64354306128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.55038130.64354306128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +578d1382-703b-f730-ec4a-08e6f7965b76,1974-02-09T20:28:02Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,e8381780-7b4c-930a-4fc3-e6e65184c127,1.2.840.99999999.1.11158836.129673682128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.62947732.129673682128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b714965d-adcd-304a-0f4e-c5d1b94f296f,1974-07-13T19:52:12Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,a78f5bbb-d7ee-d956-5432-840a540c7ee7,1.2.840.99999999.1.42459038.142977132128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.81191827.142977132128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +59b7b6bf-c81c-104d-5485-129fe87f7736,1975-04-12T19:38:14Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,6fd48172-7340-c9ae-d959-0835e4683c1a,1.2.840.99999999.1.78131023.166563494128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.77249691.166563494128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5441bbd0-22b6-419c-ac9f-8b4804e2204f,1975-06-14T20:03:07Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,50b68485-355d-3ca6-f1c6-ed48ecec4b2e,1.2.840.99999999.1.74749460.172008187128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.50942883.172008187128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b79e7fdd-50bc-1a01-f53d-b902ef78f6a1,1976-01-03T20:02:05Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,520bd3b2-51ac-d57c-54e0-83d64d3a30c5,1.2.840.99999999.1.92936545.189547325128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.75727586.189547325128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +36fd22bf-f62a-4f35-d9f4-fd77a8803d7d,1976-10-30T19:34:09Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,6f56d3b1-afe0-c625-bfb1-e77335ed846c,1.2.840.99999999.1.42683978.215552049128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.30648978.215552049128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +47a6043f-9709-36fe-83f9-61874d875eaa,1977-01-01T20:13:42Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,877a830e-c1c7-1122-4159-6bef967551a5,1.2.840.99999999.1.28487872.220997622128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.20133348.220997622128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +3a4d5f95-0f2b-a46c-e69c-fe1511de2c25,1977-07-30T20:10:54Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,85724729-12f4-a8ac-3e2c-9dfdbb6aaa14,1.2.840.99999999.1.95469017.239141454128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.27781336.239141454128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2a296f07-ff91-1a6a-f9e8-633bbd379c74,1978-03-18T20:06:22Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,9e271844-d0d7-6420-dd52-6becdc45963e,1.2.840.99999999.1.49195535.259099582128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58229970.259099582128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +49938277-2da9-c784-e139-ce86afc9e396,1979-03-24T19:58:34Z,08123544-5a97-8592-a7b7-f66e0aba6d5f,126a521f-b605-bcdb-0f71-084d56701248,1.2.840.99999999.1.88542526.291153514128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.65375592.291153514128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +316e145c-ff92-364d-e438-a4af51244f69,2015-03-09T10:30:43Z,8250f0c9-a533-d1c9-69fb-ec9096795ec3,dad88b79-832d-237c-3f59-e539e2a1e3f9,1.2.840.99999999.1.42388131.1425897043838,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99693611.1425897043838,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5b0b9ccc-b9dc-3233-9f36-01aaa9bff3da,2018-04-02T10:40:01Z,8250f0c9-a533-d1c9-69fb-ec9096795ec3,3979e063-5cb5-5e3d-3af3-7cab24ade954,1.2.840.99999999.1.12638607.1522665601838,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41715525.1522665601838,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2cd955c4-b016-610e-947b-e0ed1232bdfe,2001-11-16T23:57:30Z,473ff9e1-b1b8-36fc-0759-88dd9e3f3ad5,d0e2a5e7-b68d-81a2-4811-835af621af0e,1.2.840.99999999.1.40448670.1005955050807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.68363461.1005955050807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +00c2381b-635a-0f9d-e1e4-2ab838f05d57,2007-06-16T01:54:38Z,473ff9e1-b1b8-36fc-0759-88dd9e3f3ad5,91c8d815-3bfb-218c-5080-522121f06f4a,1.2.840.99999999.1.34087947.1181958878807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.22225313.1181958878807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1b3592cf-7f2c-5a2c-af45-e0acb44bae0f,2008-12-05T05:34:04Z,473ff9e1-b1b8-36fc-0759-88dd9e3f3ad5,a13f8d28-80e9-ffe9-7eda-9d673425e132,1.2.840.99999999.1.21992640.1228455244807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.56386071.1228455244807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +96c776e3-d993-3cad-70e6-f500dde613cc,2009-11-28T01:42:02Z,473ff9e1-b1b8-36fc-0759-88dd9e3f3ad5,20338f27-ffc1-becd-75a9-6857eab6087a,1.2.840.99999999.1.89595947.1259372522807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.11739533.1259372522807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +44caea75-0b0a-c9e8-49c6-c3d65ef20f9a,2021-04-12T10:28:44Z,8250f0c9-a533-d1c9-69fb-ec9096795ec3,629159a1-780c-4914-7ff0-79be10bc456e,1.2.840.99999999.1.51626815.1618223324838,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.51747018.1618223324838,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5334d7f6-2dc5-606f-79b2-69d5b8eca097,2024-04-29T10:21:59Z,8250f0c9-a533-d1c9-69fb-ec9096795ec3,bc5eb2f8-ca06-373b-bd9c-0d9caf7a45e1,1.2.840.99999999.1.38354843.1714386119838,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.69710362.1714386119838,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +565c38dd-be48-a13e-5fbb-6fcc25d4324d,2015-11-21T01:45:41Z,473ff9e1-b1b8-36fc-0759-88dd9e3f3ad5,2892ae1a-032b-a6c4-8c9e-952ae01b9814,1.2.840.99999999.1.48567089.1448070341807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.71557413.1448070341807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +89938797-c1d3-6094-2a77-ddc5815a4533,2015-05-02T15:26:36Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,368a6514-0e43-1c89-2596-6bbe854ce10e,1.2.840.99999999.1.96103847.1430580396990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.80922032.1430580396990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0e41dae7-d845-d00c-5b6f-e0b956cdd0b0,2018-02-10T15:48:42Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,e7a4c819-2fe4-2066-f65f-9c80b9056a5a,1.2.840.99999999.1.88728079.1518277722990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.36102661.1518277722990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c76ddb02-3376-d7c9-101a-211e03c80261,2019-07-06T14:36:25Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,45ae0811-a540-6437-cf27-40aaf70a2ba4,1.2.840.99999999.1.86750501.1562423785990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.48205034.1562423785990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2214188d-f34e-28c8-129f-d2c5fa61f001,2021-02-27T14:28:50Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,711da43f-75a8-b2c7-d308-b309593eab81,1.2.840.99999999.1.93361305.1614436130990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.60950631.1614436130990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +04b01098-6ca5-f143-fa58-a52e97c2c374,2021-03-27T15:35:05Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,9a42aadb-b6ae-af90-1056-753ffb3b5241,1.2.840.99999999.1.78247554.1616859305990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89144098.1616859305990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +21ac4dae-5a52-366f-a244-898b55d5ba88,2023-12-30T14:34:17Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,302b71ea-ace4-82d8-bb61-6bea0cf6ca01,1.2.840.99999999.1.97961624.1703946857990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95098773.1703946857990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b54adc40-1e6e-f012-4cb4-cced2a269b0e,2024-02-10T14:58:55Z,e9b464d2-ae1b-6210-654d-9bf3b0112c84,6176e240-7c42-1626-0b42-df59f6b8d8bb,1.2.840.99999999.1.78823890.1707577135990,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.40999227.1707577135990,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +caadf550-46af-1d72-182d-985fae7cb203,2015-05-27T20:53:10Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,7238a6c4-e841-9c9e-b3b0-9122a7cec5db,1.2.840.99999999.1.87724615.1432759990139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.28217512.1432759990139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +fa03dd01-6280-17b7-b6ec-605b470a5265,2017-06-07T20:57:32Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,5c5451f8-ec3a-0339-d098-afae05f1d952,1.2.840.99999999.1.58722611.1496869052139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.56056281.1496869052139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +57fbe50d-d38f-f951-10c1-ede162ce3380,2019-06-19T20:41:02Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,7e7fa251-4cba-5884-b316-dd0bb4c9d83f,1.2.840.99999999.1.52564554.1560976862139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.74744731.1560976862139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b7817852-da86-adc2-8d1d-7e80903dd5ee,2020-06-24T21:16:36Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,340a1a3e-1ac3-a043-c8eb-a3061fad1ffa,1.2.840.99999999.1.16382712.1593033396139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.76976015.1593033396139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +67c68571-e92c-743e-a54c-9b17e468afb0,2021-06-30T20:47:40Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,4ccb1819-47a1-6718-7b7e-7bf82fd2d41a,1.2.840.99999999.1.62122388.1625086060139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86329384.1625086060139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +fdb99ffd-8152-745f-4001-03df346adde6,2023-07-12T21:33:45Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,7d93d7a2-bb9a-19d3-644a-3545f5dfca90,1.2.840.99999999.1.58341283.1689197625139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41059230.1689197625139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b0eafa64-5979-dcc5-bf48-7c8685f53bf4,2024-07-17T20:23:39Z,86da3bcb-7279-ebe4-fdd6-3ccd73eb31fa,a7d51490-16e9-0c21-4dbd-0ba0162f48cb,1.2.840.99999999.1.12317541.1721247819139,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95622641.1721247819139,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +3779f666-2c76-c95e-d22e-fc2bb8c5898e,2015-04-04T09:14:09Z,11f5a32b-599b-eecb-003b-86f255a57ea1,085c8dd9-3ecd-9f60-1ebd-dc1621885181,1.2.840.99999999.1.82724119.1428138849574,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58269413.1428138849574,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e20bca80-271c-3238-5f88-7b39344c965c,2023-05-20T09:03:14Z,11f5a32b-599b-eecb-003b-86f255a57ea1,c88e76a6-d923-f24d-3dba-2ebf483b731d,1.2.840.99999999.1.14687998.1684573394574,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.59534754.1684573394574,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +bdef6a72-2af0-2680-f453-2439db53d0be,2018-12-15T01:29:52Z,20031069-a1df-c1cf-1188-b2de5855eef6,bcdacd24-6a57-fe65-2193-4f60ea6103c5,1.2.840.99999999.1.23333906.1544837392807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.54313124.1544837392807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8f50b238-ba12-cd9e-6fe1-c3d9025fca27,2022-01-01T01:26:13Z,20031069-a1df-c1cf-1188-b2de5855eef6,2450324e-d281-21be-b4a2-286bfb63df1d,1.2.840.99999999.1.33722734.1641000373807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.26910786.1641000373807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c40edede-63e3-9688-7f05-3ce15f9d8dc5,2022-07-23T01:37:11Z,20031069-a1df-c1cf-1188-b2de5855eef6,9e80b7be-4e50-fce0-73f4-c34b7d38d1e0,1.2.840.99999999.1.62443117.1658540231807,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.78806998.1658540231807,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2873719b-9af3-9d53-e952-b2ecdd2a15bd,1973-05-26T18:18:13Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,09a64a13-b7bf-608f-2196-1a4bc6313e8d,1.2.840.99999999.1.57039330.107288293128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.65959275.107288293128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +bff8024a-8351-8dd8-2080-5cb528a8648d,2015-04-06T18:11:17Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,da8d832f-6133-dc46-6aef-3985149f80e5,1.2.840.99999999.1.88972775.1428343877128,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.88640145.1428343877128,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +59de3b63-c070-74ea-f6df-cd1ed73a6e8b,2015-04-17T07:54:59Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,ba9b15cd-aab4-f066-b708-c10ff284820a,1.2.840.99999999.1.86208686.1429257299128,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.64038453.1429257299128,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +592bbd26-85b4-31a1-aae4-d786f04e9b61,2015-04-17T07:54:59Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,ba9b15cd-aab4-f066-b708-c10ff284820a,1.2.840.99999999.1.24553890.1429257299128,732167000,Entire chest and abdomen and pelvis (body structure),CT,Computed Tomography,1.2.840.99999999.1.1.99251907.1429257299128,1.2.840.10008.5.1.4.1.1.2,CT Image Storage,418023006 +204c4e07-788d-6e14-418d-81fc03c2b5f3,2015-04-17T11:04:45Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,eaeda403-c3da-8fd8-6409-e321b7d30b43,1.2.840.99999999.1.49616597.1429268685128,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.46574310.1429268685128,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +db5395df-6de5-4048-e3c1-9c79603dadda,2015-04-17T11:04:45Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,eaeda403-c3da-8fd8-6409-e321b7d30b43,1.2.840.99999999.1.86877524.1429268685128,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.73715236.1429268685128,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +dea201b1-deff-d201-3711-9bb8ed84d59d,2016-01-16T21:00:04Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,f9c6c419-9325-fea3-17a6-99b3e2ab6d0d,1.2.840.99999999.1.34898017.1452978004128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.65442687.1452978004128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +85cdafb7-85bc-e0f3-05cc-ff5eba2e7ff9,2017-01-21T19:46:08Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,efda7428-1699-f6f7-e7b9-a35f8a014282,1.2.840.99999999.1.47828614.1485027968128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.20648788.1485027968128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0f0fc3bd-b0bf-3192-65f8-53f32655cd8b,2017-09-25T00:51:53Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,54396f66-f027-4699-01dc-3ba73d1ff63b,1.2.840.99999999.1.76815758.1506300713128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95989382.1506300713128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6935c7c9-1895-2e0c-485c-3f78c1d9db40,2021-02-13T20:01:23Z,d9dad6b7-54d9-69f6-3ef0-81dbc6656f2b,930bc175-ff9a-7408-a1ae-c3362abae416,1.2.840.99999999.1.55027002.1613246483128,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42850017.1613246483128,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a9112eba-082a-875f-a7de-9e202d8a2d69,2015-03-05T12:46:08Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,583e521c-e7a6-8c21-73b6-26d096e5e2d9,1.2.840.99999999.1.34255133.1425559568898,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.27637789.1425559568898,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f8d1ed08-5ebf-03a5-4750-f6d06404f6f5,2019-03-28T12:34:09Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,3f41dad8-9a73-b464-01f8-858427cf5194,1.2.840.99999999.1.47207032.1553776449898,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.21888630.1553776449898,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +90d881b2-c41c-a58f-25be-2a87b4e2268a,2024-05-06T09:26:34Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,d144034f-e962-089a-ba88-b0330fc19ecb,1.2.840.99999999.1.42817541.1714987594898,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.28217244.1714987594898,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +980314a2-a30f-fb14-bfe1-76971a984a7a,2024-05-06T09:26:34Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,d144034f-e962-089a-ba88-b0330fc19ecb,1.2.840.99999999.1.47867954.1714987594898,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.55840936.1714987594898,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +b529720f-02d5-1d59-ee73-24ef82e48a24,2024-05-24T10:22:44Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,034f6538-810a-191a-64c9-dc02dc7bee6f,1.2.840.99999999.1.74802481.1716546164898,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.84834464.1716546164898,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +995188de-6a29-aaae-b846-75f2270037d2,2024-05-25T11:02:46Z,c5952114-d9a6-3dd1-21a7-53bf687dff85,034f6538-810a-191a-64c9-dc02dc7bee6f,1.2.840.99999999.1.92363981.1716634966898,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.77645554.1716634966898,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +bb009a83-0e4f-d58a-2155-63566ed915f0,2020-05-10T14:07:23Z,76ce01ae-b5bb-6e8d-9d26-f900352ab25c,0f641edc-d597-6930-4497-4f8b906aee8c,1.2.840.99999999.1.65110709.1589119643913,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.35733873.1589119643913,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ebc57079-28ca-8c75-a85b-c877fa39b48f,2022-11-01T16:39:58Z,32b2c1c6-8fb8-58e5-f610-0cd74a680126,cfdf7c64-9caa-aa4f-8c28-7e4f8c02528c,1.2.840.99999999.1.45506392.1667320798286,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.57462266.1667320798286,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +20a8691a-579a-1e98-5bfc-3b92192a1d43,2017-08-27T11:32:38Z,abe2cf92-3cfc-8116-05da-4909f51e2e01,7177c7c1-936f-b880-aa10-1ca27fad0fb6,1.2.840.99999999.1.22954743.1503833558369,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.64577746.1503833558369,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2f89fe71-7fb4-a734-7f29-4b871a31c31c,2019-09-08T11:39:04Z,abe2cf92-3cfc-8116-05da-4909f51e2e01,a3f10d9d-d7b8-ede8-7cad-636b4ea1837f,1.2.840.99999999.1.90903233.1567942744369,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.72283634.1567942744369,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +66452af5-87ea-acb2-df38-f3fd1ab824e5,2021-08-23T10:16:14Z,abe2cf92-3cfc-8116-05da-4909f51e2e01,8e8571f2-b656-3b09-e4c2-85f4d099d72d,1.2.840.99999999.1.14401433.1629713774369,8205005,Wrist,DX,Digital Radiography,1.2.840.99999999.1.1.29199703.1629713774369,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,60027007 +12123050-bf0a-ecbb-5371-d80aaa42fcaf,2023-10-01T12:00:02Z,abe2cf92-3cfc-8116-05da-4909f51e2e01,5dbfffa6-b82f-4d43-ea8e-abd6fe0517aa,1.2.840.99999999.1.21056188.1696161602369,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.13091758.1696161602369,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a60ddf72-cfea-358e-8bb0-bd0ee3ea6272,2016-10-10T04:25:29Z,d46755bb-a01d-8dcc-028c-24548ea1c102,891b3d77-d6f3-9c25-e99a-d20d4799f5d1,1.2.840.99999999.1.12287369.1476073529911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.78707859.1476073529911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +266a023a-722e-18c9-ae5a-bce078e38ce0,2020-11-09T05:10:39Z,d46755bb-a01d-8dcc-028c-24548ea1c102,c14c7cc5-b2d9-8225-3cb1-72be20742b7f,1.2.840.99999999.1.70640410.1604898639911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.43313947.1604898639911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c0037adb-48de-c30b-2d0c-4d0f3d16d825,2021-03-01T05:02:01Z,d46755bb-a01d-8dcc-028c-24548ea1c102,14b5791d-4440-eba5-e432-19e597d058eb,1.2.840.99999999.1.31237047.1614574921911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95642748.1614574921911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6dae5307-1dc9-3dcf-cc9e-d9124cac4117,2021-11-08T04:35:02Z,d46755bb-a01d-8dcc-028c-24548ea1c102,83c70f83-8a06-c269-cd4b-7683299dbd55,1.2.840.99999999.1.97900196.1636346102911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.13417702.1636346102911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +26c1e1eb-9290-d4c7-31be-20bb0517a170,2022-11-14T04:36:29Z,d46755bb-a01d-8dcc-028c-24548ea1c102,d03c9d0c-02ca-7518-6045-d8059125bb96,1.2.840.99999999.1.68993825.1668400589911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.35830412.1668400589911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +43e1df18-9330-e142-6f8b-b39dbcf1e431,2023-04-17T05:00:22Z,d46755bb-a01d-8dcc-028c-24548ea1c102,a0ff2ec1-0da2-f0c2-6963-8e918b73f405,1.2.840.99999999.1.22824595.1681707622911,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.63249809.1681707622911,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f2334840-f65c-ef0c-2d75-fff3b819e175,2021-07-11T20:36:58Z,1057debe-fd90-c813-fad8-0353ca3d8e6d,92784c28-0c83-0cef-6b69-01d7b4791a68,1.2.840.99999999.1.75632575.1626035818249,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.23182766.1626035818249,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +79a22bd9-0819-2728-bba0-f9f0c42e504f,2024-07-21T20:59:11Z,1057debe-fd90-c813-fad8-0353ca3d8e6d,01b56c57-2e92-d524-0d84-f6c62ad54bbf,1.2.840.99999999.1.97274277.1721595551249,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.45033915.1721595551249,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ca5fd70e-3b08-18d1-9b80-b1e8b406d456,2017-07-30T21:32:58Z,e6c36ed9-d552-75d1-7837-056c06f96e43,8f1970ad-8b56-9e71-afc1-185973eb5819,1.2.840.99999999.1.52326034.1501450378779,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99130637.1501450378779,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b5193dfa-d8fb-edc3-a0d0-e45c614901af,2021-08-08T21:10:44Z,e6c36ed9-d552-75d1-7837-056c06f96e43,1176f6de-d802-fb52-f13f-0a5a256ee37c,1.2.840.99999999.1.58894280.1628457044779,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44122038.1628457044779,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +bc1202da-145f-a4e1-1796-67b9073c459f,2015-04-09T09:13:58Z,b520bd18-30db-b093-7a03-2d934e442ee2,b189c81c-f7fb-84b0-25b4-97061b25ada6,1.2.840.99999999.1.42389638.1428570838814,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58194539.1428570838814,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7e65a55c-4884-903d-e521-bd1cf9cd65a9,2021-05-13T09:13:38Z,b520bd18-30db-b093-7a03-2d934e442ee2,9270b3a3-9d65-c69d-92f6-c86a20d813b6,1.2.840.99999999.1.91612039.1620897218814,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.31681346.1620897218814,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9476713d-d3f2-19da-04a7-ac4e63c3c39e,2017-05-10T11:07:45Z,117809db-9ede-b25e-f2e3-7f830939c0da,95861cff-78a1-5324-f7dd-ee90afe7ec43,1.2.840.99999999.1.76462482.1494414465260,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41153014.1494414465260,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0964c340-9f06-6f8c-deb3-c6cbb7bad425,2022-06-08T11:04:42Z,117809db-9ede-b25e-f2e3-7f830939c0da,005863bd-1b0a-2f90-f3c6-7ffc9c021d6a,1.2.840.99999999.1.22833590.1654686282260,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.21876488.1654686282260,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +997a6e1b-14a9-a032-b530-46140acbef12,2023-04-22T09:03:19Z,117809db-9ede-b25e-f2e3-7f830939c0da,e0622d44-76d8-5373-ef78-8a1ac75987d0,1.2.840.99999999.1.51302000.1682154199260,72696002,Knee,DX,Digital Radiography,1.2.840.99999999.1.1.93660014.1682154199260,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,74016001 +06bd606c-44dc-5870-a5c9-19fa55d64f5f,2020-08-12T13:12:51Z,1db29fb5-b3e3-d7bd-2b1e-1de97ab1976d,9f7775ef-ee54-e145-3697-f23428e88bd9,1.2.840.99999999.1.25812501.1597237971823,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99638070.1597237971823,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4611afd4-9c40-605d-f6d2-660e8583344c,2019-06-10T05:08:04Z,fc48b5c2-e6ee-e78f-2493-fafb04507fa4,172af365-81ed-75e0-6282-ebb7ba3f62b2,1.2.840.99999999.1.69005917.1560143284619,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.24696494.1560143284619,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e808e63a-e22f-c189-0efd-a9a33c15106d,2024-01-29T05:37:38Z,fc48b5c2-e6ee-e78f-2493-fafb04507fa4,995231f6-004d-29cd-c8e5-80a7f9448518,1.2.840.99999999.1.69961510.1706506658619,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.17816961.1706506658619,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +156bb2f0-da54-f68a-3f94-9ccbde82e9d5,2019-02-25T08:56:07Z,4daff133-9c6f-d305-b0d0-514495ae3b85,03c4d15b-2495-8c65-ce1c-6a06e51aed45,1.2.840.99999999.1.90065254.1551084967964,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44353131.1551084967964,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +23acf94d-f521-eac9-98d1-ec8cc197acde,2023-03-20T08:48:18Z,4daff133-9c6f-d305-b0d0-514495ae3b85,eb86a242-cda0-b391-df6a-7015340f5d18,1.2.840.99999999.1.30107769.1679302098964,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.48069744.1679302098964,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +32735314-3928-6f14-bc96-c50e3f98bfa2,2015-12-18T03:59:19Z,0d74120f-5a80-be1b-a91d-09bde798b179,09a289b9-6865-305b-f68e-9656949ed7b8,1.2.840.99999999.1.78165337.1450411159232,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.76197490.1450411159232,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e432c564-0f8e-c601-8386-d74bc148b2b0,2017-12-29T03:36:57Z,0d74120f-5a80-be1b-a91d-09bde798b179,fe252fa3-d4db-290a-5dff-adb8cb44c0bb,1.2.840.99999999.1.21540260.1514518617232,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.55347004.1514518617232,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b273cad5-558d-0567-560d-cb4b0d242450,2020-06-04T07:09:02Z,f5989d31-c2d3-0c33-e80c-eb73a1d4c5e0,84c2d03f-4df7-1959-bfb1-124a16114227,1.2.840.99999999.1.22286078.1591254542907,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.77486527.1591254542907,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +085eee64-10c5-51a3-cb2b-d5a1272380b5,2021-06-10T08:04:29Z,f5989d31-c2d3-0c33-e80c-eb73a1d4c5e0,cc551254-8c29-526c-b52d-f2fb54ad5a58,1.2.840.99999999.1.57536517.1623312269907,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.19297777.1623312269907,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a5ab10d2-16cd-d3fd-f7c4-fcc3e7c8268c,2022-11-10T05:53:04Z,f5989d31-c2d3-0c33-e80c-eb73a1d4c5e0,accf976c-d2a5-9cf3-fa5c-d5b84d6f367d,1.2.840.99999999.1.15124729.1668059584907,51299004,Clavicle,DX,Digital Radiography,1.2.840.99999999.1.1.69616593.1668059584907,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,168594001 +a13c3b92-af0f-8df1-149d-5730b1165501,2020-07-31T12:12:39Z,f264901f-b031-8e53-26f0-92ca046b9472,554541cd-858b-34b6-1399-c4e177fa38a3,1.2.840.99999999.1.17388255.1596197559534,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.31311921.1596197559534,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +fe026199-ca8b-f1c5-93e1-2ff7fc3e9d14,2022-08-12T12:11:10Z,f264901f-b031-8e53-26f0-92ca046b9472,e5ca2e94-8309-a276-3d39-e8b3395c3406,1.2.840.99999999.1.35549939.1660306270534,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.69314559.1660306270534,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1e293bd8-2545-b287-2b69-6417eb952466,2023-08-18T12:07:10Z,f264901f-b031-8e53-26f0-92ca046b9472,e78cc213-4d9a-da50-8a16-179842d4eaf9,1.2.840.99999999.1.40966275.1692360430534,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86326458.1692360430534,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1b6ffbde-33b9-ec26-b99f-ba7e16206465,2024-07-03T17:27:23Z,e149d1e7-f70f-5b21-2364-4cf1d8cfcf55,161095a5-c699-c3a2-dce7-528c49a3a823,1.2.840.99999999.1.89054287.1720027643913,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.79980012.1720027643913,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0edf218d-d8d4-a085-297c-50d672a43bb2,2015-05-25T06:23:49Z,87d42985-2ca4-9a8a-5bf1-764679754582,a23df524-5034-7b25-a7c0-5b27601736ea,1.2.840.99999999.1.55089692.1432535029896,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.57089340.1432535029896,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9c6cc290-08c7-7b8c-664f-f3fd70557a24,2021-06-07T06:28:14Z,87d42985-2ca4-9a8a-5bf1-764679754582,9118f3f4-b0b2-4298-fe79-d2668a0e3ef3,1.2.840.99999999.1.27642323.1623047294896,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.91591738.1623047294896,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7202867e-133f-56fa-760b-517f2c6cdf0a,2023-06-12T06:40:54Z,87d42985-2ca4-9a8a-5bf1-764679754582,f0f78666-9f28-c211-a3b4-2553d37d0fcc,1.2.840.99999999.1.33279467.1686552054896,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.69865856.1686552054896,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +64bfc599-7fcc-f522-c21a-5601c55c6ecd,2023-06-19T05:11:24Z,87d42985-2ca4-9a8a-5bf1-764679754582,17cb3695-07ff-710e-fcdf-4f6c50527f39,1.2.840.99999999.1.36489837.1687151484896,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.62063794.1687151484896,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ab2652c8-64e8-7e4f-817f-586d188ed0cf,2016-12-14T08:13:01Z,d4343da8-436d-1415-958d-29b30dcab38d,0e70bce1-1546-95a0-61fd-cdecddd67f3e,1.2.840.99999999.1.90508213.1481703181388,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.21079882.1481703181388,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +52d6d106-7f86-c503-b0eb-59bf959a8889,2019-07-17T08:16:47Z,d4343da8-436d-1415-958d-29b30dcab38d,b09f2aba-5632-9896-2c4d-d8a91a307c86,1.2.840.99999999.1.72473525.1563351407388,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.38579734.1563351407388,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1899bb71-9365-d4e1-8c0b-4a389d24ec5e,2020-07-29T08:04:54Z,d4343da8-436d-1415-958d-29b30dcab38d,fad9f694-5a6a-d821-73e9-06bc8c3c99a2,1.2.840.99999999.1.15371918.1596009894388,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.28626801.1596009894388,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +86f77bf4-91a4-8976-c2fa-5fcadf4c1406,2018-01-10T16:00:35Z,bd6a99b3-a9ca-77b3-ef7f-7a1fa019bce0,974dca9f-12e5-15c4-c934-dfb27994edeb,1.2.840.99999999.1.75186522.1515600035073,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.40807455.1515600035073,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7c1632f9-33dc-0e42-a2cf-fd98c6323fa7,2019-01-23T16:05:43Z,bd6a99b3-a9ca-77b3-ef7f-7a1fa019bce0,91f696c1-ff01-485d-cc20-f505036d96f5,1.2.840.99999999.1.35363160.1548259543073,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.60839222.1548259543073,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e501e3a3-cb3c-38d5-889f-e4f303ae8b1d,2020-01-29T15:50:28Z,bd6a99b3-a9ca-77b3-ef7f-7a1fa019bce0,69aec341-0619-c8e2-2b20-a4a224379b1a,1.2.840.99999999.1.81730120.1580313028073,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.33191529.1580313028073,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +13129589-13c7-3301-1785-aa01aeb2cbbb,2024-02-21T15:58:38Z,bd6a99b3-a9ca-77b3-ef7f-7a1fa019bce0,0064103d-74c2-5456-b0a7-74453e491a8e,1.2.840.99999999.1.16355414.1708531118073,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.82355840.1708531118073,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e3b1325f-d469-9b64-a76d-bfc7a3e12fb3,2016-10-01T16:06:38Z,52d81738-5ddb-209d-d07a-addb700acb34,2b6400b1-8d6b-2572-8e64-4057a3f8d099,1.2.840.99999999.1.35475070.1475337998562,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.96915634.1475337998562,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e11a858c-dd88-64a1-dc07-aec05a565631,1986-07-07T12:25:09Z,e1e9099f-8c87-6d57-6628-32cc0abfc363,da363da3-4aa5-2c33-8c75-dc126df815d8,1.2.840.99999999.1.98932645.521123109289,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.13255629.521123109289,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +15f3cdc4-339f-0d00-81b2-93a37521a104,2015-12-21T13:29:10Z,e1e9099f-8c87-6d57-6628-32cc0abfc363,d053630f-333a-ca62-d496-20ecafbfeb83,1.2.840.99999999.1.16675532.1450704550289,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86891551.1450704550289,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8f6de641-6125-7372-671d-bdf32e93fd32,2017-12-25T13:59:14Z,e1e9099f-8c87-6d57-6628-32cc0abfc363,9702f0f4-f393-c567-6fcf-3cadce526351,1.2.840.99999999.1.12967375.1514210354289,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.34125854.1514210354289,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +be868c7e-1624-fa86-6cb9-a680b9352f7a,2020-08-30T21:02:42Z,e1e9099f-8c87-6d57-6628-32cc0abfc363,aea3bfe4-6dcd-325b-b9f7-eefac7344b01,1.2.840.99999999.1.37467881.1598821362289,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.88731719.1598821362289,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0c7afa4f-afa3-3158-2b96-ee52b01b8034,2023-01-30T13:48:27Z,e1e9099f-8c87-6d57-6628-32cc0abfc363,a056b65d-1602-aa3c-c250-6f931578e7ad,1.2.840.99999999.1.95900888.1675086507289,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.93378055.1675086507289,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +51b05f60-3c59-6874-5387-7f02684d6c7c,2015-03-30T13:18:13Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,1e5221c7-6bee-a3da-7ccf-78445464477d,1.2.840.99999999.1.79968249.1427721493405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.82642085.1427721493405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +939c69a8-0b96-58d3-4b5d-920dd9d94b96,2016-04-04T12:26:19Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,f46150b6-49c8-da01-c5b8-0bb3e77d3cb1,1.2.840.99999999.1.89454654.1459772779405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89370046.1459772779405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c3637e69-11ee-45c8-b6c0-3516eee79cc8,2018-04-16T12:25:31Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,577efd27-5184-38b2-f83c-7f0b0bbb38cc,1.2.840.99999999.1.10719060.1523881531405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.95492692.1523881531405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a4969c08-8216-77ad-bf7b-3809a359ea10,2019-04-22T12:19:32Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,29442cff-fe0c-f547-a891-755d79ab7ea1,1.2.840.99999999.1.54043657.1555935572405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.26279993.1555935572405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +42a60731-a381-5a48-03f4-44f079590580,2022-05-09T12:54:30Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,d03101ec-bda2-edb7-6177-5bab6541e586,1.2.840.99999999.1.31120404.1652100870405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.73129844.1652100870405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4af7216d-40b9-7802-e4c9-543e6a38bdf6,2023-05-15T13:26:50Z,49f4ef1c-b11d-e9ae-721d-14afe29f7e3c,915c58ff-8cd8-c59b-075b-3951588a5728,1.2.840.99999999.1.28860832.1684157210405,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.73662332.1684157210405,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +9f6e5a0f-5a09-895e-8531-fa5a0f908b0c,2020-01-28T08:05:52Z,d438d707-ac82-55de-db74-74f5d0a6a0fb,30b1d4ae-8b12-fde3-ea51-30bc46d44561,1.2.840.99999999.1.87507338.1580198752247,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.31649501.1580198752247,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6648a883-8ee2-3cc3-d66e-9b93d8a9fb50,2023-02-14T09:01:34Z,d438d707-ac82-55de-db74-74f5d0a6a0fb,cea0ee45-0497-57c7-6c03-1f2f02cd7b06,1.2.840.99999999.1.18267490.1676365294247,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.28236921.1676365294247,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e2110730-af37-bcf3-106b-0707f521970b,2014-12-28T10:53:19Z,6f3fc86a-e316-f704-12de-d2a994a8f852,46ebf00c-3cae-14a0-e23d-d61cb55d3816,1.2.840.99999999.1.77017266.1419763999973,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.97160763.1419763999973,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a3b6181c-e2bb-c339-54a5-4d3faf7d8883,2022-02-13T11:03:27Z,6f3fc86a-e316-f704-12de-d2a994a8f852,875af028-7c73-51b9-291f-f02d030e7504,1.2.840.99999999.1.76910075.1644750207973,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.56470528.1644750207973,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +ab071c95-f82c-f365-60cf-be9aff5f0575,2023-02-19T10:46:54Z,6f3fc86a-e316-f704-12de-d2a994a8f852,d652d636-b916-3e0a-1c91-7526dd1b7697,1.2.840.99999999.1.76097705.1676803614973,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.54366714.1676803614973,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +63d98b9a-9c29-903d-1e34-30e41e1ce070,2016-08-02T20:15:45Z,466df3cc-4a2e-70a8-5ebb-29f1ce64763f,7675bb5b-7bda-6a58-6741-14fa16d3a7cd,1.2.840.99999999.1.23023632.1470168945810,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.60172755.1470168945810,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +0ee5ee35-26be-7c7e-980d-1aecc387bbe5,2019-06-18T19:58:04Z,466df3cc-4a2e-70a8-5ebb-29f1ce64763f,905cf6d0-4149-fdd6-74e9-c7cc8101e8a1,1.2.840.99999999.1.93663458.1560887884810,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.90733567.1560887884810,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +214b26e9-d20b-50d8-e9d7-95da85e7144e,2012-06-10T10:14:28Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,b3f18f28-984e-23f9-5823-395e5966c175,1.2.840.99999999.1.87264020.1339323268508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.60784714.1339323268508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +54a5ee9f-bfde-6009-dd27-9b63ef450012,2019-12-03T19:24:41Z,466df3cc-4a2e-70a8-5ebb-29f1ce64763f,9b2a8ab4-4e17-0afd-3f2c-5fc6bcb53a26,1.2.840.99999999.1.59222233.1575401081810,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.79588892.1575401081810,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +a4fa90fc-7b99-62e2-f5ea-b290297c70a6,2013-12-01T10:31:22Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,a82ea0eb-3537-1a46-ce72-d4f0ce847a55,1.2.840.99999999.1.89979139.1385893882508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.70580324.1385893882508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f032c770-2741-36b9-aa09-3978ab564ce7,2014-06-29T10:24:21Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,2e794654-1647-d708-02b1-91c0a3ac013d,1.2.840.99999999.1.29979362.1404037461508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.55964625.1404037461508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +21e6a0cc-baab-2792-ded3-ad78546570a6,2014-08-31T09:33:55Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,d49e8588-890b-0601-6074-9dc3263121af,1.2.840.99999999.1.36840285.1409477635508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.22171188.1409477635508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7791e13e-ef57-82ff-f403-2f9046efd4da,2015-06-14T10:25:20Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,fd022356-6372-62f5-eb32-a89db74b932d,1.2.840.99999999.1.56417501.1434277520508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.93781153.1434277520508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +da6cdf70-22ff-a608-122a-fb0399170a4a,2018-02-11T09:58:56Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,edf66976-eeb8-2109-aa13-1b283df2e0bc,1.2.840.99999999.1.47963097.1518343136508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.48735333.1518343136508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f0e9647b-bac9-e922-2092-b39e50a3a403,2018-09-09T10:32:20Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,720b8cf9-a1f7-bc06-0de3-ef8732bf4bb2,1.2.840.99999999.1.69586874.1536489140508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.32406079.1536489140508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +02c0f483-6110-d874-dc1b-581688d33ffd,2018-11-04T10:03:12Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,1839922f-76dd-7ba8-662d-e160e09cfd30,1.2.840.99999999.1.68121231.1541325792508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.51047424.1541325792508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +42562bf9-d210-ed9c-b1de-06fd399560d4,2020-03-08T10:32:32Z,e96e262b-bf69-4c3f-c26f-9acdaefaf770,483af20b-63cc-813e-4f3d-72980df298a9,1.2.840.99999999.1.21170159.1583663552508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.12111637.1583663552508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +64372f06-c9bb-bc95-4563-5ebda893653c,2015-01-08T18:10:48Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,98459dc6-e3eb-635f-22c0-4681e46bcbaa,1.2.840.99999999.1.64651831.1420740648095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.16473964.1420740648095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +53178136-5c59-3a53-613f-7326f2403114,2015-10-13T18:24:48Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,b5d21df3-8bd4-9ad4-711b-1b0466c66aa3,1.2.840.99999999.1.92370485.1444760688095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.39489621.1444760688095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +fc5d3894-7d7b-e2ea-9976-3110931a25e0,2016-10-19T18:48:37Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,5417fef2-47b9-8016-d4cf-c5dc11c9e7e9,1.2.840.99999999.1.88754790.1476902917095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.68290758.1476902917095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +5bf3e3ba-7c0b-8692-a13e-3f8772274423,2018-05-12T20:23:24Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,c4e7478a-e1d1-766d-f739-66bbbf78eccf,1.2.840.99999999.1.40075801.1526156604095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44015176.1526156604095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f518c079-62fb-c789-7bc5-d626f66137fc,2018-07-11T19:00:56Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,62b3c441-de5b-6065-e087-dd46412b721c,1.2.840.99999999.1.21115204.1531335656095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.19861194.1531335656095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +f9cd4d32-10bd-05dc-69ac-4672a8c4daad,2019-06-29T20:19:18Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,4aa35e1a-5032-3a2d-2908-90fa912f4e0a,1.2.840.99999999.1.37670872.1561839558095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.76149492.1561839558095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e41e6116-5eab-8737-8dfc-736c1797e462,2019-07-06T18:16:00Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,41b04385-dbe0-5054-dee3-fb2c402a0a9d,1.2.840.99999999.1.22139025.1562436960095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.18001724.1562436960095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +04d4a6f3-8218-551d-7a0e-a636be36bf28,2020-05-23T19:51:40Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,2cc02b3d-96c5-c262-5c00-d2d9e0302f63,1.2.840.99999999.1.82649531.1590263500095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.67674113.1590263500095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8a82e5ff-eb95-ae4c-f02e-91f992138998,2021-04-24T18:23:41Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,3df39236-ae82-776f-42d3-fcda8e38bbed,1.2.840.99999999.1.69334702.1619288621095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.35295000.1619288621095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +bd9276f5-19f9-f666-8a8c-8846c11932eb,2021-05-29T20:17:55Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,8fec8f99-5ae7-c544-3ba9-1da614520d20,1.2.840.99999999.1.14867342.1622319475095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89921636.1622319475095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e6a95985-da71-f5f2-487c-53c7e7b4d1b4,2021-06-26T19:46:49Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,9ce91034-50f0-a468-d558-b0535e3a321a,1.2.840.99999999.1.52516699.1624736809095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.73300105.1624736809095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +750c85a4-0f95-c921-aa59-4f389e0f5ed7,2021-08-21T19:54:13Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,17426457-25b8-2c35-81bb-dabaf62b83ba,1.2.840.99999999.1.85904718.1629575653095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.26981389.1629575653095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +93fbeaa5-a0a0-98fb-4029-1c2add71ccf5,2021-11-13T20:06:37Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,c665a229-55e4-3308-0783-f500e813e5f5,1.2.840.99999999.1.43522229.1636833997095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41627590.1636833997095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +1b6368eb-9935-2926-92b7-d3be90e9813b,2021-11-16T18:45:22Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,fb99ba5b-9a0c-f427-4007-fcc37c17e841,1.2.840.99999999.1.95044464.1637088322095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.44993505.1637088322095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +ad4ae1ce-ca46-85e0-33e0-9bd91339dae5,2021-12-18T19:58:56Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,d231d2af-9fca-989f-aae4-e606313c8518,1.2.840.99999999.1.93798328.1639857536095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.44949521.1639857536095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +947f3b37-63be-f680-dcdb-92bf6aebe962,2022-02-19T19:48:27Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,ca490cda-7afd-0c6d-d728-4339804e1c43,1.2.840.99999999.1.14379484.1645300107095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.62887487.1645300107095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +b6fc6cb0-6f9c-c6e1-86e4-b83d233e6862,2022-08-26T19:12:58Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,94bb01cf-a6d6-3f02-3c03-f81bdf77735a,1.2.840.99999999.1.60098724.1661541178095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.94255789.1661541178095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,433236007 +e0f413ba-a18f-ed0b-efd3-dd16941c0df0,2022-09-30T14:41:11Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,228cbd6b-16c8-b007-9d9c-2776229f7610,1.2.840.99999999.1.22168484.1664548871095,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.85131785.1664548871095,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +4b66e4c2-8595-9501-6d8b-e80c2f186e02,2022-09-30T14:41:11Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,228cbd6b-16c8-b007-9d9c-2776229f7610,1.2.840.99999999.1.34649979.1664548871095,732167000,Entire chest and abdomen and pelvis (body structure),CT,Computed Tomography,1.2.840.99999999.1.1.35337729.1664548871095,1.2.840.10008.5.1.4.1.1.2,CT Image Storage,418023006 +444a117f-6286-de4a-7546-10347a08615b,2022-10-30T14:25:45Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,3391f089-c775-3ad6-ee49-b6815c689902,1.2.840.99999999.1.14591807.1667139945095,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.40876020.1667139945095,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,105376000 +2a030464-615b-ca2a-6a6e-816a055fcfed,2022-12-10T19:55:06Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,90d95b85-5b6d-6eec-251e-200dcbcc3a2b,1.2.840.99999999.1.52578025.1670702106095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.33213259.1670702106095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +98734d45-b399-6491-15bf-6a6489f90ffd,2023-10-07T20:14:47Z,12c3795b-c935-f4d5-8a2c-2c2b381b90f7,2df7d6ea-6320-ecfa-95f8-3801763fb931,1.2.840.99999999.1.44536259.1696709687095,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86473609.1696709687095,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +98da55d7-78e9-cb22-c386-74c2547383c1,2017-12-15T12:09:07Z,b1886192-fd4d-8077-bb70-b07f5818bd6b,5125c67c-0a4a-301a-5263-5d7a5b615d2c,1.2.840.99999999.1.68495369.1513339747966,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.63266707.1513339747966,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +561baa58-392b-795f-e8f2-762528a504a8,2019-03-15T11:34:59Z,b1886192-fd4d-8077-bb70-b07f5818bd6b,4496aeee-e59e-3261-441f-b5b7e32ed38c,1.2.840.99999999.1.59512969.1552649699966,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.42533068.1552649699966,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +fd19132a-63ff-3943-526b-b351affb19ef,2015-08-24T22:10:16Z,56c7fc10-632f-3760-e98f-4b270c87360e,a5d58d83-16e3-035d-5069-f9266aea1a52,1.2.840.99999999.1.99958227.1440454216583,344001,Ankle,DX,Digital Radiography,1.2.840.99999999.1.1.11998334.1440454216583,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage,19490002 +b91eaada-130d-519d-4bfa-39c1913de3aa,2018-08-02T07:50:32Z,56c7fc10-632f-3760-e98f-4b270c87360e,dfe15369-4a38-a83b-f4a8-999c479aec6d,1.2.840.99999999.1.94667023.1533196232583,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.41526223.1533196232583,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +701dff85-411a-7719-0ad8-4fa32f8e665b,1954-04-15T08:38:19Z,ddd42265-8985-ee08-a0d6-56da944048d1,a3e5b49e-ac34-aa51-6050-631b664860c0,1.2.840.99999999.1.31160722.495904900166,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.87829621.495904900166,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d68db4e2-8ec4-aa4a-af48-c7c8c5586a0f,2015-08-11T21:32:51Z,ddd42265-8985-ee08-a0d6-56da944048d1,b4e0d5a1-bdcf-5b64-969a-e3d2f61afed6,1.2.840.99999999.1.98102583.1439328771834,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.32348120.1439328771834,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4f9c6a75-4768-2c9c-95a8-7c1c3849c6f9,2019-08-22T10:40:05Z,ddd42265-8985-ee08-a0d6-56da944048d1,60536e1a-ffb3-da7c-d620-0f1f487fd78c,1.2.840.99999999.1.97511949.1566470405834,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.73893884.1566470405834,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +2da943d0-b0d3-5bc3-2b2a-73696c78d359,2022-09-01T08:38:01Z,ddd42265-8985-ee08-a0d6-56da944048d1,51edee67-1da2-6e6f-ce75-23332bcf8ae3,1.2.840.99999999.1.97130750.1662021481834,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.99767469.1662021481834,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +7746c674-f6f4-6245-bb43-631b211c77c7,2022-09-15T10:16:55Z,ddd42265-8985-ee08-a0d6-56da944048d1,0ec887f0-c92b-d4d5-9c00-38a3fe8c4656,1.2.840.99999999.1.54693625.1663237015834,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.34656647.1663237015834,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6826eab2-cee4-bc42-c99a-40000bcebc58,2023-09-14T09:31:09Z,ddd42265-8985-ee08-a0d6-56da944048d1,196a587c-8eab-7bb4-e2a7-2b8d316fe004,1.2.840.99999999.1.55894773.1694683869834,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.97252794.1694683869834,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +f6941a5b-15f9-5bb5-8fe9-d28ef79d388c,2014-12-05T12:30:55Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,8ca96d31-9a34-29de-5bea-963baf89bdc4,1.2.840.99999999.1.73079652.1417782655641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.14984506.1417782655641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +61c79bd8-82c0-25f3-6936-e07a15592f84,2015-10-30T12:45:08Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,dc9365c0-0e9d-9a65-5795-3c65e2c1fb44,1.2.840.99999999.1.55937256.1446209108641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.64562020.1446209108641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +86a793e6-4414-a2b3-bdff-bb47206c59de,2016-11-04T13:09:06Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,989b1cf3-28de-470f-105a-4be9ea879c96,1.2.840.99999999.1.58483284.1478264946641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.19135847.1478264946641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +330bf1e5-a311-72f4-130f-771b1e3cfd69,2017-01-27T12:29:22Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,c13f52d7-2dd3-1cea-88c4-156a9d2705df,1.2.840.99999999.1.77446725.1485520162641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.90669224.1485520162641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +af51a37e-a47f-a5cd-4da1-9e1daaa74cb5,2018-02-23T12:15:38Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,7af5dc61-bc9a-c636-88a2-a8ccd04a1aeb,1.2.840.99999999.1.61640143.1519388138641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.66143664.1519388138641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8d91edb2-8225-62ac-683c-f5cc7c83086d,2018-11-16T12:38:21Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,e2272230-3fd6-8c75-17e6-5573d12748e7,1.2.840.99999999.1.54797088.1542371901641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.58809773.1542371901641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +e3aa5155-6c82-045d-8f28-a25e1746ab20,2019-10-11T12:14:49Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,6189f2f5-6625-3384-7a9a-464a7ad89122,1.2.840.99999999.1.10568835.1570796089641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.47652767.1570796089641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +26b54fb9-a7c9-9fd8-47b9-cb63caba7002,2023-12-15T12:22:59Z,2a19ac8c-076b-857f-48f9-5776d28cd7f8,fb919ee7-1176-5b37-cb06-0372aff9acfa,1.2.840.99999999.1.29215368.1702642979641,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.22389775.1702642979641,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +69fa56d4-1b96-b316-525b-7a801f0b9cb5,2005-12-18T10:20:14Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,b0a594c2-e33a-31d6-f082-d87e5d5542f4,1.2.840.99999999.1.39355603.1134901214508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.32934310.1134901214508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +d583efff-c43f-eec8-2b5b-9543dfa80b8f,2006-01-15T10:11:14Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,78677016-34bd-4ef6-921f-ff6268dd8f9e,1.2.840.99999999.1.15745935.1137319874508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.13994061.1137319874508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +c5bff821-42f4-2dcf-d732-028b6bac27bc,2006-06-18T10:06:04Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,fa86d12c-0b68-3c15-5e52-67b0e855e440,1.2.840.99999999.1.92107377.1150625164508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.68264281.1150625164508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +81e0bba0-11cf-af41-1eb3-1e8d47c52735,2006-06-25T09:09:02Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,8e4f8747-b141-e89a-3fd3-69b55f1f75d9,1.2.840.99999999.1.55830001.1151226542508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.64288261.1151226542508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +8317e34a-a657-ae82-7b02-d9d83c8b1900,2006-11-12T10:28:23Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,0961688f-36ec-0452-bd87-87b254ea266a,1.2.840.99999999.1.28388495.1163327303508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.73262548.1163327303508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +5be9cfd8-20d7-a133-ece9-4b700803330b,2008-04-27T10:20:41Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,6634f7bf-39a8-6d59-93dc-1f08512ffb6e,1.2.840.99999999.1.44703624.1209291641508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.18017255.1209291641508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +60411ed8-1292-85b7-db59-e7c1708baf90,2008-07-06T10:10:04Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,259f698c-9718-40f0-586c-d645d9bfb7a5,1.2.840.99999999.1.91486764.1215339004508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.40431167.1215339004508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +4003c1fe-79e8-2c75-6d48-5f4b9f4c8c23,2009-03-29T10:14:10Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,67e1f664-8967-cd21-ca8c-f83707470b22,1.2.840.99999999.1.93582910.1238321650508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.61212589.1238321650508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +01f96179-75a6-eda4-73c4-018d8a088852,2009-09-27T10:34:54Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,85c8fdd2-63c5-167c-ecab-04cf04735743,1.2.840.99999999.1.98619243.1254047694508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.89263013.1254047694508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +892c0240-16c4-ebc6-122f-80a812b1ac9e,2010-02-28T10:36:30Z,068d2ed4-b12c-e380-fe78-3d99eab488c2,9b42b0d0-a54a-2b7b-12b3-f450a8684ed4,1.2.840.99999999.1.77582531.1267353390508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.86355303.1267353390508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 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+ed65e091-00f3-d929-fe65-396233fe8436,2016-06-19T10:41:26Z,1e27f678-220f-8359-d925-5531615fee8f,b6f7debc-78e0-d663-5fd5-c89035b80a85,1.2.840.99999999.1.77723932.1466332886508,700016008,Structure of region of internal part of mouth (body structure),DX,Digital Radiography,1.2.840.99999999.1.1.47620134.1466332886508,1.2.840.10008.5.1.4.1.1.1.3,Digital Intra-Oral X-Ray Image Storage - For Presentation,241046008 +6e74a5c3-f058-b838-66ca-406488b63e3d,2016-07-17T01:18:00Z,1e27f678-220f-8359-d925-5531615fee8f,e2409264-5bb4-2392-1f22-45fc997f9d5b,1.2.840.99999999.1.32557051.1468718280508,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.32217493.1468718280508,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 +e438b74c-3af5-f838-ccba-23694f4c2602,2016-07-17T01:18:00Z,1e27f678-220f-8359-d925-5531615fee8f,e2409264-5bb4-2392-1f22-45fc997f9d5b,1.2.840.99999999.1.94889358.1468718280508,80891009,Heart structure (body structure),US,Ultrasound,1.2.840.99999999.1.1.68571958.1468718280508,1.2.840.10008.5.1.4.1.1.3.1,Ultrasound Multiframe Image Storage,40701008 +4f3120f6-f20d-033a-12ff-c6a755cedcc6,2016-08-12T13:24:05Z,1e27f678-220f-8359-d925-5531615fee8f,730d3582-4759-b768-6786-14c46b9f2e74,1.2.840.99999999.1.61384310.1471008245508,51185008,Thoracic structure (body structure),CR,Computed Radiography,1.2.840.99999999.1.1.33664074.1471008245508,1.2.840.10008.5.1.4.1.1.1.1,Digital X-Ray Image Storage – for Presentation,399208008 diff --git a/metasyn/distribution/string.py b/metasyn/distribution/string.py index d0372221..717efbf4 100644 --- a/metasyn/distribution/string.py +++ b/metasyn/distribution/string.py @@ -331,6 +331,12 @@ def _param_schema(cls): def default_distribution(cls): return cls(r"[ABC][0-9]{3,4}") + def information_criterion(self, values): + mean_len = values.str.len_chars().mean() + diff = ((values.str.len_chars() - mean_len)**2).mean() + if mean_len > 3 and diff < 1: + return -2 + return 0 @metadist(implements="core.regex", var_type="string") class UniqueRegexDistribution(UniqueDistributionMixin, RegexDistribution): diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 819c40d7..d18aa8d4 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -6,7 +6,7 @@ import pytest from pytest import mark -from metasyn.demo.dataset import _get_demo_class, demo_dataframe, demo_file +from metasyn.demo.dataset import _get_demo_class, demo_dataframe, demo_file, _AVAILABLE_DATASETS from metasyn.metaframe import MetaFrame from metasyn.provider import get_distribution_provider from metasyn.var import MetaVar @@ -117,7 +117,7 @@ def test_distributions(tmp_path): dataset.to_json(tmp_fp) @mark.parametrize( - "dataset_name", ["spaceship", "titanic", "fruit", "survey", "test"] + "dataset_name", list(_AVAILABLE_DATASETS) ) def test_demo_datasets(tmp_path, dataset_name): demo_fp = demo_file(dataset_name) From 789ae6314b013b7bd6cfe4867929f5000ceda429 Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Wed, 9 Oct 2024 15:55:36 +0200 Subject: [PATCH 04/17] Change the representation of MetaVar/MetaFrame (#331) * Change the representation of MetaVar/MetaFrame * Remove old code --- metasyn/metaframe.py | 9 ++++++--- metasyn/var.py | 2 ++ 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index 5114539e..de8486c6 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -318,9 +318,12 @@ def synthesize(self, n: Optional[int] = None) -> pl.DataFrame: def __repr__(self) -> str: """Return the MetaFrame as it would be output to JSON.""" - pretty_data = _jsonify(self.to_dict()) - output = json.dumps(pretty_data, indent=4) - return output + if len(self.meta_vars) <= 3: + var_str = ", ".join(var.__repr__() for var in self.meta_vars) + else: + var_str = ", ".join(var.__repr__() for var in self.meta_vars[:2]) + var_str += ", ..." + return f"MetaFrame: size = ({self.n_rows} x {self.n_columns}) <{var_str}>" def _jsonify(data): diff --git a/metasyn/var.py b/metasyn/var.py index 6ae081e7..e711822f 100644 --- a/metasyn/var.py +++ b/metasyn/var.py @@ -157,6 +157,8 @@ def __str__(self) -> str: f"- Proportion of Missing Values: {self.prop_missing:.4f}\n" f"- Distribution:\n{distribution_formatted}\n" ) + def __repr__(self) -> str: + return f"MetaVar <{self.name}, {self.distribution.implements}>" @classmethod def fit( From cd0e8ac3cc69feee6d8de9a829c501cb5f75a4bc Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Thu, 10 Oct 2024 11:05:03 +0200 Subject: [PATCH 05/17] Add export functionality to TOML in addition to JSON (#327) * Add support for simplified configuration file. * Rename methods: * MetaFrame.export -> MetaFrame.save * MetaFrame.to_json -> MetaFrame.save_json * MetaFrame.from_json -> MetaFrame.load_json * Add support for TOML GMF files. They work almost exactly the same as the JSON GMF files. Some comments are automatically included to make the file more easily understandable. --- .github/workflows/core-ci.yml | 6 +- README.md | 2 +- docs/paper/paper.md | 8 +- docs/source/developer/GMF.rst | 5 +- docs/source/developer/overview.rst | 2 +- docs/source/faq.rst | 2 +- docs/source/images/ClassDiagram.drawio | 64 +- docs/source/images/Diagrams_figma.jam | Bin 0 -> 619415 bytes .../images/flow_metadata_generation.png | Bin 53165 -> 52378 bytes .../images/flow_metadata_generation_code.png | Bin 59443 -> 58515 bytes .../images/flow_synthetic_data_generation.png | Bin 56147 -> 55269 bytes .../flow_synthetic_data_generation_code.png | Bin 60571 -> 60832 bytes docs/source/images/pipeline_basic.png | Bin 96271 -> 137582 bytes docs/source/images/pipeline_basic_legacy.png | Bin 203582 -> 0 bytes docs/source/images/pipeline_cli.png | Bin 155746 -> 0 bytes .../images/pipeline_cli_create_meta.png | Bin 171516 -> 102614 bytes .../source/images/pipeline_cli_synthesize.png | Bin 0 -> 108672 bytes .../images/pipeline_estimation_code.png | Bin 61202 -> 60426 bytes .../images/pipeline_estimation_simple.png | Bin 123167 -> 0 bytes docs/source/images/pipeline_func_names.png | Bin 147699 -> 140462 bytes .../images/pipeline_generation_simple.png | Bin 123056 -> 0 bytes .../images/pipeline_serialization_code.png | Bin 64310 -> 57175 bytes .../images/pipeline_serialization_simple.png | Bin 122965 -> 0 bytes docs/source/index.rst | 6 +- docs/source/metasyn_in_detail.rst | 11 +- docs/source/usage/cli.rst | 6 +- docs/source/usage/generating_metaframes.rst | 2 +- .../usage/generating_synthetic_data.rst | 4 +- docs/source/usage/quick_start.rst | 8 +- ...g_metaframes.rst => saving_metaframes.rst} | 28 +- docs/source/usage/usage.rst | 2 +- examples/basic_example.py | 7 +- examples/config_files/example_all.toml | 331 +++++++ .../{ => config_files}/example_config.toml | 0 examples/config_files/example_datafree.toml | 53 ++ examples/demonstration.csv | 892 ------------------ examples/getting_started.ipynb | 275 +++--- .../{ => gmf_files}/example_gmf_simple.json | 4 +- .../{ => gmf_files}/example_gmf_titanic.json | 4 +- metasyn/__init__.py | 2 +- metasyn/__main__.py | 25 +- metasyn/config.py | 77 +- metasyn/metaframe.py | 165 +++- metasyn/privacy.py | 13 + metasyn/provider.py | 5 +- metasyn/testutils.py | 118 +++ metasyn/util.py | 2 + metasyn/var.py | 12 +- metasyn/varspec.py | 35 +- pyproject.toml | 4 +- tests/data/bad_config.toml | 1 + tests/test_cli.py | 7 +- tests/test_dataset.py | 17 +- tests/test_toml.py | 57 ++ 54 files changed, 1066 insertions(+), 1196 deletions(-) create mode 100644 docs/source/images/Diagrams_figma.jam delete mode 100644 docs/source/images/pipeline_basic_legacy.png delete mode 100644 docs/source/images/pipeline_cli.png create mode 100644 docs/source/images/pipeline_cli_synthesize.png delete mode 100644 docs/source/images/pipeline_estimation_simple.png delete mode 100644 docs/source/images/pipeline_generation_simple.png delete mode 100644 docs/source/images/pipeline_serialization_simple.png rename docs/source/usage/{exporting_metaframes.rst => saving_metaframes.rst} (86%) create mode 100644 examples/config_files/example_all.toml rename examples/{ => config_files}/example_config.toml (100%) create mode 100644 examples/config_files/example_datafree.toml delete mode 100644 examples/demonstration.csv rename examples/{ => gmf_files}/example_gmf_simple.json (97%) rename examples/{ => gmf_files}/example_gmf_titanic.json (99%) create mode 100644 tests/data/bad_config.toml create mode 100644 tests/test_toml.py diff --git a/.github/workflows/core-ci.yml b/.github/workflows/core-ci.yml index c56a0fb1..6b65de97 100644 --- a/.github/workflows/core-ci.yml +++ b/.github/workflows/core-ci.yml @@ -21,9 +21,9 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install ".[check]" + python -m pip install ".[check,extra]" - name: Lint with Ruff - run: ruff check + run: ruff check metasyn - name: Check types with MyPy run: mypy metasyn @@ -61,7 +61,7 @@ jobs: run: | pip install git+https://github.com/sodascience/metasyn-disclosure-control pip install . - metasyn create-meta metasyn/demo/demo_titanic.csv --config examples/example_config.toml + metasyn create-meta metasyn/demo/demo_titanic.csv --config examples/config_files/example_config.toml build-docs: name: Build documentation diff --git a/README.md b/README.md index a13c82b6..ee03e0f0 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ __Generate synthetic tabular data__ in a transparent, understandable, and privacy-friendly way. Metasyn makes it possible for owners of sensitive data to create test data, do open science, improve code reproducibility, encourage data reuse, and enhance accessibility of their datasets, without worrying about leaking private information. -With metasyn you can __fit__ a model to an existing dataframe, __export__ it to a transparent and auditable `.json` file, and __synthesize__ a dataframe that looks a lot like the real one. In contrast to most other synthetic data software, we make the explicit choice to strictly limit the statistical information in our model in order to adhere to the highest privacy standards. +With metasyn you can __fit__ a model to an existing dataframe, __save__ it to a transparent and auditable `.json` file, and __synthesize__ a dataframe that looks a lot like the real one. In contrast to most other synthetic data software, we make the explicit choice to strictly limit the statistical information in our model in order to adhere to the highest privacy standards. ## Highlights - 👋 __Accessible__. Metasyn is designed to be easy to use and understand, and we do our best to be welcoming to newcomers and novice users. [Let us know](https://github.com/sodascience/metasyn/issues/new) if we can improve! diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 2cd0758e..3c3804cf 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -46,7 +46,7 @@ These choices enable the software to generate synthetic data with __privacy and At its core, `metasyn` has three main functions: 1. __Estimation__: Fit a generative model to a properly formatted tabular dataset, optionally with privacy guarantees. -2. __(De)serialization__: Create an intermediate representation of the fitted model for auditing, editing, and exporting. +2. __(De)serialization__: Create an intermediate representation of the fitted model for auditing, editing, and saving. 3. __Generation__: Synthesize new datasets based on a fitted model. ## Estimation @@ -117,11 +117,11 @@ After fitting a model, `metasyn` can transparently store it in a human- and mach } ``` -This `.json` can be manually audited, edited, and after exporting this file, an unlimited number of synthetic records can be created without incurring additional privacy risks. Serialization and deserialization with `metasyn` can be performed as follows: +This `.json` can be manually audited, edited, and after saving this file, an unlimited number of synthetic records can be created without incurring additional privacy risks. Serialization and deserialization with `metasyn` can be performed as follows: ```python -mf.export("fruits.json") -mf_new = MetaFrame.from_json("fruits.json") +mf.save("fruits.json") +mf_new = MetaFrame.load("fruits.json") ``` ## Data generation diff --git a/docs/source/developer/GMF.rst b/docs/source/developer/GMF.rst index 46abd544..75a7ed0c 100644 --- a/docs/source/developer/GMF.rst +++ b/docs/source/developer/GMF.rst @@ -1,7 +1,10 @@ Generative Metadata Format (GMF) ================================ -At the core of ``metasyn`` lies its ability to :doc:`generate `, :doc:`export` and :doc:`import` statistical metadata for a given dataset, which can then be used to :doc:`generate synthetic datasets `. To achieve this, ``metasyn`` uses the Generative Metadata Format (GMF), an open source format (available on `GitHub `_) designed to store statistical metadata for tabular datasets. The GMF standard is designed to be modular and extensible, with more distributions and privacy-enhancing mechanisms. Due to its open nature, GMF can be used by other software too. +At the core of ``metasyn`` lies its ability to :doc:`generate `, +:doc:`save` and :ref:`load` statistical metadata +for a given dataset, which can then be used to :doc:`generate synthetic datasets `. To achieve this, ``metasyn`` uses the Generative Metadata Format (GMF), an open source format (available on `GitHub `_) designed to store statistical metadata for tabular datasets. The GMF standard is designed to be modular and extensible, with more distributions and privacy-enhancing mechanisms. +Due to its open nature, GMF can be used by other software too. diff --git a/docs/source/developer/overview.rst b/docs/source/developer/overview.rst index ea22a1d7..fc28827e 100644 --- a/docs/source/developer/overview.rst +++ b/docs/source/developer/overview.rst @@ -15,7 +15,7 @@ The :class:`~metasyn.MetaFrame` class is a core component of the ``metasyn`` pac Essentially, a :obj:`~metasyn.MetaFrame` is a collection of :obj:`~metasyn.MetaVar` objects, each representing a column in a dataset. It contains methods that allow for the following: - **Fitting to a DataFrame**: The :meth:`~metasyn.MetaFrame.fit_dataframe` method allows for fitting a Polars DataFrame to create a :obj:`~metasyn.MetaFrame` object. This method takes several parameters including the DataFrame, column specifications, distribution providers, privacy level, and a progress bar flag. -- **Exporting and importing**: The :meth:`~metasyn.MetaFrame.export` method serializes and exports the :obj:`~metasyn.MetaFrame` to a JSON file, following the GMF format. The :meth:`~metasyn.MetaFrame.from_json` method reads a :obj:`~metasyn.MetaFrame` from a JSON file. +- **Saving and loading**: The :meth:`~metasyn.MetaFrame.save` method serializes and saves the :obj:`~metasyn.MetaFrame` to a JSON or TOML file, following the GMF format. The :meth:`~metasyn.MetaFrame.load` method reads a :obj:`~metasyn.MetaFrame` from a JSON or TOML file. - **Synthesizing to a DataFrame**: The :meth:`~metasyn.MetaFrame.synthesize` method creates a synthetic Polars DataFrame based on the :obj:`~metasyn.MetaFrame`. diff --git a/docs/source/faq.rst b/docs/source/faq.rst index 52a69d65..c471bd6f 100644 --- a/docs/source/faq.rst +++ b/docs/source/faq.rst @@ -14,7 +14,7 @@ A MetaFrame is a fitted model that describes the aggregate structure and charact Key elements encapsulated in a MetaFrame include variable names, their data types, the proportion of missing values, and the parameters of the distributions that these variables follow in the dataset. This information is sufficient to understand the overall structure and attributes of the data, without divulging the exact data points. -When a MetaFrame is created from an input dataset, it can be exported for auditing or manual editing. +When a MetaFrame is created from an input dataset, it can be saved for auditing or manual editing. In the ``metasyn`` workflow, once you have a MetaFrame, ``metasyn`` can generate synthetic data that aligns with the MetaFrame. This synthetic data shares the structural and distributional characteristics (as defined in the MetaFrame) with the original data but does not contain any actual data points from the original dataset, thus preserving privacy. diff --git a/docs/source/images/ClassDiagram.drawio b/docs/source/images/ClassDiagram.drawio index 34c663e8..bf938fe3 100644 --- a/docs/source/images/ClassDiagram.drawio +++ b/docs/source/images/ClassDiagram.drawio @@ -1,10 +1,10 @@ - + - + - + @@ -16,19 +16,19 @@ - + - + - + @@ -42,7 +42,7 @@ - + @@ -104,8 +104,8 @@ - - + + @@ -137,13 +137,13 @@ - + - + @@ -152,16 +152,16 @@ - + - + - + @@ -170,13 +170,13 @@ - + - + @@ -194,7 +194,7 @@ - + @@ -230,7 +230,7 @@ - + @@ -245,7 +245,7 @@ - + @@ -254,79 +254,79 @@ - + - + - + - + - + - + - + - + - + - + - + - + - + diff --git a/docs/source/images/Diagrams_figma.jam b/docs/source/images/Diagrams_figma.jam new file mode 100644 index 0000000000000000000000000000000000000000..12563d06a2416d20736eff4169c27232b58d5074 GIT 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zDyAqbb@gf~OZWHQ`G$-8S6}|WSt==|;rn#ha%~2N9~yUgW~FDZjyk5L9rZOL)wR3p z+rG6X^iDe+r*Dwcb?zr;*w)_TUBY%znZZ7bLvZ9 zLr*z@6lJ62Xb6mkz-S1JhQMeDjE2By2#kinXb6nV5O|RL^S_aX>Quf1mSLa}@O1Ta JS?83{1OOzQQPBVZ diff --git a/docs/source/index.rst b/docs/source/index.rst index e133e68d..6a2483f9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -38,16 +38,16 @@ Welcome to the `metasyn `_ documentatio 1. **Estimation**: ``Metasyn`` can **create a MetaFrame**, from a dataset. A MetaFrame is metadata describing a table, augmented with statistical information on the columns. It captures individual distributions and features and enables the generation of synthetic data based on it. 2. **Generation**: ``Metasyn`` can **generate synthetic data** based on a MetaFrame. The synthetic data produced solely depends on the MetaFrame, thereby maintaining a critical separation between the original sensitive data and the generated synthetic data. -3. **Serialization**: ``Metasyn`` can **export a MetaFrame** into an easy-to-read :doc:`/developer/GMF` file. This allows users to audit, understand, and modify their data generation model. These GMF files can also be imported back into Metasyn to generate synthetic data. +3. **Serialization**: ``Metasyn`` can **save a MetaFrame** into an easy-to-read :doc:`/developer/GMF` file. This allows users to audit, understand, and modify their data generation model. These GMF files can also be imported back into Metasyn to generate synthetic data. -Researchers and data owners can use ``metasyn`` to generate and share synthetic versions of their sensitive datasets, mitigating privacy concerns. Additionally, ``metasyn`` facilitates transparency and reproducibility by allowing the underlying MetaFrames to be exported and shared. Other researchers can use these to regenerate consistent synthetic datasets, validating published work without requiring sensitive data. +Researchers and data owners can use ``metasyn`` to generate and share synthetic versions of their sensitive datasets, mitigating privacy concerns. Additionally, ``metasyn`` facilitates transparency and reproducibility by allowing the underlying MetaFrames to be saved and shared. Other researchers can use these to regenerate consistent synthetic datasets, validating published work without requiring sensitive data. .. admonition:: Key Features - **MetaFrame Generation**: ``Metasyn`` allows the creation of a MetaFrame from a dataset provided as a `Polars `_ or `Pandas `_ DataFrame. MetaFrames include key characteristics such as *variable names*, *data types*, *percentage of missing values*, and *distribution parameters*. - - **Exporting MetaFrames**: ``Metasyn`` can export and import MetaFrames to GMF files. These are JSON files that follow the easy-to-read and understand :doc:`/developer/GMF`. + - **Saving MetaFrames**: ``Metasyn`` can save and load MetaFrames to GMF files. These are JSON files that follow the easy-to-read and understand :doc:`/developer/GMF`. - **Synthetic Data Generation**: ``Metasyn`` allows for the generation of a Polars DataFrame with synthetic data that resembles the original data. - **Distribution Fitting**: ``Metasyn`` allows for manual and automatic distribution fitting. - **Data Type Support**: ``Metasyn`` supports generating synthetic data for a variety of common data types including ``categorical``, ``string``, ``integer``, ``float``, ``date``, ``time``, and ``datetime``. diff --git a/docs/source/metasyn_in_detail.rst b/docs/source/metasyn_in_detail.rst index 0688f252..572f64e8 100644 --- a/docs/source/metasyn_in_detail.rst +++ b/docs/source/metasyn_in_detail.rst @@ -51,7 +51,7 @@ The three key stages of the ``metasyn`` pipeline include the **estimation** of t Estimation ^^^^^^^^^^^^^ -.. image:: /images/pipeline_estimation_simple.png +.. image:: /images/pipeline_estimation_code.png :alt: Metasyn Estimation Step in Pipeline :align: center @@ -147,9 +147,10 @@ From all those fits, the distribution with the lowest `AIC ` follow the :doc:`/developer/GMF`, a standard designed to be easy to read and understand. @@ -158,7 +159,7 @@ This allows for manual and automatic editing, as well as sharing. .. raw:: html

    - An example of an exported MetaFrame [click to expand]: + An example of a saved MetaFrame [click to expand]: .. code-block:: json @@ -268,12 +269,12 @@ This allows for manual and automatic editing, as well as sharing.
    .. note:: - See the :doc:`/usage/exporting_metaframes` page for information on *how* to export and load MetaFrame to and from JSON files. + See the :doc:`/usage/saving_metaframes` page for information on *how* to save and load MetaFrame to and from JSON files. Data generation ^^^^^^^^^^^^^^^^ -.. image:: /images/pipeline_generation_simple.png +.. image:: /images/pipeline_generation_code.png :alt: Metasyn Estimation Step in Pipeline :align: center diff --git a/docs/source/usage/cli.rst b/docs/source/usage/cli.rst index e002192a..0765ab6e 100644 --- a/docs/source/usage/cli.rst +++ b/docs/source/usage/cli.rst @@ -83,7 +83,7 @@ The ``metasyn`` CLI should now be up and running within the Docker container and Creating Generative Metadata ---------------------------- -The ``create-meta`` subcommand combines the :doc:`estimation ` and :doc:`serialization ` steps in the pipeline into one, this allows you to generate generative metadata for a tabular dataset (in CSV format), and store it in a GMF (Generative Metadata Format) file. +The ``create-meta`` subcommand combines the :doc:`estimation ` and :doc:`serialization ` steps in the pipeline into one, this allows you to generate generative metadata for a tabular dataset (in CSV format), and store it in a GMF (Generative Metadata Format) file. .. image:: /images/pipeline_cli_create_meta.png :alt: Creating Generative Metadata using the CLI @@ -158,9 +158,9 @@ The ``create-meta`` command also takes one optional argument: Generating Synthetic Data ------------------------- -The ``synthesize`` subcommand combines the :doc:`deserialization ` and :doc:`generation ` steps in the pipeline into one, and allows you to generate a synthetic dataset from a previously exported MetaFrame (stored as GMF file). +The ``synthesize`` subcommand combines the :doc:`deserialization ` and :doc:`generation ` steps in the pipeline into one, and allows you to generate a synthetic dataset from a previously saved MetaFrame (stored as GMF file). -.. image:: /images/pipeline_cli.png +.. image:: /images/pipeline_cli_synthesize.png :alt: Creating Synthetic Data from a GMF file using the CLI :align: center diff --git a/docs/source/usage/generating_metaframes.rst b/docs/source/usage/generating_metaframes.rst index 864be8e2..3599933a 100644 --- a/docs/source/usage/generating_metaframes.rst +++ b/docs/source/usage/generating_metaframes.rst @@ -3,7 +3,7 @@ Generating MetaFrames One of the main features of ``metasyn`` is to create a :obj:`MetaFrame `, an object which captures the essential aspects of the dataset, including variable names, types, data types, the percentage of missing values, and distribution attributes. :obj:`MetaFrame ` objects essentially capture all the information needed to generate a synthetic dataset that aligns with the original dataset. -.. image:: /images/pipeline_estimation_simple.png +.. image:: /images/pipeline_estimation_code.png :alt: MetaFrame Generation Flow :align: center diff --git a/docs/source/usage/generating_synthetic_data.rst b/docs/source/usage/generating_synthetic_data.rst index f081d3f8..add360ef 100644 --- a/docs/source/usage/generating_synthetic_data.rst +++ b/docs/source/usage/generating_synthetic_data.rst @@ -3,7 +3,7 @@ Generating Synthetic Data Metasyn can **generate synthetic data** from any :obj:`MetaFrame ` object. -.. image:: /images/pipeline_generation_simple.png +.. image:: /images/pipeline_generation_code.png :alt: Synthetic Data Generation :align: center @@ -16,7 +16,7 @@ The generated data does **not** preserve any relationships between variables. .. admonition:: Prerequisite - Before synthetic data can be generated, a :obj:`MetaFrame ` object must be :doc:`created ` or :doc:`loaded `. + Before synthetic data can be generated, a :obj:`MetaFrame ` object must be :doc:`created ` or :doc:`loaded `. To generate a synthetic dataset, simply call the :meth:`MetaFrame.synthesize(n) ` method on a :obj:`MetaFrame ` object. This method takes a parameter `n` which represents the number of rows of data that should be generated. By default (when `n` is not provided), metasyn tries to generate as many rows as in the original dataset. diff --git a/docs/source/usage/quick_start.rst b/docs/source/usage/quick_start.rst index 5112e077..bea7ef02 100644 --- a/docs/source/usage/quick_start.rst +++ b/docs/source/usage/quick_start.rst @@ -112,17 +112,17 @@ We can inspect the MetaFrame by simply printing it (``print(mf)``). This will pr Saving and Loading the MetaFrame -------------------------------- -The MetaFrame can be saved to a JSON file for future use, to do so we simply use the :func:`~metasyn.metaframe.MetaFrame.to_json` function on the MetaFrame (which in our case is named ``mf``), and pass in the filepath as a parameter. The following code saves the MetaFrame to a JSON file named "exported_metaframe.json": +The MetaFrame can be saved to a JSON file for future use, to do so we simply use the :func:`~metasyn.metaframe.MetaFrame.save` method on the MetaFrame (which in our case is named ``mf``), and pass in the filepath as a parameter. The following code saves the MetaFrame to a JSON file named "saved_metaframe.json": .. code-block:: python - mf.to_json("exported_metaframe.json") + mf.save("saved_metaframe.json") -Inversely, we can load a MetaFrame from a JSON file using the :func:`~metasyn.metaframe.MetaFrame.from_json` function, passing in the filepath as a parameter. To load our previously saved MetaFrame, we use the following code: +Inversely, we can load a MetaFrame from a JSON file using the :func:`~metasyn.metaframe.MetaFrame.load` method, passing in the filepath as a parameter. To load our previously saved MetaFrame, we use the following code: .. code-block:: python - mf = MetaFrame.from_json("exported_metaframe.json") + mf = MetaFrame.load("saved_metaframe.json") Synthesizing the Data --------------------- diff --git a/docs/source/usage/exporting_metaframes.rst b/docs/source/usage/saving_metaframes.rst similarity index 86% rename from docs/source/usage/exporting_metaframes.rst rename to docs/source/usage/saving_metaframes.rst index 18c65a79..55d3241f 100644 --- a/docs/source/usage/exporting_metaframes.rst +++ b/docs/source/usage/saving_metaframes.rst @@ -5,26 +5,27 @@ Exporting and importing MetaFrames =================================== -Metasyn can serialize and **export a MetaFrame** into a GMF file. GMF files are JSON files that follow the :doc:`/developer/GMF` and have been designed to be easy to read and understand. This allows users to audit, understand, modify and share their data generation model with ease. +Metasyn can serialize and **save a MetaFrame** into a GMF file. GMF files are JSON files that follow the :doc:`/developer/GMF` and have been designed to be easy to read and understand. This allows users to audit, understand, modify and share their data generation model with ease. -.. image:: /images/pipeline_serialization_simple.png +.. image:: /images/pipeline_serialization_code.png :alt: MetaFrame Serialization Flow :align: center + :width: 400px Exporting a MetaFrame ---------------------- -MetaFrames can be serialized and exported to a GMF file by calling the :meth:`metasyn.metaframe.MetaFrame.to_json` method on a :obj:`MetaFrame`. +MetaFrames can be serialized and saved to a GMF file by calling the :meth:`metasyn.metaframe.MetaFrame.save` method on a :obj:`MetaFrame`. -The following code exports a generated :obj:`MetaFrame` object named ``mf`` to a GMF file named ``exported_metaframe``. +The following code saves a generated :obj:`MetaFrame` object named ``mf`` to a GMF file named ``saved_metaframe``. .. code-block:: python - mf.to_json("exported_metaframe.json") + mf.save("saved_metaframe.json") .. raw:: html
    - An example of a MetaFrame that has been exported to a GMF file: + An example of a MetaFrame that has been saved to a GMF file: .. code-block:: json @@ -164,25 +165,28 @@ The following code exports a generated :obj:`MetaFrame` function on a :obj:`MetaFrame` object, and printing its output. +It is possible to preview the GMF file, without having to save it. This can be done by calling the Python built-in :func:`repr ` function on a :obj:`MetaFrame` object, and printing its output. .. code-block:: python gmf_preview = repr(mf) print(gmf_preview) + +.. _loading-a-metaframe: + Loading a MetaFrame ------------------- -You can load a MetaFrame from a GMF file using the :meth:`MetaFrame.from_json ` classmethod. +You can load a MetaFrame from a GMF file using the :meth:`MetaFrame.load ` classmethod. -The following code loads a :obj:`MetaFrame` object named ``mf`` from a GMF file named ``exported_metaframe``. +The following code loads a :obj:`MetaFrame` object named ``mf`` from a GMF file named ``saved_metaframe``. .. code-block:: python - mf = metasyn.MetaFrame.from_json("exported_metaframe.json") + mf = metasyn.MetaFrame.load("saved_metaframe.json") -Tweaking an exported MetaFrame +Tweaking an saved MetaFrame ----------------------------------- Since the JSON is formatted in an easy to read way (for both humans *and* computers), it is easy to manually edit the metadata, or to automatically edit the metadata using a script. @@ -231,7 +235,7 @@ Let's say we import a MetaFrame from the GMF (from earlier on this page) and use - audi - 87 -Well, what if we wanted to change the distribution of the ``fruits`` variable to instead be 30% ``apple``, 30% ``banana``, and introduce a new fruit ``orange`` with a distribution of 40%? We can do this by editing the ``probs`` and ``labels`` attributes of the ``fruits`` variable in the exported MetaFrame. The following is the edited MetaFrame: +Well, what if we wanted to change the distribution of the ``fruits`` variable to instead be 30% ``apple``, 30% ``banana``, and introduce a new fruit ``orange`` with a distribution of 40%? We can do this by editing the ``probs`` and ``labels`` attributes of the ``fruits`` variable in the saved MetaFrame. The following is the edited MetaFrame: .. tab:: GMF file before diff --git a/docs/source/usage/usage.rst b/docs/source/usage/usage.rst index 052013bf..e124609b 100644 --- a/docs/source/usage/usage.rst +++ b/docs/source/usage/usage.rst @@ -11,7 +11,7 @@ information on installation, quickstart, tutorials and information on the core f installation quick_start generating_metaframes - exporting_metaframes + saving_metaframes generating_synthetic_data config_files cli diff --git a/examples/basic_example.py b/examples/basic_example.py index 6541c6ba..aa781bd5 100644 --- a/examples/basic_example.py +++ b/examples/basic_example.py @@ -1,3 +1,5 @@ +from pathlib import Path + from metasyn import MetaFrame, demo_dataframe from metasyn.config import VarSpec @@ -14,12 +16,13 @@ mf = MetaFrame.fit_dataframe(df, var_specs=specs) # write to json -mf.export("example_gmf_simple.json") +gmf_path = Path("examples", "gmf_files", "example_gmf_simple.json") +mf.save(gmf_path) # then, export json from secure environment # outside secure environment, load json -mf_out = MetaFrame.from_json("example_gmf_simple.json") +mf_out = MetaFrame.load_json(gmf_path) # create a fake dataset df_syn = mf_out.synthesize(10) diff --git a/examples/config_files/example_all.toml b/examples/config_files/example_all.toml new file mode 100644 index 00000000..c4bfd61f --- /dev/null +++ b/examples/config_files/example_all.toml @@ -0,0 +1,331 @@ +config_version = "1.1" +dist_providers = ["builtin"] +n_rows = 100 + +[defaults] +data_free = true +prop_missing = 0.1 + +[[var]] +name = "DiscreteNormalDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.normal" +unique = false + +[var.distribution.parameters] +mean = 0 +sd = 1 + + +[[var]] +name = "DiscreteTruncatedNormalDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.truncated_normal" +unique = false + +[var.distribution.parameters] +lower = -1 +upper = 2 +mean = 0 +sd = 1 + + +[[var]] +name = "DiscreteUniformDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.uniform" +unique = false + +[var.distribution.parameters] +lower = 0 +upper = 10 + + +[[var]] +name = "PoissonDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.poisson" +unique = false + +[var.distribution.parameters] +rate = 0.5 + + +[[var]] +name = "UniqueKeyDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.unique_key" +unique = true + +[var.distribution.parameters] +lower = 0 +consecutive = false + + +[[var]] +name = "UniformDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.uniform" +unique = false + +[var.distribution.parameters] +lower = 0 +upper = 1 + + +[[var]] +name = "NormalDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.normal" +unique = false + +[var.distribution.parameters] +mean = 0 +sd = 1 + + +[[var]] +name = "LogNormalDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.lognormal" +unique = false + +[var.distribution.parameters] +mean = 0 +sd = 1 + + +[[var]] +name = "TruncatedNormalDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.truncated_normal" +unique = false + +[var.distribution.parameters] +lower = -1 +upper = 2 +mean = 0 +sd = 1 + + +[[var]] +name = "ExponentialDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.exponential" +unique = false + +[var.distribution.parameters] +rate = 1.0 + + +[[var]] +name = "MultinoulliDistribution" +var_type = "categorical" + +[var.distribution] +implements = "core.multinoulli" +unique = false + +[var.distribution.parameters] +labels = ["a", "b", "c"] +probs = [0.1, 0.3, 0.6] + + +[[var]] +name = "RegexDistribution" +var_type = "string" + +[var.distribution] +implements = "core.regex" +unique = false + +[var.distribution.parameters.regex_data] +regex = "[ABC][0-9]{3,4}" +counts = [1, 1, 1] + + +[[var]] +name = "UniqueRegexDistribution" +var_type = "string" + +[var.distribution] +implements = "core.regex" +unique = true + +[var.distribution.parameters.regex_data] +regex = "[ABC][0-9]{3,4}" +counts = [1, 1, 1] + + +[[var]] +name = "FakerDistribution" +var_type = "string" + +[var.distribution] +implements = "core.faker" +unique = false + +[var.distribution.parameters] +faker_type = "city" +locale = "en_US" + + +[[var]] +name = "UniqueFakerDistribution" +var_type = "string" + +[var.distribution] +implements = "core.faker" +unique = true + +[var.distribution.parameters] +faker_type = "city" +locale = "en_US" + + +[[var]] +name = "FreeTextDistribution" +var_type = "string" + +[var.distribution] +implements = "core.freetext" +unique = false + +[var.distribution.parameters] +locale = "en_US" +avg_sentences = 3 +avg_words = 6 + + +[[var]] +name = "DateUniformDistribution" +var_type = "date" + +[var.distribution] +implements = "core.uniform" +unique = false + +[var.distribution.parameters] +lower = "1903-07-15" +upper = "1940-07-16" + + +[[var]] +name = "TimeUniformDistribution" +var_type = "time" + +[var.distribution] +implements = "core.uniform" +unique = false + +[var.distribution.parameters] +lower = "10:39:36" +upper = "18:39:36" +precision = "seconds" + + +[[var]] +name = "DateTimeUniformDistribution" +var_type = "datetime" + +[var.distribution] +implements = "core.uniform" +unique = false + +[var.distribution.parameters] +lower = "2022-07-15T10:39:36" +upper = "2022-08-15T10:39:36" +precision = "seconds" + + +[[var]] +name = "ConstantDistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = 0.0 + + +[[var]] +name = "DiscreteConstantDistribution" +var_type = "discrete" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = 0 + + +[[var]] +name = "StringConstantDistribution" +var_type = "string" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = "text" + + +[[var]] +name = "DateTimeConstantDistribution" +var_type = "datetime" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = "2022-07-15T10:39:36" + + +[[var]] +name = "DateConstantDistribution" +var_type = "date" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = "1903-07-15" + + +[[var]] +name = "TimeConstantDistribution" +var_type = "time" + +[var.distribution] +implements = "core.constant" +unique = false + +[var.distribution.parameters] +value = "10:39:36" + diff --git a/examples/example_config.toml b/examples/config_files/example_config.toml similarity index 100% rename from examples/example_config.toml rename to examples/config_files/example_config.toml diff --git a/examples/config_files/example_datafree.toml b/examples/config_files/example_datafree.toml new file mode 100644 index 00000000..412dcf0a --- /dev/null +++ b/examples/config_files/example_datafree.toml @@ -0,0 +1,53 @@ +# Example toml file as input for metasyn + +config_version = "1.1" +dist_providers = ["builtin"] +n_rows = 100 + +[defaults] + +data_free = true +prop_missing = 0.1 + +[defaults.distribution] +discrete = {implements = "core.uniform", parameters = {lower = 1, upper = 30}} +continuous = {implements = "core.normal", parameters = {mean = 0, stdev = 1}} +string = {implements = "core.faker", parameters = {faker_type = "name", locale = "en_US"}} + +[[var]] +name = "PassengerId" +var_type = "discrete" +distribution = {implements = "core.unique_key", unique = true, parameters = {"consecutive" = true, lower=1}} # Notice booleans are lower case in .toml files. +prop_missing = 0.0 + +[[var]] +name = "Name" +description = "Name of the unfortunate passenger of the titanic." +distribution = {implements = "core.faker", parameters = {faker_type = "name", locale = "en_US"}} + +[[var]] +name = "Fare" +distribution = {implements = "core.exponential", parameters = {rate = 0.4}} + +[[var]] +name = "Age" +var_type = "discrete" +distribution = {implements = "core.uniform", parameters = {lower = 20, upper = 40}} + +[[var]] +name = "Cabin" +distribution = {implements = "core.regex", parameters = {regex_data = "[A-F][0-9]{2,3}"}} + +[[var]] +name = "Secret" +var_type = "discrete" + + +[[var]] +name = "Anotherone" +var_type = "string" + +[[var]] +name = "cats" +var_type = "categorical" +distribution = {implements = "core.multinoulli", parameters= {labels = ["male", "female"], probs=[0.45, 0.55]}} diff --git a/examples/demonstration.csv b/examples/demonstration.csv deleted file mode 100644 index e3fb9159..00000000 --- a/examples/demonstration.csv +++ /dev/null @@ -1,892 +0,0 @@ -PassengerId,Name,Sex,Age,Parch,Fare,Cabin,Embarked,Birthday,Board time,Married since,all_NA -1,"Braund, Mr. Owen Harris",male,22,0,7.25,,S,1937-10-28,15:53:04,2022-08-05 04:43:34, -2,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,0,71.2833,C85,C,,12:26:00,2022-08-07 01:56:33, -3,"Heikkinen, Miss. Laina",female,26,0,7.925,,S,1931-09-24,16:08:25,2022-08-04 20:27:37, -4,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,0,53.1,C123,S,1936-11-30,,2022-08-07 07:05:55, -5,"Allen, Mr. William Henry",male,35,0,8.05,,S,1918-11-07,10:59:08,2022-08-02 15:13:34, -6,"Moran, Mr. James",male,,0,8.4583,,Q,1923-06-02,16:28:33,2022-07-28 14:37:30, -7,"McCarthy, Mr. Timothy J",male,54,0,51.8625,E46,S,1928-07-02,17:20:04,2022-07-18 20:20:09, -8,"Palsson, Master. Gosta Leonard",male,2,1,21.075,,S,1918-03-12,17:56:40,2022-08-02 03:42:58, -9,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,2,11.1333,,S,1909-11-16,16:33:50,2022-08-07 04:06:24, -10,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,0,30.0708,,C,,17:28:21,2022-07-17 22:55:10, -11,"Sandstrom, Miss. Marguerite Rut",female,4,1,16.7,G6,S,1903-10-30,16:41:34,2022-07-24 19:18:18, -12,"Bonnell, Miss. Elizabeth",female,58,0,26.55,C103,S,1938-12-31,17:32:25,2022-07-23 03:46:49, -13,"Saundercock, Mr. William Henry",male,20,0,8.05,,S,1932-05-01,11:09:30,2022-07-27 10:47:33, -14,"Andersson, Mr. Anders Johan",male,39,5,31.275,,S,1933-03-06,14:16:15,2022-08-04 18:49:36, -15,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,7.8542,,S,1908-05-26,18:36:24,2022-08-10 22:17:32, -16,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,16.0,,S,1906-05-01,17:46:34,, -17,"Rice, Master. Eugene",male,2,1,29.125,,Q,1909-05-19,15:39:25,2022-08-06 22:46:12, -18,"Williams, Mr. Charles Eugene",male,,0,13.0,,S,1916-12-15,15:00:45,2022-07-24 10:54:46, -19,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,0,18.0,,S,1938-02-27,17:27:11,2022-08-15 03:39:59, -20,"Masselmani, Mrs. Fatima",female,,0,7.225,,C,1921-08-01,13:01:05,2022-08-09 03:06:51, -21,"Fynney, Mr. Joseph J",male,35,0,26.0,,S,1920-09-17,12:11:48,2022-07-31 12:30:35, -22,"Beesley, Mr. Lawrence",male,34,0,13.0,D56,S,1928-09-03,18:30:54,2022-07-15 12:58:08, -23,"McGowan, Miss. Anna ""Annie""",female,15,0,8.0292,,Q,1939-03-28,14:10:42,, -24,"Sloper, Mr. William Thompson",male,28,0,35.5,A6,S,1926-03-20,12:19:12,2022-07-25 06:31:32, -25,"Palsson, Miss. Torborg Danira",female,8,1,21.075,,S,1938-06-17,,2022-07-25 00:25:51, -26,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,5,31.3875,,S,,18:15:57,2022-08-10 05:31:28, -27,"Emir, Mr. Farred Chehab",male,,0,7.225,,C,1921-12-24,,2022-08-09 15:16:19, -28,"Fortune, Mr. Charles Alexander",male,19,2,263.0,C23 C25 C27,S,1934-06-08,11:47:08,2022-07-22 07:34:41, -29,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,7.8792,,Q,1938-03-20,,2022-07-17 12:20:31, -30,"Todoroff, Mr. Lalio",male,,0,7.8958,,S,1928-02-07,11:06:55,2022-07-17 11:29:11, -31,"Uruchurtu, Don. Manuel E",male,40,0,27.7208,,C,,11:18:08,2022-08-01 09:00:50, -32,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,0,146.5208,B78,C,1904-05-23,17:38:32,2022-08-12 00:23:17, -33,"Glynn, Miss. Mary Agatha",female,,0,7.75,,Q,1939-10-03,15:15:06,2022-07-26 20:12:13, -34,"Wheadon, Mr. Edward H",male,66,0,10.5,,S,1907-10-10,18:15:37,2022-07-16 14:14:18, -35,"Meyer, Mr. Edgar Joseph",male,28,0,82.1708,,C,1919-08-24,15:49:03,2022-07-19 00:44:15, -36,"Holverson, Mr. Alexander Oskar",male,42,0,52.0,,S,1939-05-24,17:33:36,2022-08-12 14:24:05, -37,"Mamee, Mr. Hanna",male,,0,7.2292,,C,1917-04-14,17:14:57,2022-08-08 22:40:30, -38,"Cann, Mr. Ernest Charles",male,21,0,8.05,,S,1934-11-16,11:51:09,2022-08-03 22:37:37, -39,"Vander Planke, Miss. Augusta Maria",female,18,0,18.0,,S,1914-04-19,15:05:30,2022-08-14 13:54:54, -40,"Nicola-Yarred, Miss. Jamila",female,14,0,11.2417,,C,1927-09-20,14:07:06,2022-07-18 02:10:45, -41,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,0,9.475,,S,1907-08-06,15:09:05,, -42,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,0,21.0,,S,1909-04-06,18:17:30,2022-08-11 18:57:36, -43,"Kraeff, Mr. Theodor",male,,0,7.8958,,C,1929-01-19,,2022-07-30 02:04:47, -44,"Laroche, Miss. Simonne Marie Anne Andree",female,3,2,41.5792,,C,1939-01-23,,2022-07-25 17:40:43, -45,"Devaney, Miss. Margaret Delia",female,19,0,7.8792,,Q,1910-09-23,18:36:50,2022-07-16 21:31:16, -46,"Rogers, Mr. William John",male,,0,8.05,,S,1913-06-12,10:48:51,2022-08-01 00:12:07, -47,"Lennon, Mr. Denis",male,,0,15.5,,Q,1908-08-05,13:24:32,2022-07-19 06:11:27, -48,"O'Driscoll, Miss. Bridget",female,,0,7.75,,Q,,16:00:34,2022-07-27 09:24:55, -49,"Samaan, Mr. Youssef",male,,0,21.6792,,C,1920-09-13,,2022-07-19 15:04:12, -50,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,0,17.8,,S,1907-07-30,13:47:45,2022-08-02 04:21:34, -51,"Panula, Master. Juha Niilo",male,7,1,39.6875,,S,1923-04-26,14:59:39,2022-07-21 06:35:49, -52,"Nosworthy, Mr. Richard Cater",male,21,0,7.8,,S,,18:15:31,2022-08-09 08:14:03, -53,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,0,76.7292,D33,C,1929-11-03,,2022-08-14 16:38:12, -54,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,0,26.0,,S,1930-11-19,,2022-08-04 07:10:22, -55,"Ostby, Mr. Engelhart Cornelius",male,65,1,61.9792,B30,C,1919-04-01,12:29:33,2022-08-13 13:54:25, -56,"Woolner, Mr. Hugh",male,,0,35.5,C52,S,1905-08-28,18:06:13,2022-08-04 18:15:31, -57,"Rugg, Miss. Emily",female,21,0,10.5,,S,1905-04-01,10:40:18,2022-07-23 20:19:47, -58,"Novel, Mr. Mansouer",male,28,0,7.2292,,C,,,2022-07-21 05:22:04, -59,"West, Miss. Constance Mirium",female,5,2,27.75,,S,1907-07-04,12:45:12,2022-07-22 14:30:14, -60,"Goodwin, Master. William Frederick",male,11,2,46.9,,S,1910-12-18,15:39:59,2022-07-26 03:24:34, -61,"Sirayanian, Mr. Orsen",male,22,0,7.2292,,C,1923-11-01,13:27:13,2022-07-23 20:39:22, -62,"Icard, Miss. Amelie",female,38,0,80.0,B28,,1918-11-02,11:37:34,2022-07-29 05:50:51, -63,"Harris, Mr. Henry Birkhardt",male,45,0,83.475,C83,S,1939-04-18,13:08:10,2022-08-08 21:01:36, -64,"Skoog, Master. Harald",male,4,2,27.9,,S,1922-12-21,16:39:09,2022-08-03 20:21:38, -65,"Stewart, Mr. Albert A",male,,0,27.7208,,C,1913-02-11,11:48:25,2022-07-23 13:46:04, -66,"Moubarek, Master. Gerios",male,,1,15.2458,,C,,11:20:44,2022-07-16 04:33:36, -67,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,10.5,F33,S,1912-03-27,14:31:49,2022-07-31 10:35:43, -68,"Crease, Mr. Ernest James",male,19,0,8.1583,,S,1904-11-07,13:24:11,2022-07-21 01:10:14, -69,"Andersson, Miss. Erna Alexandra",female,17,2,7.925,,S,1938-08-02,12:15:38,2022-07-26 13:20:55, -70,"Kink, Mr. Vincenz",male,26,0,8.6625,,S,1939-05-29,,2022-07-23 11:39:22, -71,"Jenkin, Mr. Stephen Curnow",male,32,0,10.5,,S,1918-12-28,14:40:44,2022-08-15 09:36:35, -72,"Goodwin, Miss. Lillian Amy",female,16,2,46.9,,S,1927-04-25,14:11:50,2022-07-27 15:43:59, -73,"Hood, Mr. Ambrose Jr",male,21,0,73.5,,S,1917-12-20,11:50:14,2022-08-08 03:54:50, -74,"Chronopoulos, Mr. Apostolos",male,26,0,14.4542,,C,1927-01-27,11:35:57,, -75,"Bing, Mr. Lee",male,32,0,56.4958,,S,1913-03-03,14:24:40,2022-07-19 11:26:13, -76,"Moen, Mr. Sigurd Hansen",male,25,0,7.65,F G73,S,1930-05-05,,2022-07-29 15:38:10, -77,"Staneff, Mr. Ivan",male,,0,7.8958,,S,1930-11-08,15:39:49,2022-07-18 07:39:55, -78,"Moutal, Mr. Rahamin Haim",male,,0,8.05,,S,1938-01-25,15:50:46,2022-07-26 11:25:26, -79,"Caldwell, Master. Alden Gates",male,1,2,29.0,,S,1922-06-09,15:00:24,2022-08-05 22:45:56, -80,"Dowdell, Miss. Elizabeth",female,30,0,12.475,,S,1913-04-10,18:38:14,2022-08-10 02:27:56, -81,"Waelens, Mr. Achille",male,22,0,9.0,,S,1925-07-10,15:40:23,, -82,"Sheerlinck, Mr. Jan Baptist",male,29,0,9.5,,S,1917-04-21,11:31:25,2022-08-15 09:31:02, -83,"McDermott, Miss. Brigdet Delia",female,,0,7.7875,,Q,1923-08-06,,2022-07-31 12:54:26, -84,"Carrau, Mr. Francisco M",male,28,0,47.1,,S,1918-03-28,10:55:34,, -85,"Ilett, Miss. Bertha",female,17,0,10.5,,S,1913-02-25,16:23:05,, -86,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,0,15.85,,S,1908-12-25,14:02:29,2022-08-10 17:00:53, -87,"Ford, Mr. William Neal",male,16,3,34.375,,S,1909-03-12,11:39:17,2022-08-10 17:51:51, -88,"Slocovski, Mr. Selman Francis",male,,0,8.05,,S,1905-08-24,15:29:07,2022-07-22 07:59:40, -89,"Fortune, Miss. Mabel Helen",female,23,2,263.0,C23 C25 C27,S,1937-06-27,11:33:48,2022-07-26 00:04:03, -90,"Celotti, Mr. Francesco",male,24,0,8.05,,S,1908-04-29,16:39:25,2022-08-11 20:14:00, -91,"Christmann, Mr. Emil",male,29,0,8.05,,S,1922-03-26,11:00:57,2022-07-23 20:48:59, -92,"Andreasson, Mr. Paul Edvin",male,20,0,7.8542,,S,1907-09-26,12:23:54,2022-08-13 01:59:15, -93,"Chaffee, Mr. Herbert Fuller",male,46,0,61.175,E31,S,1910-11-23,11:14:21,2022-07-24 11:37:37, -94,"Dean, Mr. Bertram Frank",male,26,2,20.575,,S,1917-04-18,14:53:19,2022-07-21 03:24:15, -95,"Coxon, Mr. Daniel",male,59,0,7.25,,S,,13:40:37,2022-08-12 01:16:53, -96,"Shorney, Mr. Charles Joseph",male,,0,8.05,,S,,,2022-08-03 04:01:43, -97,"Goldschmidt, Mr. George B",male,71,0,34.6542,A5,C,1939-11-11,16:19:26,, -98,"Greenfield, Mr. William Bertram",male,23,1,63.3583,D10 D12,C,1906-08-03,17:40:23,, -99,"Doling, Mrs. John T (Ada Julia Bone)",female,34,1,23.0,,S,1908-02-23,16:28:52,, -100,"Kantor, Mr. Sinai",male,34,0,26.0,,S,1905-09-14,15:34:05,2022-08-13 05:05:35, -101,"Petranec, Miss. Matilda",female,28,0,7.8958,,S,1929-09-06,12:02:26,2022-07-31 01:58:29, -102,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,7.8958,,S,1920-09-25,13:08:52,2022-07-22 20:59:45, -103,"White, Mr. Richard Frasar",male,21,1,77.2875,D26,S,1921-11-20,17:15:55,2022-08-01 09:52:31, -104,"Johansson, Mr. Gustaf Joel",male,33,0,8.6542,,S,1937-06-22,16:18:02,2022-07-29 21:07:32, -105,"Gustafsson, Mr. Anders Vilhelm",male,37,0,7.925,,S,1922-08-09,11:52:27,2022-07-31 05:03:11, -106,"Mionoff, Mr. Stoytcho",male,28,0,7.8958,,S,1930-06-29,14:21:59,2022-08-14 16:24:06, -107,"Salkjelsvik, Miss. Anna Kristine",female,21,0,7.65,,S,1928-10-06,12:32:52,2022-07-27 09:47:09, -108,"Moss, Mr. Albert Johan",male,,0,7.775,,S,1931-03-10,12:29:12,2022-08-12 17:24:54, -109,"Rekic, Mr. Tido",male,38,0,7.8958,,S,1910-05-05,17:54:22,2022-07-20 05:48:15, -110,"Moran, Miss. 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Augusta",female,30,0,31.0,,C,1906-09-16,11:15:05,2022-07-24 01:02:07, -844,"Lemberopolous, Mr. Peter L",male,34,0,6.4375,,C,1932-05-27,18:08:39,, -845,"Culumovic, Mr. Jeso",male,17,0,8.6625,,S,1925-04-22,12:03:39,, -846,"Abbing, Mr. Anthony",male,42,0,7.55,,S,,14:02:53,2022-07-31 04:10:32, -847,"Sage, Mr. Douglas Bullen",male,,2,69.55,,S,1904-08-29,13:08:15,, -848,"Markoff, Mr. Marin",male,35,0,7.8958,,C,1939-04-14,17:47:02,2022-08-09 01:53:59, -849,"Harper, Rev. John",male,28,1,33.0,,S,1905-06-20,14:19:47,2022-08-05 04:59:39, -850,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,0,89.1042,C92,C,1919-02-15,18:10:45,2022-07-29 07:54:52, -851,"Andersson, Master. Sigvard Harald Elias",male,4,2,31.275,,S,1922-12-06,,2022-07-17 16:16:07, -852,"Svensson, Mr. Johan",male,74,0,7.775,,S,1928-06-17,13:40:30,2022-08-04 10:45:32, -853,"Boulos, Miss. Nourelain",female,9,1,15.2458,,C,1908-02-05,11:12:58,2022-08-06 16:16:17, -854,"Lines, Miss. Mary Conover",female,16,1,39.4,D28,S,1916-04-20,13:05:42,2022-08-09 03:43:58, -855,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,0,26.0,,S,1938-09-02,13:39:12,2022-08-09 03:06:29, -856,"Aks, Mrs. Sam (Leah Rosen)",female,18,1,9.35,,S,1927-10-06,18:20:35,2022-08-14 23:58:10, -857,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,164.8667,,S,1908-11-15,12:55:23,2022-08-08 19:08:35, -858,"Daly, Mr. Peter Denis ",male,51,0,26.55,E17,S,1926-08-15,,2022-07-15 18:30:07, -859,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,3,19.2583,,C,1924-03-15,17:34:19,2022-08-08 22:16:32, -860,"Razi, Mr. Raihed",male,,0,7.2292,,C,1927-01-04,,2022-07-22 06:07:51, -861,"Hansen, Mr. Claus Peter",male,41,0,14.1083,,S,1914-04-20,18:38:11,2022-07-27 02:23:17, -862,"Giles, Mr. Frederick Edward",male,21,0,11.5,,S,1923-09-16,16:11:31,2022-08-11 12:10:55, -863,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,25.9292,D17,S,1918-06-11,13:24:06,2022-08-12 01:59:41, -864,"Sage, Miss. Dorothy Edith ""Dolly""",female,,2,69.55,,S,1922-06-20,,2022-08-05 23:57:19, -865,"Gill, Mr. John William",male,24,0,13.0,,S,1903-12-31,18:04:53,2022-08-12 17:56:13, -866,"Bystrom, Mrs. (Karolina)",female,42,0,13.0,,S,1905-06-17,17:55:39,, -867,"Duran y More, Miss. Asuncion",female,27,0,13.8583,,C,1908-04-17,14:07:05,2022-07-28 08:03:10, -868,"Roebling, Mr. Washington Augustus II",male,31,0,50.4958,A24,S,1924-05-15,14:04:48,2022-07-31 16:36:50, -869,"van Melkebeke, Mr. Philemon",male,,0,9.5,,S,1919-02-20,13:57:20,2022-07-19 04:52:58, -870,"Johnson, Master. Harold Theodor",male,4,1,11.1333,,S,1922-01-05,11:08:30,2022-07-15 19:00:57, -871,"Balkic, Mr. Cerin",male,26,0,7.8958,,S,1907-04-18,11:37:04,2022-08-12 07:11:23, -872,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,52.5542,D35,S,1906-09-22,,2022-08-06 23:54:08, -873,"Carlsson, Mr. Frans Olof",male,33,0,5.0,B51 B53 B55,S,1935-05-02,13:53:42,2022-08-03 08:57:03, -874,"Vander Cruyssen, Mr. Victor",male,47,0,9.0,,S,1923-02-25,11:06:07,2022-07-21 13:38:28, -875,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,0,24.0,,C,1930-08-29,15:26:37,2022-07-18 09:35:23, -876,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,7.225,,C,1924-10-01,17:31:01,2022-08-07 05:17:34, -877,"Gustafsson, Mr. Alfred Ossian",male,20,0,9.8458,,S,1908-12-07,13:58:03,2022-08-04 20:00:38, -878,"Petroff, Mr. Nedelio",male,19,0,7.8958,,S,1906-05-24,11:13:04,2022-08-13 15:51:37, -879,"Laleff, Mr. Kristo",male,,0,7.8958,,S,1922-11-19,10:43:39,2022-07-17 13:00:02, -880,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,1,83.1583,C50,C,1931-11-15,14:37:49,2022-07-19 19:15:57, -881,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,1,26.0,,S,1915-06-06,14:08:17,2022-08-15 06:11:18, -882,"Markun, Mr. Johann",male,33,0,7.8958,,S,1921-11-12,15:55:36,2022-07-18 04:28:15, -883,"Dahlberg, Miss. Gerda Ulrika",female,22,0,10.5167,,S,,15:36:36,2022-08-05 13:26:26, -884,"Banfield, Mr. Frederick James",male,28,0,10.5,,S,1934-09-04,16:10:24,2022-08-06 03:44:50, -885,"Sutehall, Mr. Henry Jr",male,25,0,7.05,,S,1938-03-07,17:36:02,2022-08-02 02:29:17, -886,"Rice, Mrs. William (Margaret Norton)",female,39,5,29.125,,Q,1923-05-27,15:55:39,, -887,"Montvila, Rev. Juozas",male,27,0,13.0,,S,1918-09-16,13:58:18,2022-08-02 17:29:12, -888,"Graham, Miss. Margaret Edith",female,19,0,30.0,B42,S,1911-04-28,11:43:34,2022-07-30 03:35:42, -889,"Johnston, Miss. Catherine Helen ""Carrie""",female,,2,23.45,,S,1939-03-18,14:41:36,, -890,"Behr, Mr. Karl Howell",male,26,0,30.0,C148,C,1905-04-16,13:37:08,2022-07-23 08:04:22, -891,"Dooley, Mr. Patrick",male,32,0,7.75,,Q,,17:21:32,2022-08-01 11:07:59, diff --git a/examples/getting_started.ipynb b/examples/getting_started.ipynb index f05fa9aa..fe13cedd 100644 --- a/examples/getting_started.ipynb +++ b/examples/getting_started.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "e4fae59f", "metadata": {}, "outputs": [], @@ -57,12 +57,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "2442cb34", "metadata": {}, "outputs": [], "source": [ "# import required packages\n", + "from pathlib import Path\n", + "\n", "import polars as pl\n", "\n", "from metasyn import MetaFrame, VarSpec, demo_file" @@ -86,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "3c2a44b7", "metadata": {}, "outputs": [ @@ -125,7 +127,7 @@ "└─────────────┴───────────────┴────────┴─────┴───┴────────────┴────────────┴──────────────┴────────┘" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -154,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "0a5a1aa2", "metadata": {}, "outputs": [ @@ -176,7 +178,7 @@ " ('all_NA', String)])" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -195,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "c72c2acb55fca193", "metadata": { "collapsed": false @@ -243,7 +245,7 @@ "└────────────┴─────────────┴────────────┴──────┴───┴────────────┴────────────┴────────────┴────────┘" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -288,12 +290,12 @@ "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 0/13 [00:00 **GMF files:**\n", "> GMF files are JSON files that follow the [Generative Metadata Format (GMF)](https://github.com/sodascience/generative_metadata_format), a format designed to contain statistical metadata for (tabular) datasets that has been designed to be easy to read and understand. This allows users to audit, understand, modify and share their data generation model with ease.\n", "\n", - "More information on exporting and importing MetaFrames can be found on the metasyn docs ['exporting and importing metaframes'](https://metasynth.readthedocs.io/en/latest/usage/exporting_metaframes.html) page." + "More information on saving and importing MetaFrames can be found on the metasyn docs ['saving and importing metaframes'](https://metasynth.readthedocs.io/en/latest/usage/saving_metaframes.html) page." ] }, { @@ -513,10 +515,10 @@ "metadata": {}, "outputs": [], "source": [ - "file_path = \"example_gmf_titanic.json\"\n", + "file_path = Path(\"gmf_files\", \"example_gmf_titanic.json\")\n", "\n", - "# Serialize and export the MetaFrame to a GMF file\n", - "mf.export(file_path)" + "# Serialize and save the MetaFrame to a GMF file\n", + "mf.save(file_path)" ] }, { @@ -536,7 +538,7 @@ "collapsed": false }, "source": [ - "It's also possible to preview how the exported file would look, without actually saving it to disk. This can be done as follows:" + "It's also possible to preview how the saved file would look, without actually saving it to disk. This can be done as follows:" ] }, { @@ -557,9 +559,9 @@ " \"provenance\": {\n", " \"created by\": {\n", " \"name\": \"metasyn\",\n", - " \"version\": \"1.0.1\"\n", + " \"version\": \"1.0.2.dev34+gd68929e\"\n", " },\n", - " \"creation time\": \"2024-07-11T12:04:20.936256\"\n", + " \"creation time\": \"2024-10-03T14:56:34.145956\"\n", " },\n", " \"vars\": [\n", " {\n", @@ -922,7 +924,7 @@ "id": "fcd16a5a", "metadata": {}, "source": [ - "A GMF file can be imported and loaded into a MetaFrame using the `MetaFrame.from_json()` class method, passing in the file path as a parameter. " + "A GMF file can be imported and loaded into a MetaFrame using the `MetaFrame.load()` class method, passing in the file path as a parameter. " ] }, { @@ -934,8 +936,8 @@ }, "outputs": [], "source": [ - "# Create a MetaFrame based on a GMF (.json) file\n", - "mf = MetaFrame.from_json(file_path)" + "# Create a MetaFrame based on a GMF (.json or .toml) file\n", + "mf = MetaFrame.load(file_path)" ] }, { @@ -999,29 +1001,29 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 13)
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    646"Line. Enter. Financial arrive.…"female"null0"8018"42.809746"E7""S"1934-09-2116:26:082022-08-13 16:23:17null
    324"Tax sit. Benefit. Money.""male"270"803170"22.745622null"S"1914-05-1918:07:532022-07-16 14:24:41null
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    694"Himself.""male"10"618407"22.897537null"S"1921-10-1917:23:48nullnull
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    5"Big. Financial.""female"500"506091"3.406364null"S"1915-05-27null2022-08-01 16:27:03null
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    2"Adult.""male"null0"25222"27.87715null"S"1936-06-2418:03:352022-07-26 01:42:15null
    3"Country result. Author. Benefi…"male"170"KIP.A. 34247"51.373905null"S"1913-02-1218:34:482022-08-06 06:34:19null
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    " ], "text/plain": [ "shape: (10, 13)\n", "┌─────────────┬──────────────┬────────┬──────┬───┬────────────┬────────────┬──────────────┬────────┐\n", "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", - "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ null │\n", + "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", "╞═════════════╪══════════════╪════════╪══════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", - "│ 1 ┆ Kathy ┆ male ┆ 33 ┆ … ┆ 1928-07-19 ┆ 17:40:17 ┆ 2022-07-23 ┆ null │\n", - "│ ┆ Crawford ┆ ┆ ┆ ┆ ┆ ┆ 02:35:47 ┆ │\n", - "│ 2 ┆ Toni ┆ female ┆ 37 ┆ … ┆ 1915-01-17 ┆ 17:06:54 ┆ 2022-07-29 ┆ null │\n", - "│ ┆ Rodriguez ┆ ┆ ┆ ┆ ┆ ┆ 08:50:29 ┆ │\n", - "│ 3 ┆ Jesse Lopez ┆ female ┆ 26 ┆ … ┆ 1935-10-16 ┆ 12:27:44 ┆ 2022-08-09 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 13:10:57 ┆ │\n", - "│ 4 ┆ Ashley ┆ female ┆ null ┆ … ┆ 1935-08-27 ┆ 14:44:39 ┆ 2022-08-12 ┆ null │\n", - "│ ┆ Wheeler ┆ ┆ ┆ ┆ ┆ ┆ 10:31:37 ┆ │\n", - "│ 5 ┆ Edward Jones ┆ female ┆ 27 ┆ … ┆ 1929-07-02 ┆ 13:57:05 ┆ 2022-07-23 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 09:33:00 ┆ │\n", - "│ 6 ┆ Scott Abbott ┆ female ┆ null ┆ … ┆ 1915-07-05 ┆ 14:30:59 ┆ 2022-08-07 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 17:07:11 ┆ │\n", - "│ 7 ┆ Samantha ┆ female ┆ 28 ┆ … ┆ 1938-11-28 ┆ 16:23:54 ┆ 2022-07-27 ┆ null │\n", - "│ ┆ Reyes ┆ ┆ ┆ ┆ ┆ ┆ 06:23:42 ┆ │\n", - "│ 8 ┆ Danielle ┆ male ┆ 31 ┆ … ┆ 1922-12-13 ┆ 15:33:10 ┆ 2022-07-21 ┆ null │\n", - "│ ┆ Chaney ┆ ┆ ┆ ┆ ┆ ┆ 04:47:26 ┆ │\n", - "│ 9 ┆ Rebecca Wade ┆ female ┆ 31 ┆ … ┆ 1939-07-10 ┆ 13:27:52 ┆ null ┆ null │\n", - "│ 10 ┆ Steven Rice ┆ male ┆ 23 ┆ … ┆ null ┆ 12:59:28 ┆ 2022-08-09 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 03:06:18 ┆ │\n", + "│ 1 ┆ Martin Smith ┆ male ┆ 27 ┆ … ┆ 1905-06-20 ┆ 15:20:36 ┆ 2022-07-29 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 02:33:29 ┆ │\n", + "│ 2 ┆ Jeremy Lee ┆ female ┆ 25 ┆ … ┆ 1915-11-18 ┆ 16:31:58 ┆ null ┆ null │\n", + "│ 3 ┆ Megan Wright ┆ female ┆ 21 ┆ … ┆ 1932-01-18 ┆ 10:58:57 ┆ 2022-08-12 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 10:10:27 ┆ │\n", + "│ 4 ┆ Jonathon ┆ male ┆ 34 ┆ … ┆ 1907-07-20 ┆ 13:08:41 ┆ 2022-07-26 ┆ null │\n", + "│ ┆ Ward ┆ ┆ ┆ ┆ ┆ ┆ 22:05:29 ┆ │\n", + "│ 5 ┆ Monica White ┆ male ┆ 25 ┆ … ┆ 1909-11-18 ┆ 13:53:40 ┆ 2022-07-23 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 14:19:42 ┆ │\n", + "│ 6 ┆ James Bryant ┆ male ┆ 39 ┆ … ┆ 1923-08-20 ┆ 10:42:54 ┆ 2022-07-24 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 17:42:18 ┆ │\n", + "│ 7 ┆ Patricia ┆ male ┆ null ┆ … ┆ 1909-08-29 ┆ 14:40:33 ┆ 2022-08-09 ┆ null │\n", + "│ ┆ Murphy ┆ ┆ ┆ ┆ ┆ ┆ 11:07:34 ┆ │\n", + "│ 8 ┆ Jeremy Kane ┆ female ┆ 37 ┆ … ┆ 1912-04-26 ┆ null ┆ 2022-08-10 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 13:27:33 ┆ │\n", + "│ 9 ┆ Matthew ┆ male ┆ 39 ┆ … ┆ null ┆ 18:36:30 ┆ 2022-07-20 ┆ null │\n", + "│ ┆ Baker ┆ ┆ ┆ ┆ ┆ ┆ 13:01:07 ┆ │\n", + "│ 10 ┆ Michael Cox ┆ male ┆ 38 ┆ … ┆ 1930-01-17 ┆ 14:44:28 ┆ null ┆ null │\n", "└─────────────┴──────────────┴────────┴──────┴───┴────────────┴────────────┴──────────────┴────────┘" ] }, @@ -1501,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "id": "0ba303f3", "metadata": {}, "outputs": [ @@ -1515,23 +1516,23 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    f64strcatf64f64strf64strcatdatetime[ms]timedatetime[μs]null
    446.0nullnull29.617080.075196null1.728378nullnull1921-10-25 02:59:33.06814:37:11.3172242872022-07-30 23:39:23.192874null
    " + "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
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    446.0nullnull29.528490.080808null1.633843nullnull1921-05-09 01:59:16.78314:41:29.5340768272022-07-31 02:46:03.870927null
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    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    u32u32u32u32u32u32u32u32u32u32u32u32u32
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    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
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    " ], "text/plain": [ "shape: (1, 13)\n", @@ -1611,11 +1612,11 @@ "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ u32 ┆ u32 ┆ u32 ┆ u32 ┆ ┆ u32 ┆ u32 ┆ u32 ┆ u32 │\n", "╞═════════════╪══════╪═════╪═════╪═══╪══════════╪════════════╪═══════════════╪════════╡\n", - "│ 0 ┆ 0 ┆ 0 ┆ 174 ┆ … ┆ 74 ┆ 83 ┆ 81 ┆ 891 │\n", + "│ 0 ┆ 0 ┆ 0 ┆ 185 ┆ … ┆ 87 ┆ 68 ┆ 93 ┆ 891 │\n", "└─────────────┴──────┴─────┴─────┴───┴──────────┴────────────┴───────────────┴────────┘" ] }, - "execution_count": 24, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1648,7 +1649,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "id": "e37faad4df8ffde8", "metadata": { "collapsed": false @@ -1658,7 +1659,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 13/13 [00:00<00:00, 14.49it/s]\n" + "100%|██████████| 13/13 [00:00<00:00, 24.19it/s]\n" ] } ], @@ -1695,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "id": "694d4474707f7950", "metadata": { "collapsed": false @@ -1725,7 +1726,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "id": "34a8aafc95c0219f", "metadata": { "collapsed": false @@ -1757,7 +1758,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "id": "3b2e873f7362160a", "metadata": { "collapsed": false @@ -1793,7 +1794,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "id": "ab2b34ed6b11578c", "metadata": { "collapsed": false @@ -1845,7 +1846,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.11.6" }, "vscode": { "interpreter": { diff --git a/examples/example_gmf_simple.json b/examples/gmf_files/example_gmf_simple.json similarity index 97% rename from examples/example_gmf_simple.json rename to examples/gmf_files/example_gmf_simple.json index 901acaef..b7cf5001 100644 --- a/examples/example_gmf_simple.json +++ b/examples/gmf_files/example_gmf_simple.json @@ -4,9 +4,9 @@ "provenance": { "created by": { "name": "metasyn", - "version": "0.8.1.dev0+ge481025.d20240325" + "version": "1.0.2.dev34+gd68929e" }, - "creation time": "2024-03-25T17:06:53.455575" + "creation time": "2024-10-01T09:57:15.595769" }, "vars": [ { diff --git a/examples/example_gmf_titanic.json b/examples/gmf_files/example_gmf_titanic.json similarity index 99% rename from examples/example_gmf_titanic.json rename to examples/gmf_files/example_gmf_titanic.json index 4a9f370c..776c3b5c 100644 --- a/examples/example_gmf_titanic.json +++ b/examples/gmf_files/example_gmf_titanic.json @@ -4,9 +4,9 @@ "provenance": { "created by": { "name": "metasyn", - "version": "1.0.2.dev14+gc102905.d20240830" + "version": "1.0.2.dev34+gd68929e" }, - "creation time": "2024-09-04T14:45:48.696499" + "creation time": "2024-10-03T14:56:33.372136" }, "vars": [ { diff --git a/metasyn/__init__.py b/metasyn/__init__.py index 8dca7f3c..4540dd88 100644 --- a/metasyn/__init__.py +++ b/metasyn/__init__.py @@ -7,7 +7,7 @@ information on the columns. It captures individual distributions and features and enables generation of synthetic data based on it. -2. Serialization and deserialization: Metasyn can export a +2. Serialization and deserialization: Metasyn can save a MetaFrame into an easy to read GMF file. This allows users to audit, understand, and modify their data generation model. These GMF files can also be imported back into Metasyn to generate synthetic data. diff --git a/metasyn/__main__.py b/metasyn/__main__.py index 3e7662e5..ad9e9d74 100644 --- a/metasyn/__main__.py +++ b/metasyn/__main__.py @@ -22,6 +22,7 @@ from metasyn.validation import create_schema EXAMPLE_CREATE_META="metasyn create-meta your_dataset.csv -o your_gmf_file.json --config your_config.toml" # noqa: E501 +EXAMPLE_CREATE_TOML="metasyn create-meta your_dataset.csv -o your_gmf_file.toml --config your_config.toml" # noqa: E501 EXAMPLE_SYNTHESIZE="metasyn synthesize your_gmf_file.json -o your_synthetic_file.csv" MAIN_HELP_MESSAGE = f""" @@ -31,7 +32,7 @@ Available subcommands: create-meta: - Create a intermediate metadata file (GMF/.json). This file can later be used to + Create a intermediate metadata file (GMF/.json/.toml). This file can later be used to create a new synthetic dataset with the `synthesize` subcommand. synthesize: Create a synthetic dataset from the intermediate metadata file (GMF). @@ -49,6 +50,7 @@ Example usage: {EXAMPLE_CREATE_META} +{EXAMPLE_CREATE_TOML} {EXAMPLE_SYNTHESIZE} @@ -84,13 +86,16 @@ def main() -> None: def create_metadata() -> None: - """Program to create and export metadata from a DataFrame to a GMF file (.json).""" + """Program to create and save metadata from a DataFrame to a GMF file (.json/.toml).""" parser = argparse.ArgumentParser( prog="metasyn create-meta", description=f"""Create a Generative Metadata Format file from a CSV file. This metadata file can then be used to create a synthetic dataset with the `synthesize` subcommand. -Example: {EXAMPLE_CREATE_META} +Examples: + +{EXAMPLE_CREATE_META} +{EXAMPLE_CREATE_TOML} """, formatter_class=RawDescriptionHelpFormatter, ) @@ -103,7 +108,7 @@ def create_metadata() -> None: ) parser.add_argument( "--output", "-o", - help="Metadata GMF output file: .json. This file can be used to synthesize data.", + help="Metadata GMF output file: .json/.toml. This file can be used to synthesize data.", type=pathlib.Path, default=None, required=False, @@ -130,14 +135,14 @@ def create_metadata() -> None: null_values=["", "na", "NA", "N/A", "Na"], ignore_errors=True) meta_frame = MetaFrame.fit_dataframe(data_frame, meta_config) - meta_frame.export(args.output) + meta_frame.save(args.output) def synthesize() -> None: """Program to generate synthetic data.""" parser = argparse.ArgumentParser( prog="metasyn synthesize", - description=f"""Synthesize data from a Generative Metadata Format .json file. + description=f"""Synthesize data from a Generative Metadata Format .json/.toml file. To create the metadata file from your dataset, use the `create-meta` subcommand. Example: {EXAMPLE_SYNTHESIZE} @@ -146,7 +151,7 @@ def synthesize() -> None: ) parser.add_argument( "input", - help="input file; typically .json adhering to the Generative Metadata Format", + help="input file; typically .json or .toml adhering to the Generative Metadata Format", type=pathlib.Path, ) parser.add_argument( @@ -174,12 +179,12 @@ def synthesize() -> None: if not args.preview and not args.output: parser.error("Output file is required if you are not using the preview option.") - # Create the metaframe from the json file + # Create the metaframe from the GMF file try: - meta_frame = MetaFrame.from_json(args.input) + meta_frame = MetaFrame.load(args.input) except json.JSONDecodeError as _err: print(f"Error: Unable to parse the file '{args.input}'.\n\n" - "Expecting a GMF/.json file as input.\n" + "Expecting a GMF/.json/.toml file as input.\n" "Did you perhaps provide your dataset?\n" "If so, please first create the metadata with the `create-meta` sub command.\n" "Otherwise your GMF file might be corrupted, and you should recreate it.") diff --git a/metasyn/config.py b/metasyn/config.py index 30d86cb7..59149d15 100644 --- a/metasyn/config.py +++ b/metasyn/config.py @@ -1,6 +1,7 @@ """Module defining configuration classes for creating MetaFrames.""" from __future__ import annotations +import warnings from pathlib import Path from typing import Iterable, Optional, Union @@ -9,9 +10,8 @@ except ImportError: import tomli as tomllib # type: ignore # noqa -from metasyn.privacy import BasePrivacy, BasicPrivacy, get_privacy from metasyn.provider import DistributionProviderList -from metasyn.varspec import VarSpec +from metasyn.varspec import DistributionSpec, VarDefaults, VarSpec class MetaConfig(): @@ -44,12 +44,15 @@ def __init__( self, var_specs: Union[list[dict], list[VarSpec]], dist_providers: Union[DistributionProviderList, list[str], str, None], - privacy: Optional[Union[BasePrivacy, dict]], - n_rows: Optional[int] = None): + defaults: Optional[dict] = None, + n_rows: Optional[int] = None, + config_version: str = "1.1"): self.var_specs = [self._parse_var_spec(v) for v in var_specs] self.dist_providers = dist_providers # type: ignore - self.privacy = privacy # type: ignore self.n_rows = n_rows + defaults = {} if defaults is None else defaults + self.defaults = VarDefaults(**defaults) + self.config_version = config_version @staticmethod def _parse_var_spec(var_spec): @@ -57,20 +60,6 @@ def _parse_var_spec(var_spec): return var_spec return VarSpec.from_dict(var_spec) - @property - def privacy(self) -> BasePrivacy: - """Return default privacy for generating the metaframe.""" - return self._privacy - - @privacy.setter - def privacy(self, privacy: Optional[Union[dict, BasePrivacy]]): - if privacy is None: - self._privacy: BasePrivacy = BasicPrivacy() - elif isinstance(privacy, dict): - self._privacy = get_privacy(**privacy) - else: - self._privacy = privacy - @property def dist_providers(self) -> DistributionProviderList: """Return the distribution provider list to be used for the metaframe.""" @@ -104,22 +93,32 @@ def from_toml(cls, config_fp: Union[str, Path]) -> MetaConfig: raise FileNotFoundError(f"It appears '{config_fp}' is not a valid filepath." f" Please provide a path to a .toml file to load a MetaConfig" f" from.") from fnf_error - except ValueError as value_error: - if Path(config_fp).suffix == ".toml": + except tomllib.TOMLDecodeError as value_error: + if Path(config_fp).suffix != ".toml": raise ValueError(f"It appears '{Path(config_fp).name}' is a" f" '{Path(config_fp).suffix}' file." f" To load a MetaConfig, " - f"provide it as a .toml file.") from value_error - raise ValueError(f"An error occured while parsing the configuration file \n" - f"('{Path(config_fp).name}').") from value_error + f"provide the configuration as a .toml file.") from value_error + raise value_error var_list = config_dict.pop("var", []) n_rows = config_dict.pop("n_rows", None) dist_providers = config_dict.pop("dist_providers", ["builtin"]) - privacy = config_dict.pop("privacy", {"name": "none", "parameters": {}}) + defaults = config_dict.pop("defaults", None) + privacy = config_dict.pop("privacy", None) + config_version = config_dict.pop("config_version", "1.0") + if config_version not in ["1.0", "1.1"]: + warnings.warn(f"Trying to read configuration file with version {config_version}, " + "this version of metasyn only supports 1.0 and 1.1.") + if privacy is not None: + if defaults is not None: + raise ValueError("Error parsing configuration file: cannot have both [privacy]" + " and [defaults] tables.") + defaults = {"privacy": privacy} if len(config_dict) > 0: raise ValueError(f"Error parsing configuration file '{config_fp}'." f" Unknown keys detected: '{list(config_dict)}'") - return cls(var_list, dist_providers, privacy, n_rows=n_rows) + return cls(var_list, dist_providers, defaults, n_rows=n_rows, + config_version=config_version) def to_dict(self) -> dict: """Convert the configuration to a dictionary. @@ -130,10 +129,10 @@ def to_dict(self) -> dict: Configuration in dictionary form. """ return { - "general": { - "privacy": self.privacy, - "dist_providers": self.dist_providers, - }, + "config_version": self.config_version, + "dist_providers": self.dist_providers, + "n_rows": self.n_rows, + "defaults": self.defaults, "var": self.var_specs } @@ -197,10 +196,20 @@ def __init__(self, var_spec: VarSpec, meta_config: MetaConfig): self.meta_config = meta_config def __getattribute__(self, attr): - if attr == "privacy": - if self.var_spec.privacy is None: - return self.meta_config.privacy - return self.var_spec.privacy + if attr in ["privacy", "data_free", "prop_missing", "privacy"]: + if getattr(self.var_spec, attr) is None: + return getattr(self.meta_config.defaults, attr) + return getattr(self.var_spec, attr) + if attr == "dist_spec": + if self.data_free and self.var_spec.dist_spec.implements is None: + if self.var_type not in self.meta_config.defaults.distribution: + raise ValueError( + f"Variable with name '{self.name}' is declared datafree and a distribution " + f"cannot be inferred. You can set the distribution for '{self.name}' or set" + f" a default distribution for variable type '{self.var_type}'.") + return DistributionSpec.parse(self.meta_config.defaults.distribution[self.var_type]) + return self.var_spec.dist_spec + if attr not in ("var_spec", "meta_config") and hasattr(self.var_spec, attr): return getattr(self.var_spec, attr) return super().__getattribute__(attr) diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index de8486c6..37078a5b 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -6,14 +6,22 @@ import pathlib from datetime import datetime from importlib.metadata import version -from typing import Any, Dict, List, Optional, Sequence, Union +from pathlib import Path +from typing import Any, Dict, List, Optional, Sequence, Union, no_type_check +from warnings import warn import numpy as np import polars as pl + +try: + import tomllib +except ImportError: + import tomli as tomllib # type: ignore # noqa + from tqdm import tqdm from metasyn.config import MetaConfig -from metasyn.privacy import BasePrivacy +from metasyn.privacy import BasePrivacy, get_privacy from metasyn.validation import validate_gmf_dict from metasyn.var import MetaVar from metasyn.varspec import VarSpec @@ -23,7 +31,7 @@ class MetaFrame(): """Container for statistical metadata describing a dataset. This class is used to fit a MetaFrame to a Polars DataFrame, serialize and - export the MetaFrame to a file, read a MetaFrame from a file, and create + save the MetaFrame to a file, read a MetaFrame from a file, and create a synthetic Polars DataFrame. A MetaFrame represents a metadata frame, which is a structure that holds @@ -104,13 +112,13 @@ def fit_dataframe( # noqa: PLR0912 elif isinstance(var_specs, MetaConfig): meta_config = var_specs elif var_specs is None: - meta_config = MetaConfig([], dist_providers, privacy) + meta_config = MetaConfig([], dist_providers, defaults = {"privacy": privacy}) else: - meta_config = MetaConfig(var_specs, dist_providers, privacy) + meta_config = MetaConfig(var_specs, dist_providers, defaults = {"privacy": privacy}) if dist_providers is not None: meta_config.dist_providers = dist_providers # type: ignore if privacy is not None: - meta_config.privacy = privacy # type: ignore + meta_config.defaults["privacy"] = privacy # type: ignore if df is not None and not isinstance(df, pl.DataFrame): if isinstance(df, (str, pathlib.Path)): @@ -227,36 +235,61 @@ def descriptions( for i_desc, new_desc in enumerate(new_descriptions): self[i_desc].description = new_desc - def export(self, fp: Optional[Union[pathlib.Path, str]], - validate: bool = True) -> None: - """Serialize and export the MetaFrame to a JSON file, following the GMF format. + def save(self, fp: Optional[Union[pathlib.Path, str]], validate: bool = True) -> None: + """Serialize and save the MetaFrame to a JSON or TOML file, following the GMF format. - Optionally, validate the exported JSON file against the JSON schema(s) included in the - package. + Optionally, validate the saved JSON file against the JSON schema(s) included in the + package. A TOML cannot be validated against a schema currently. Parameters ---------- fp: File to write the metaframe to. validate: - Validate the JSON file with a schema. + Validate the JSON file with a schema. If the file is a TOML file, then this will + be ignored. """ - self_dict = _jsonify(self.to_dict()) - if validate: - validate_gmf_dict(self_dict) if fp is None: - print(json.dumps(self_dict, indent=4)) + self.save_json(fp, validate) + return + fp_path = Path(fp) + if fp_path.suffix == ".toml": + self.save_toml(fp, validate) else: - with open(fp, "w", encoding="utf-8") as f: - json.dump(self_dict, f, indent=4) + self.save_json(fp, validate) + + @classmethod + def load(cls, fp: Union[pathlib.Path, str], validate: bool = True) -> MetaFrame: + """Read a MetaFrame from a JSON or TOML GMF file. + + Optionally, validate the saved JSON file against the JSON schema(s) included in the + package. A TOML cannot be validated against a schema currently. + + Parameters + ---------- + fp: + Path to read the data from. + validate: + Validate the JSON file with a schema. If the file is a TOML file, then this will + be ignored. - def to_json(self, fp: Union[pathlib.Path, str], - validate: bool = True) -> None: - """Serialize and export the MetaFrame to a JSON file, following the GMF format. + Returns + ------- + MetaFrame: + A restored MetaFrame from the file. + """ + fp_path = Path(fp) + if fp_path.suffix == ".toml": + return cls.load_toml(fp, validate) + else: + return cls.load_json(fp, validate) - This method is a wrapper and simply calls the 'export' function. - Optionally, validate the exported JSON file against the JSON schema(s) included in the + def save_json(self, fp: Optional[Union[pathlib.Path, str]], + validate: bool = True) -> None: + """Serialize and save the MetaFrame to a JSON file, following the GMF format. + + Optionally, validate the saved JSON file against the JSON schema(s) included in the package. Parameters @@ -266,11 +299,17 @@ def to_json(self, fp: Union[pathlib.Path, str], validate: Validate the JSON file with a schema. """ - self.export(fp, validate) + self_dict = _jsonify(self.to_dict()) + if validate: + validate_gmf_dict(self_dict) + if fp is None: + print(json.dumps(self_dict, indent=4)) + else: + with open(fp, "w", encoding="utf-8") as f: + json.dump(self_dict, f, indent=4) @classmethod - def from_json(cls, fp: Union[pathlib.Path, str], - validate: bool = True) -> MetaFrame: + def load_json(cls, fp: Union[pathlib.Path, str], validate: bool = True) -> MetaFrame: """Read a MetaFrame from a JSON file. Parameters @@ -295,6 +334,80 @@ def from_json(cls, fp: Union[pathlib.Path, str], meta_vars = [MetaVar.from_dict(d) for d in self_dict["vars"]] return cls(meta_vars, n_rows) + def to_json(self, fp: Union[pathlib.Path, str], validate: bool = True) -> None: + """Export, deprecated method, use Metaframe.save_json instead.""" + warn("to_json method of MetaFrame is deprecated and will be removed in the future, " + "Use MetaFrame.save_json or MetaFrame.save instead.", + DeprecationWarning, stacklevel=2) + self.save_json(fp, validate) + + def export(self, fp: Union[pathlib.Path, str], validate: bool = True) -> None: + """Export, deprecated method, use Metaframe.save instead.""" + warn("Export method of MetaFrame is deprecated and will be removed in the future, " + "Use MetaFrame.save_json or MetaFrame.save instead.", + DeprecationWarning, stacklevel=2) + self.save_json(fp, validate) + + @classmethod + def from_json(cls, fp: Union[pathlib.Path, str], + validate: bool = True) -> MetaFrame: + """Import, deprecated method, use Metaframe.load_json instead.""" + warn("MetaFrame.from_json is deprecated and will be removed in the future, " + "use MetaFrame.load_json or MetaFrame.load instead.", + DeprecationWarning, stacklevel=2) + return cls.load_json(fp, validate) + + @no_type_check + def save_toml(self, fp: Optional[Union[pathlib.Path, str]], + validate: bool = True) -> None: + try: + import tomlkit + except ImportError: + raise ValueError("Please install tomlkit (pip install tomlkit) to " + "enable support for saving to toml.") + self_dict = _jsonify(self.to_dict()) + if validate: + validate_gmf_dict(self_dict) + + doc = tomlkit.loads(tomlkit.dumps(self_dict)) + doc["n_rows"].comment("Number of rows") + doc["n_columns"].comment("Number of columns") + for i in range(self.n_columns): + var = self.meta_vars[i] + doc["vars"][i].comment(f"Metadata for column with name {var.name}") + doc["vars"][i]["prop_missing"].comment( + f"Fraction of missing values, remaining: {round(self.n_rows*(1-var.prop_missing))} " + "values") + # The below comment does not work, a tomlkit bug? + # doc["vars"][i]["distribution"]["unique"].add(tomlkit.comment( + # "Whether to generate unique values or not")) + if "privacy" in var.creation_method: + privacy = get_privacy(**var.creation_method["privacy"]) + doc["vars"][i]["distribution"]["parameters"].add(tomlkit.comment(privacy.comment(var))) + if var.distribution.matches_name("multinoulli"): + counts = (var.distribution.probs*(1-var.prop_missing)*self.n_rows).round() + doc["vars"][i]["distribution"].add(tomlkit.comment( + f"Counts: {counts.astype(int)}\n")) + + if fp is None: + print(tomlkit.dumps(doc)) + else: + with open(fp, "w", encoding="utf-8") as f: + tomlkit.dump(doc, f) + + @classmethod + def load_toml(cls, fp: Union[pathlib.Path, str], + validate: bool = True) -> MetaFrame: + with open(fp, "rb") as f: + self_dict = tomllib.load(f) + + if validate: + validate_gmf_dict(self_dict) + + n_rows = self_dict["n_rows"] + meta_vars = [MetaVar.from_dict(d) for d in self_dict["vars"]] + return cls(meta_vars, n_rows) + def synthesize(self, n: Optional[int] = None) -> pl.DataFrame: """Create a synthetic Polars dataframe. diff --git a/metasyn/privacy.py b/metasyn/privacy.py index bb16280a..e1b1975f 100644 --- a/metasyn/privacy.py +++ b/metasyn/privacy.py @@ -51,6 +51,19 @@ def fit_kwargs(self): """ return self.to_dict()["parameters"] + def comment(self, var): # noqa + """Comment on the privacy features for the TOML GMF file. + + Parameters + ---------- + col_name + Name of the column. + + Returns + ------- + A comment on the privacy features. + """ + return "Above are the parameters for the column {var.name}" class BasicPrivacy(BasePrivacy): """Class representing no privacy level. diff --git a/metasyn/provider.py b/metasyn/provider.py index a3d39411..9753e968 100644 --- a/metasyn/provider.py +++ b/metasyn/provider.py @@ -241,7 +241,8 @@ def create(self, var_spec: Union[VarSpec, VarSpecAccess]) -> BaseDistribution: """ dist_spec = var_spec.dist_spec unique = dist_spec.unique if dist_spec.unique else False - assert dist_spec.implements is not None and var_spec.var_type is not None + if dist_spec.implements is None: + raise ValueError("Cannot create distribution without specifying the 'implements' key.") dist_class = self.find_distribution( dist_spec.implements, var_spec.var_type, privacy=BasicPrivacy(), unique=unique) @@ -298,7 +299,7 @@ def _find_best_fit(self, series: pl.Series, var_type: str, def find_distribution(self, dist_name: str, - var_type: str, + var_type: Optional[str], privacy: Optional[BasePrivacy] = BasicPrivacy(), unique: bool = False, version: Optional[str] = None) -> type[BaseDistribution]: diff --git a/metasyn/testutils.py b/metasyn/testutils.py index 017028b5..10daa15a 100644 --- a/metasyn/testutils.py +++ b/metasyn/testutils.py @@ -7,10 +7,14 @@ from __future__ import annotations +import json + import jsonschema +import numpy as np import polars as pl from jsonschema.exceptions import SchemaError +from metasyn.distribution import MultinoulliDistribution, NADistribution from metasyn.distribution.base import BaseDistribution from metasyn.metaframe import _jsonify from metasyn.privacy import BasePrivacy @@ -19,6 +23,7 @@ DistributionProviderList, get_distribution_provider, ) +from metasyn.var import MetaVar def check_distribution_provider(provider_name: str): @@ -81,3 +86,116 @@ def check_distribution(distribution: type[BaseDistribution], privacy: BasePrivac assert isinstance(new_dist, distribution) assert set(list(new_dist.to_dict())) >= set( ("implements", "provenance", "class_name", "parameters")) + + + +HEADER_TEMPLATE = """ +# Metasyn report for {file_name} + +## General + +- Number of rows: {n_rows} +- Number of columns: {n_columns} +- Generated by {program_name} version {program_version} at {generation_time} + +## Variables / columns + +""" + + +VAR_TEMPLATE = """ +### {var_name} + +- Distribution {class_name} with parameters: +{parameters} +- Variable type {var_type} +- Based on {n_based_on} values{disclosure} +- Percentage missing: {missing_perc} % +- Generated examples: {example_list} + + +""" + +def create_md_report(file_name, out_md_file): + """Create markdown report from GMF file.""" + with open(file_name, "r") as handle: + gmf_dict = json.load(handle) + + header = HEADER_TEMPLATE.format( + file_name=file_name, + n_rows=gmf_dict["n_rows"], + n_columns=gmf_dict["n_columns"], + program_name=gmf_dict["provenance"]["created by"]["name"], + program_version=gmf_dict["provenance"]["created by"]["version"], + generation_time=gmf_dict["provenance"]["creation time"], + ) + variables = "" + + for var_dict in gmf_dict["vars"]: + var = MetaVar.from_dict(var_dict) + if isinstance(var.distribution, MultinoulliDistribution): + parameters = [f"\t- {label}: {round(prob*gmf_dict['n_rows']*(1-var.prop_missing))}\n" + for label, prob in zip(var.distribution.labels, var.distribution.probs)] + elif isinstance(var.distribution, NADistribution): + variables += (f"### {var.name}\n- Distribution NADistribution\n- Only missing values" + "\n- Examples: NA, NA, NA, ...\n") + continue + else: + parameters = [f"\t - {name}: {value}\n" + for name, value in var.distribution._param_dict().items()] + parameter_str = "".join(parameters) + if var.prop_missing > 0: + examples = np.random.permutation([str(var.distribution.draw()) for _ in range(3)] + + ["NA", "NA"]) + else: + examples = [str(x) for x in var.draw_series(5)] + + if "privacy" in var_dict["creation_method"]: + partition_size = var_dict["creation_method"]["privacy"]["parameters"]["partition_size"] + disclosure = f" using micro aggregation with a partition size of {partition_size}" + else: + disclosure = "" + variables += VAR_TEMPLATE.format( + var_name = var.name, + var_type=var.var_type, + class_name=var.distribution.__class__.__name__, + n_based_on=round(gmf_dict["n_rows"]*(1-var.prop_missing)), + example_list=", ".join(examples) + ", ...", + parameters=parameter_str, + missing_perc=f"{100*var.prop_missing:.2f}", + disclosure=disclosure, + ) + with open(out_md_file, "w", encoding="utf-8") as handle: + handle.write(header + variables) + + +def create_input_toml(file_name): + """Create input toml with all distribution in builtin.""" + import tomlkit + + prov = get_distribution_provider("builtin") + doc = tomlkit.document() + doc.add("config_version", "1.1") + doc.add("dist_providers", ["builtin"]) + doc.add("n_rows", 100) + doc.add("defaults", {"data_free": True, "prop_missing": 0.1}) + var_array = tomlkit.aot() + for dist in prov.distributions: + var = tomlkit.table() + var.add("name", dist.__name__) + if isinstance(dist.var_type, str): + var_type = dist.var_type + else: + var_type = dist.var_type[0] + var.add("var_type", var_type) + dist_dict = _jsonify(dist.default_distribution().to_dict()) + dist_dict.pop("version") + dist_dict.pop("class_name") + dist_dict.pop("provenance") + var.add("distribution", dist_dict) + var.add(tomlkit.nl()) + var_array.append(var) + doc.add("var", var_array) + + with open(file_name, "w", encoding="utf-8") as handle: + tomlkit.dump(doc, handle) diff --git a/metasyn/util.py b/metasyn/util.py index 187e3c33..983179f1 100644 --- a/metasyn/util.py +++ b/metasyn/util.py @@ -12,6 +12,8 @@ from importlib.resources import files # type: ignore +ALL_VAR_TYPES = ["discrete", "continuous", "time", "date", "datetime", "string", "categorical"] + def get_registry() -> dict: """Get the registry dictionary from the package. diff --git a/metasyn/var.py b/metasyn/var.py index e711822f..b1432892 100644 --- a/metasyn/var.py +++ b/metasyn/var.py @@ -53,7 +53,7 @@ class MetaVar: def __init__( # noqa: PLR0913 self, name: str, - var_type: str, + var_type: Optional[str], distribution: BaseDistribution, dtype: str = "unknown", description: Optional[str] = None, @@ -61,6 +61,11 @@ def __init__( # noqa: PLR0913 creation_method: Optional[dict] = None, ): self.name = name + if var_type is None: + if not isinstance(distribution.var_type, str): + raise ValueError("Failed to infer variable type for variable '{name}'" + " supply var_type or a different distribution.") + var_type = distribution.var_type self.var_type = var_type self.distribution = distribution self.dtype = dtype @@ -75,7 +80,10 @@ def __init__( # noqa: PLR0913 "outside range [0, 1]" ) if self.dtype == "unknown": - self.dtype = str(pl.Series([self.draw()]).dtype) + if self.var_type == "categorical": + self.dtype = "Categorical" + else: + self.dtype = str(pl.Series([self.distribution.draw()]).dtype) @staticmethod def get_var_type(series: pl.Series) -> str: diff --git a/metasyn/varspec.py b/metasyn/varspec.py index 68dec429..74027da1 100644 --- a/metasyn/varspec.py +++ b/metasyn/varspec.py @@ -7,6 +7,7 @@ from metasyn.distribution.base import BaseDistribution from metasyn.privacy import BasePrivacy, BasicPrivacy, get_privacy +from metasyn.util import ALL_VAR_TYPES @dataclass @@ -164,7 +165,7 @@ def __init__( privacy: Optional[BasePrivacy] = None, prop_missing: Optional[float] = None, description: Optional[str] = None, - data_free: bool = False, + data_free: Optional[bool] = None, var_type: Optional[str] = None): self.name = name @@ -183,6 +184,9 @@ def __post_init__(self): if self.data_free and not self.dist_spec.fully_specified: raise ValueError("Error creating variable specification: data free variable should have" f" 'implements' and 'parameters'. {self}") + if self.var_type is not None and self.var_type not in ALL_VAR_TYPES: + raise ValueError(f"Cannot create variable '{self.name}': unknown variable type " + f"'{self.var_type}'. Choose from {ALL_VAR_TYPES}.") @classmethod def from_dict(cls, var_dict: dict) -> VarSpec: @@ -198,3 +202,32 @@ def from_dict(cls, var_dict: dict) -> VarSpec: A VarSpec instance. """ return cls(**var_dict) + + +@dataclass +class VarDefaults(): + """Dataclass for variable defaults. + + Parameters + ---------- + data_free: + Whether the variable is completely synthetic or is based on real data. + prop_missing: + Proportion of missing values. + distribution: + Dictionary containing default distributions for each variable type. + privacy: + Privacy to be used by default for estimating distributions. + """ + + data_free: bool = False + prop_missing: Optional[float] = None + distribution: Optional[dict] = None + privacy: Optional[BasePrivacy] = None + + def __post_init__(self): + if isinstance(self.privacy, dict): + self.privacy = get_privacy(**self.privacy) + elif not isinstance(self.privacy, BasePrivacy): + self.privacy = get_privacy("none") + diff --git a/pyproject.toml b/pyproject.toml index 76e4a6ed..1226b6ed 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,7 +48,7 @@ GitHub = "https://github.com/sodascience/metasyn" documentation = "https://metasyn.readthedocs.io/en/latest/index.html" [project.optional-dependencies] -extra = ["xlsxwriter", "pandas"] +extra = ["xlsxwriter", "pandas", "tomlkit"] check = ["ruff", "mypy", "types-tqdm", "types-regex"] test = ["pytest", "nbval"] docs = [ @@ -90,7 +90,7 @@ module = [ ignore_missing_imports = true [tool.ruff] -exclude = ["_version.py", "metasyn/distribution/legacy.*", "tests/*", "examples/*"] +exclude = ["_version.py", "metasyn/distribution/legacy.*", "examples/*"] line-length = 100 lint.select = ["E", "F", "W", "I", "N", "D", "PL", "ARG"] lint.ignore = ["PLR2004", "D107", "D105", "D102"] diff --git a/tests/data/bad_config.toml b/tests/data/bad_config.toml new file mode 100644 index 00000000..3790ef5e --- /dev/null +++ b/tests/data/bad_config.toml @@ -0,0 +1 @@ +This is not a toml file \ No newline at end of file diff --git a/tests/test_cli.py b/tests/test_cli.py index 474999d0..eec4f998 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -37,7 +37,7 @@ def tmp_dir(tmp_path_factory) -> Path: } data_frame = pl.read_csv(csv_fp, schema_overrides=csv_dt)[:100] meta_frame = MetaFrame.fit_dataframe(data_frame, var_specs=[{"name": "PassengerId", "distribution": {"unique": True}}]) - meta_frame.to_json(json_path) + meta_frame.save_json(json_path) config_fp = TMP_DIR_PATH / "config.ini" with open(config_fp, "w") as handle: handle.write(""" @@ -56,7 +56,6 @@ def tmp_dir(tmp_path_factory) -> Path: @mark.parametrize("ext", [".csv", ".feather", ".parquet", ".pkl", ".xlsx"]) def test_cli(tmp_dir, ext): """A simple integration test for reading and writing using the CLI""" - # create out file path with correct extension out_file = tmp_dir / f"titanic{ext}" @@ -96,7 +95,7 @@ def test_create_meta(tmp_dir, config): result = subprocess.run(cmd, check=False, capture_output=True) assert result.returncode == 0 assert out_file.is_file() - meta_frame = MetaFrame.from_json(out_file) + meta_frame = MetaFrame.load_json(out_file) assert len(meta_frame.meta_vars) == 12 @@ -145,7 +144,7 @@ def test_datafree(tmp_dir): ] result = subprocess.run(cmd, check=False, capture_output=True) assert result.returncode == 0 - meta_frame = MetaFrame.from_json(gmf_fp) + meta_frame = MetaFrame.load_json(gmf_fp) assert meta_frame.n_rows == 100 assert len(meta_frame.meta_vars) == 3 cmd2 = [ diff --git a/tests/test_dataset.py b/tests/test_dataset.py index d18aa8d4..0551ce4b 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -78,8 +78,8 @@ def check_dataset(dataset): dataset.n_rows = 100 check_dataset(dataset) - dataset.to_json(tmp_fp) - dataset = MetaFrame.from_json(tmp_fp) + dataset.save_json(tmp_fp) + dataset = MetaFrame.load_json(tmp_fp) check_dataset(dataset) dataset.descriptions = {"Embarked": "Some description", "Sex": "Other description"} @@ -114,7 +114,7 @@ def test_distributions(tmp_path): var = MetaVar(name="None", var_type=var_type, distribution=dist.default_distribution(), prop_missing=random()) dataset = MetaFrame([var], n_rows=10) - dataset.to_json(tmp_fp) + dataset.save_json(tmp_fp) @mark.parametrize( "dataset_name", list(_AVAILABLE_DATASETS) @@ -131,8 +131,8 @@ def test_demo_datasets(tmp_path, dataset_name): assert isinstance(mf, MetaFrame) tmp_file = tmp_path / "gmf.json" - mf.to_json(tmp_file) - mf = MetaFrame.from_json(tmp_file) + mf.save_json(tmp_file) + mf = MetaFrame.load_json(tmp_file) df_syn = mf.synthesize(100) @@ -143,3 +143,10 @@ def test_demo_datasets(tmp_path, dataset_name): def test_demo_non_exist(): with pytest.raises(ValueError): demo_file("non-existing-dataset") + + +def test_create_dataset(tmpdir): + test_class = _get_demo_class("test") + test_class.create(tmpdir/"test.csv") + df = pl.read_csv(Path(tmpdir/"test.csv")) + assert isinstance(df, pl.DataFrame) diff --git a/tests/test_toml.py b/tests/test_toml.py new file mode 100644 index 00000000..3842e3b0 --- /dev/null +++ b/tests/test_toml.py @@ -0,0 +1,57 @@ +from filecmp import cmp +from pathlib import Path + +import pytest +try: + import tomllib +except ImportError: + import tomli as tomllib + +from pytest import mark + +from metasyn.demo import demo_dataframe +from metasyn.metaframe import MetaFrame +from metasyn.provider import BuiltinDistributionProvider +from metasyn.testutils import create_input_toml, create_md_report + + +def test_datafree_create(tmpdir): + + temp_toml = tmpdir / "test.toml" + create_input_toml(temp_toml) + assert cmp(temp_toml, Path("examples", "config_files", "example_all.toml")) + mf = MetaFrame.fit_dataframe(None, var_specs=Path(temp_toml)) + + assert isinstance(mf, MetaFrame) + assert mf.n_columns == len(BuiltinDistributionProvider.distributions) + +@mark.parametrize( + "toml_input,data", [ + (Path("examples", "config_files", "example_all.toml"), None), + # (Path("examples", "config_files", "example_config.toml"), demo_dataframe("titanic")), + (Path("examples", "config_files", "example_datafree.toml"), None), + ] +) +def test_toml_save_load(tmpdir, toml_input, data): + mf = MetaFrame.fit_dataframe(data, toml_input) + mf.save(tmpdir/"test.toml") + new_mf = MetaFrame.load(tmpdir/"test.toml") + assert mf.n_columns == new_mf.n_columns + +@mark.parametrize( + "gmf_file", [ + Path("examples", "gmf_files", "example_gmf_simple.json"), + Path("examples", "gmf_files", "example_gmf_titanic.json"), + ] +) +def test_md_output(tmpdir, gmf_file): + create_md_report(gmf_file, tmpdir/"test.md") + assert Path(tmpdir/"test.md").is_file() + assert isinstance(MetaFrame.load(gmf_file), MetaFrame) + +def test_toml_err(): + with pytest.raises(FileNotFoundError): + MetaFrame.from_config("doesnotexist.toml") + + with pytest.raises(tomllib.TOMLDecodeError): + MetaFrame.from_config(Path("tests", "data", "bad_config.toml")) From db65390851a5374f573f01dcbaf245bf869d55db Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Wed, 16 Oct 2024 15:46:53 +0200 Subject: [PATCH 06/17] Fix scheduled CI --- .github/workflows/scheduled-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/scheduled-ci.yml b/.github/workflows/scheduled-ci.yml index 24682482..9d4e88a3 100644 --- a/.github/workflows/scheduled-ci.yml +++ b/.github/workflows/scheduled-ci.yml @@ -27,7 +27,7 @@ jobs: python -m pip install --upgrade pip python -m pip install ".[check]" - name: Lint with Ruff - run: ruff check + run: ruff check metasyn - name: Check types with MyPy run: mypy metasyn From 19e49e83acac86b4fd4bdb36c55064212693ee9c Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Wed, 16 Oct 2024 16:06:04 +0200 Subject: [PATCH 07/17] Update scheduled-ci.yml --- .github/workflows/scheduled-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/scheduled-ci.yml b/.github/workflows/scheduled-ci.yml index 9d4e88a3..684a5140 100644 --- a/.github/workflows/scheduled-ci.yml +++ b/.github/workflows/scheduled-ci.yml @@ -9,7 +9,7 @@ jobs: check: name: Lint and check types strategy: - fail-fast: true + fail-fast: false matrix: branch: ["main", "develop"] python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] From 708563b9653861a1b967c475560b8157082f86f3 Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Wed, 16 Oct 2024 16:10:24 +0200 Subject: [PATCH 08/17] Update scheduled-ci.yml --- .github/workflows/scheduled-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/scheduled-ci.yml b/.github/workflows/scheduled-ci.yml index 684a5140..36abbfa2 100644 --- a/.github/workflows/scheduled-ci.yml +++ b/.github/workflows/scheduled-ci.yml @@ -25,7 +25,7 @@ jobs: - name: Install dependencies run: | python -m pip install --upgrade pip - python -m pip install ".[check]" + python -m pip install ".[check,extra]" - name: Lint with Ruff run: ruff check metasyn - name: Check types with MyPy From e0209007ee987742023d579841f93659823ed87d Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Mon, 21 Oct 2024 19:07:53 +0200 Subject: [PATCH 09/17] Improve unique warning (#332) Lower the frequency of this warning. Only if we are quite certain about the uniqueness, a warning will be issued. We now have an exception for unique key columns: if they are consecutive and metasyn is quite certain in it being the best fit, they will be chosen. --- .github/workflows/core-ci.yml | 8 +- .github/workflows/scheduled-ci.yml | 8 +- docs/source/faq.rst | 6 +- docs/source/usage/generating_metaframes.rst | 2 +- docs/source/usage/quick_start.rst | 8 - examples/getting_started.ipynb | 716 +++----------------- examples/gmf_files/example_gmf_titanic.json | 10 +- metasyn/provider.py | 18 +- pyproject.toml | 3 +- 9 files changed, 142 insertions(+), 637 deletions(-) diff --git a/.github/workflows/core-ci.yml b/.github/workflows/core-ci.yml index 6b65de97..686197e5 100644 --- a/.github/workflows/core-ci.yml +++ b/.github/workflows/core-ci.yml @@ -10,7 +10,7 @@ jobs: strategy: fail-fast: true matrix: - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.9", "3.10", "3.11", "3.12"] runs-on: "ubuntu-latest" steps: - uses: actions/checkout@v4 @@ -33,12 +33,12 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.9", "3.10", "3.11", "3.12"] include: - os: macos-latest - python-version: "3.x" + python-version: "3.12" - os: windows-latest - python-version: "3.x" + python-version: "3.12" runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 diff --git a/.github/workflows/scheduled-ci.yml b/.github/workflows/scheduled-ci.yml index 36abbfa2..b6ddf3e9 100644 --- a/.github/workflows/scheduled-ci.yml +++ b/.github/workflows/scheduled-ci.yml @@ -12,7 +12,7 @@ jobs: fail-fast: false matrix: branch: ["main", "develop"] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.9", "3.10", "3.11", "3.12"] runs-on: "ubuntu-latest" steps: - uses: actions/checkout@v4 @@ -38,12 +38,12 @@ jobs: matrix: branch: ["main", "develop"] os: [ubuntu-latest] - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.9", "3.10", "3.11", "3.12"] include: - os: macos-latest - python-version: "3.x" + python-version: "3.12" - os: windows-latest - python-version: "3.x" + python-version: "3.12" runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 diff --git a/docs/source/faq.rst b/docs/source/faq.rst index c471bd6f..5d58de8f 100644 --- a/docs/source/faq.rst +++ b/docs/source/faq.rst @@ -22,8 +22,8 @@ The process of generating synthetic data solely from the MetaFrame ensures that -**I encountered the warning: "Variable PassengerId seems unique, but not set to be unique." What should I do?** ------------------------------------------------------------------------------------------------------------------ +**I encountered the warning: "Metasyn detected that variable {x} is potentially unique." What should I do?** +------------------------------------------------------------------------------------------------------------ This warning occurs when ``metasyn`` detects a column, that seems to have unique values in the real dataset but isn't specified to be unique in the fitting of the :obj:`MetaFrame`. To address this, you can use the `spec` parameter to create a specification dictionary and indicate that the column should have unique values. Here's an example of how to do this (in this example ``PassengerId`` is the column with unique values): .. code-block:: python @@ -40,6 +40,8 @@ This warning occurs when ``metasyn`` detects a column, that seems to have unique More information on how to use the optional parameters in the :meth:`metasyn.MetaFrame.fit_dataframe() ` function can be found in :doc:`/usage/generating_metaframes` under :ref:`optionalparams`. +You can also set the uniqueness of a variable in the :doc:`configuration file ` + **I found a bug/issue, where can I report it?** ----------------------------------------------- If you encounter any bugs or have identified an issue with metasyn, we encourage you to report it on our `GitHub issue tracker `_. This allows us to track and address the problem efficiently. Alternatively, you can find out how to contact us through the details provided in our :doc:`contact page `. diff --git a/docs/source/usage/generating_metaframes.rst b/docs/source/usage/generating_metaframes.rst index 3599933a..79d97b22 100644 --- a/docs/source/usage/generating_metaframes.rst +++ b/docs/source/usage/generating_metaframes.rst @@ -85,7 +85,7 @@ The potential directives include: When generating a MetaFrame, ``metasyn`` will automatically analyze the columns of the input DataFrame to detect ones that contain only unique values. If such a column is found, and it has not manually been set to unique in the ``var_specs`` list, the user will be notified with the following warning: - ``Variable '[variable name]' was detected to be unique, but has not explicitly been set to unique. To generate only unique values for column 'PassengerId', set unique to True. To dismiss this warning, set unique to False."`` + ``Metasyn detected that variable '{variable name}' is potentially unique."`` It is safe to ignore this warning - however, be aware that without setting the column as unique, ``metasyn`` may generate duplicate values for that column when synthesizing data. diff --git a/docs/source/usage/quick_start.rst b/docs/source/usage/quick_start.rst index bea7ef02..585a9f58 100644 --- a/docs/source/usage/quick_start.rst +++ b/docs/source/usage/quick_start.rst @@ -78,14 +78,6 @@ With the DataFrame loaded, you can now generate a :obj:`MetaFrame , MetaVar , ...>\n" ] } ], @@ -1001,29 +639,31 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    842"Rise. Answer. Camera. Response…"female"300"8227"53.968851null"S"1936-03-1414:02:392022-07-21 16:19:37null
    798"Realize. Ten someone. Behind m…"female"60"465036"21.628286null"S"1936-05-2917:00:182022-07-27 03:35:13null
    305"History wait. Leg through year…"female"480"661985"51.767786null"S"1936-01-1213:01:272022-07-29 19:25:29null
    377"Section. Able. Study.""female"390"612165"139.011237"A12""S"1927-06-0811:32:172022-08-11 10:28:23null
    694"Himself.""male"10"618407"22.897537null"S"1921-10-1917:23:48nullnull
    " + "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Sister. Benefit. Reality. Deci…"male"420"3928"10.508311null"S"1913-07-1313:34:242022-07-21 01:17:45null
    2"Truth. System. Role.""male"530"601707"15.501538null"S"1937-08-3112:53:592022-07-29 22:21:06null
    3"What.""male"260"4697"10.67768null"S"1926-06-1018:23:232022-08-12 06:55:58null
    4"Bed. Late. Town.""female"null0"897872"15.867427null"S"1924-09-0512:39:472022-08-05 04:59:33null
    5"Large business cup effect. Thr…"male"null0"6633"104.973689null"S"1927-12-1310:46:22nullnull
    " ], "text/plain": [ "shape: (5, 13)\n", - "┌─────────────┬───────────────┬────────┬─────┬───┬────────────┬────────────┬──────────────┬────────┐\n", - "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", - "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", - "╞═════════════╪═══════════════╪════════╪═════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", - "│ 842 ┆ Rise. Answer. ┆ female ┆ 30 ┆ … ┆ 1936-03-14 ┆ 14:02:39 ┆ 2022-07-21 ┆ null │\n", - "│ ┆ Camera. ┆ ┆ ┆ ┆ ┆ ┆ 16:19:37 ┆ │\n", - "│ ┆ Response… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 798 ┆ Realize. Ten ┆ female ┆ 6 ┆ … ┆ 1936-05-29 ┆ 17:00:18 ┆ 2022-07-27 ┆ null │\n", - "│ ┆ someone. ┆ ┆ ┆ ┆ ┆ ┆ 03:35:13 ┆ │\n", - "│ ┆ Behind m… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 305 ┆ History wait. ┆ female ┆ 48 ┆ … ┆ 1936-01-12 ┆ 13:01:27 ┆ 2022-07-29 ┆ null │\n", - "│ ┆ Leg through ┆ ┆ ┆ ┆ ┆ ┆ 19:25:29 ┆ │\n", - "│ ┆ year… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 377 ┆ Section. ┆ female ┆ 39 ┆ … ┆ 1927-06-08 ┆ 11:32:17 ┆ 2022-08-11 ┆ null │\n", - "│ ┆ Able. Study. ┆ ┆ ┆ ┆ ┆ ┆ 10:28:23 ┆ │\n", - "│ 694 ┆ Himself. ┆ male ┆ 1 ┆ … ┆ 1921-10-19 ┆ 17:23:48 ┆ null ┆ null │\n", - "└─────────────┴───────────────┴────────┴─────┴───┴────────────┴────────────┴──────────────┴────────┘" + "┌─────────────┬──────────────┬────────┬──────┬───┬────────────┬────────────┬──────────────┬────────┐\n", + "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", + "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", + "╞═════════════╪══════════════╪════════╪══════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", + "│ 1 ┆ Sister. ┆ male ┆ 42 ┆ … ┆ 1913-07-13 ┆ 13:34:24 ┆ 2022-07-21 ┆ null │\n", + "│ ┆ Benefit. ┆ ┆ ┆ ┆ ┆ ┆ 01:17:45 ┆ │\n", + "│ ┆ Reality. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ Deci… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ 2 ┆ Truth. ┆ male ┆ 53 ┆ … ┆ 1937-08-31 ┆ 12:53:59 ┆ 2022-07-29 ┆ null │\n", + "│ ┆ System. ┆ ┆ ┆ ┆ ┆ ┆ 22:21:06 ┆ │\n", + "│ ┆ Role. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ 3 ┆ What. ┆ male ┆ 26 ┆ … ┆ 1926-06-10 ┆ 18:23:23 ┆ 2022-08-12 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 06:55:58 ┆ │\n", + "│ 4 ┆ Bed. Late. ┆ female ┆ null ┆ … ┆ 1924-09-05 ┆ 12:39:47 ┆ 2022-08-05 ┆ null │\n", + "│ ┆ Town. ┆ ┆ ┆ ┆ ┆ ┆ 04:59:33 ┆ │\n", + "│ 5 ┆ Large ┆ male ┆ null ┆ … ┆ 1927-12-13 ┆ 10:46:22 ┆ null ┆ null │\n", + "│ ┆ business cup ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ effect. Thr… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "└─────────────┴──────────────┴────────┴──────┴───┴────────────┴────────────┴──────────────┴────────┘" ] }, "execution_count": 12, @@ -1061,136 +701,6 @@ "The `MetaFrame.fit_dataframe()` method allows you to have more control over how your synthetic dataset is generated by passing in an optional `spec` (short for specification) parameter. `spec` is a dictionary that can be used to give metasyn instructions on a per-variable basis, these instructions can range from setting a variable to be unique, to directly setting its distribution. " ] }, - { - "cell_type": "markdown", - "id": "df32cc3a", - "metadata": {}, - "source": [ - "### Spec: Setting variables to have unique variables\n", - "\n", - "During the MetaFrame generation at the start (using `MetaFrame.fit_dataframe()`), metaframe detected a column (PassengerId) as possibly unique, as indicated by the following warning:\n", - "\n", - "> *\"Variable PassengerId seems unique, but not set to be unique.\"*\n", - "\n", - "This is because this column holds a unique identifier for each passenger, which is in fact unique to each passenger. As such, we want the synthetic data generated for this column to be unique as well. \n", - "\n", - "In order to set a variable to be unique, we can add a `unique` key to the specification dictionary of the variable, and set it to `True`. We can do it for the `PassengerId` column as follows:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e7b76751", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 13/13 [00:01<00:00, 8.32it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "{'name': 'PassengerId',\n", - " 'type': 'discrete',\n", - " 'dtype': 'Int64',\n", - " 'prop_missing': 0.0,\n", - " 'distribution': {'implements': 'core.unique_key',\n", - " 'version': '1.0',\n", - " 'provenance': 'builtin',\n", - " 'class_name': 'UniqueKeyDistribution',\n", - " 'unique': True,\n", - " 'parameters': {'lower': 1, 'consecutive': True}},\n", - " 'creation_method': {'created_by': 'metasyn', 'unique': True}}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# First, we create a specification dictionary for the variables\n", - "var_specs = [\n", - " VarSpec(\"PassengerId\", unique=True)\n", - "]\n", - "\n", - "# then, we add that dictionary as the `spec` argument\n", - "mf = MetaFrame.fit_dataframe(df, var_specs=var_specs)\n", - "\n", - "# then, let's check what the metadata about PassengerId contains!\n", - "mf[\"PassengerId\"].to_dict()" - ] - }, - { - "cell_type": "markdown", - "id": "a28f0970", - "metadata": {}, - "source": [ - "So let's check what is generated from this new MetaFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "38c2f4b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Really several. Instead.""male"190"320491"18.726832null"C"null14:11:202022-07-20 07:54:52null
    2"Adult.""male"null0"25222"27.87715null"S"1936-06-2418:03:352022-07-26 01:42:15null
    3"Country result. Author. Benefi…"male"170"KIP.A. 34247"51.373905null"S"1913-02-1218:34:482022-08-06 06:34:19null
    4"More finally anything class. C…"male"320"4318"60.565239null"S"1931-08-2812:39:492022-07-26 12:06:39null
    5"Real. Which.""male"260"09729"6.532226null"S"1914-08-3118:04:472022-07-22 16:18:58null
    " - ], - "text/plain": [ - "shape: (5, 13)\n", - "┌─────────────┬───────────────┬──────┬──────┬───┬────────────┬────────────┬───────────────┬────────┐\n", - "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married since ┆ all_NA │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", - "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ datetime[μs] ┆ str │\n", - "╞═════════════╪═══════════════╪══════╪══════╪═══╪════════════╪════════════╪═══════════════╪════════╡\n", - "│ 1 ┆ Really ┆ male ┆ 19 ┆ … ┆ null ┆ 14:11:20 ┆ 2022-07-20 ┆ null │\n", - "│ ┆ several. ┆ ┆ ┆ ┆ ┆ ┆ 07:54:52 ┆ │\n", - "│ ┆ Instead. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 2 ┆ Adult. ┆ male ┆ null ┆ … ┆ 1936-06-24 ┆ 18:03:35 ┆ 2022-07-26 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 01:42:15 ┆ │\n", - "│ 3 ┆ Country ┆ male ┆ 17 ┆ … ┆ 1913-02-12 ┆ 18:34:48 ┆ 2022-08-06 ┆ null │\n", - "│ ┆ result. ┆ ┆ ┆ ┆ ┆ ┆ 06:34:19 ┆ │\n", - "│ ┆ Author. ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ ┆ Benefi… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 4 ┆ More finally ┆ male ┆ 32 ┆ … ┆ 1931-08-28 ┆ 12:39:49 ┆ 2022-07-26 ┆ null │\n", - "│ ┆ anything ┆ ┆ ┆ ┆ ┆ ┆ 12:06:39 ┆ │\n", - "│ ┆ class. C… ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", - "│ 5 ┆ Real. Which. ┆ male ┆ 26 ┆ … ┆ 1914-08-31 ┆ 18:04:47 ┆ 2022-07-22 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 16:18:58 ┆ │\n", - "└─────────────┴───────────────┴──────┴──────┴───┴────────────┴────────────┴───────────────┴────────┘" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mf.synthesize(5)" - ] - }, - { - "cell_type": "markdown", - "id": "11c7f562", - "metadata": {}, - "source": [ - "As you can see, the `PassengerId` column is now unique!" - ] - }, { "cell_type": "markdown", "id": "6f884eb7", @@ -1205,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "d5615aa0", "metadata": {}, "outputs": [ @@ -1213,7 +723,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 13/13 [00:00<00:00, 15.17it/s]\n" + "100%|██████████| 13/13 [00:00<00:00, 14.79it/s]\n" ] }, { @@ -1226,30 +736,28 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Joseph Garcia""female"null0"96223"20.99684null"S"1906-04-1111:22:222022-08-15 00:37:43null
    2"Julie Kim""male"470"20728"17.124865null"S"1913-04-24null2022-07-29 06:33:10null
    3"Mitchell Perry""male"160"88165"29.45121null"S"1929-05-20null2022-07-22 02:15:33null
    4"Sean Sanchez""male"341"0956"26.174926null"S"null15:52:552022-08-08 17:34:23null
    5"Laurie Adams""male"240"26664"102.336395null"Q"1913-12-1313:07:552022-08-04 07:46:23null
    " + "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Jesus Armstrong""male"120"4765"0.984583null"C"1913-05-1317:59:252022-08-01 04:52:02null
    2"Jeff Ball""female"110"5832"48.003537"B4""S"1921-05-0612:40:37nullnull
    3"Lindsey Collins""female"360"346812"31.828248null"S"1929-07-2815:55:152022-08-15 04:35:43null
    4"Vincent Stevenson""male"451"0369"13.633204null"S"1919-03-30null2022-08-09 06:01:43null
    5"Michele Whitney""male"480"288891"3.003839"E 4""S"1926-09-0613:46:372022-07-18 13:16:16null
    " ], "text/plain": [ "shape: (5, 13)\n", - "┌─────────────┬──────────────┬────────┬──────┬───┬────────────┬────────────┬──────────────┬────────┐\n", - "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", - "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", - "╞═════════════╪══════════════╪════════╪══════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", - "│ 1 ┆ Joseph ┆ female ┆ null ┆ … ┆ 1906-04-11 ┆ 11:22:22 ┆ 2022-08-15 ┆ null │\n", - "│ ┆ Garcia ┆ ┆ ┆ ┆ ┆ ┆ 00:37:43 ┆ │\n", - "│ 2 ┆ Julie Kim ┆ male ┆ 47 ┆ … ┆ 1913-04-24 ┆ null ┆ 2022-07-29 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 06:33:10 ┆ │\n", - "│ 3 ┆ Mitchell ┆ male ┆ 16 ┆ … ┆ 1929-05-20 ┆ null ┆ 2022-07-22 ┆ null │\n", - "│ ┆ Perry ┆ ┆ ┆ ┆ ┆ ┆ 02:15:33 ┆ │\n", - "│ 4 ┆ Sean Sanchez ┆ male ┆ 34 ┆ … ┆ null ┆ 15:52:55 ┆ 2022-08-08 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 17:34:23 ┆ │\n", - "│ 5 ┆ Laurie Adams ┆ male ┆ 24 ┆ … ┆ 1913-12-13 ┆ 13:07:55 ┆ 2022-08-04 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 07:46:23 ┆ │\n", - "└─────────────┴──────────────┴────────┴──────┴───┴────────────┴────────────┴──────────────┴────────┘" + "┌─────────────┬───────────┬────────┬─────┬───┬────────────┬────────────┬───────────────┬────────┐\n", + "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married since ┆ all_NA │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ datetime[μs] ┆ str │\n", + "╞═════════════╪═══════════╪════════╪═════╪═══╪════════════╪════════════╪═══════════════╪════════╡\n", + "│ 1 ┆ Jesus ┆ male ┆ 12 ┆ … ┆ 1913-05-13 ┆ 17:59:25 ┆ 2022-08-01 ┆ null │\n", + "│ ┆ Armstrong ┆ ┆ ┆ ┆ ┆ ┆ 04:52:02 ┆ │\n", + "│ 2 ┆ Jeff Ball ┆ female ┆ 11 ┆ … ┆ 1921-05-06 ┆ 12:40:37 ┆ null ┆ null │\n", + "│ 3 ┆ Lindsey ┆ female ┆ 36 ┆ … ┆ 1929-07-28 ┆ 15:55:15 ┆ 2022-08-15 ┆ null │\n", + "│ ┆ Collins ┆ ┆ ┆ ┆ ┆ ┆ 04:35:43 ┆ │\n", + "│ 4 ┆ Vincent ┆ male ┆ 45 ┆ … ┆ 1919-03-30 ┆ null ┆ 2022-08-09 ┆ null │\n", + "│ ┆ Stevenson ┆ ┆ ┆ ┆ ┆ ┆ 06:01:43 ┆ │\n", + "│ 5 ┆ Michele ┆ male ┆ 48 ┆ … ┆ 1926-09-06 ┆ 13:46:37 ┆ 2022-07-18 ┆ null │\n", + "│ ┆ Whitney ┆ ┆ ┆ ┆ ┆ ┆ 13:16:16 ┆ │\n", + "└─────────────┴───────────┴────────┴─────┴───┴────────────┴────────────┴───────────────┴────────┘" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1259,7 +767,6 @@ "from metasyn.distribution import FakerDistribution\n", "\n", "var_specs = [\n", - " VarSpec(\"PassengerId\", unique=True),\n", " VarSpec(\"Name\", distribution=FakerDistribution(\"name\")),\n", "]\n", "\n", @@ -1289,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "ac4ba506", "metadata": {}, "outputs": [ @@ -1297,7 +804,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 13/13 [00:00<00:00, 19.44it/s]\n" + "100%|██████████| 13/13 [00:00<00:00, 18.91it/s]\n" ] }, { @@ -1310,7 +817,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Jessica Pena""male"340"28283"0.224997null"S"1928-11-1315:14:592022-07-23 00:08:48null
    2"Paul Burke""male"210"87671"3.173168"E66""S"1940-05-0710:43:112022-07-28 20:28:12null
    3"Seth Moore""female"320"72893"1.422964null"C"1932-12-0613:41:312022-07-21 07:54:26null
    4"Melissa Collins""female"330"2627"1.864894null"S"1920-06-0911:14:332022-07-19 19:12:19null
    5"Michael Barrera""female"220"29098"1.940266null"S"1920-11-08null2022-07-27 08:57:22null
    " + "shape: (5, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Steven Solis""male"260"06781"4.426615null"Q"1917-10-3116:19:122022-08-03 02:07:29null
    2"Courtney Ramirez""male"350"63056"2.3421null"Q"1931-03-1618:01:242022-07-31 10:38:49null
    3"Yvonne Hansen""male"301"5978"0.157967null"C"null16:47:232022-07-15 19:29:30null
    4"Mark Green""male"240"3317"0.880757null"S"1906-04-1713:02:332022-07-15 23:48:15null
    5"Tony Reynolds""female"390"195928"0.422501null"C"1910-08-1916:04:092022-08-03 02:35:05null
    " ], "text/plain": [ "shape: (5, 13)\n", @@ -1320,20 +827,20 @@ "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", "╞═════════════╪═══════════════╪════════╪═════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", - "│ 1 ┆ Jessica Pena ┆ male ┆ 34 ┆ … ┆ 1928-11-13 ┆ 15:14:59 ┆ 2022-07-23 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 00:08:48 ┆ │\n", - "│ 2 ┆ Paul Burke ┆ male ┆ 21 ┆ … ┆ 1940-05-07 ┆ 10:43:11 ┆ 2022-07-28 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 20:28:12 ┆ │\n", - "│ 3 ┆ Seth Moore ┆ female ┆ 32 ┆ … ┆ 1932-12-06 ┆ 13:41:31 ┆ 2022-07-21 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 07:54:26 ┆ │\n", - "│ 4 ┆ Melissa ┆ female ┆ 33 ┆ … ┆ 1920-06-09 ┆ 11:14:33 ┆ 2022-07-19 ┆ null │\n", - "│ ┆ Collins ┆ ┆ ┆ ┆ ┆ ┆ 19:12:19 ┆ │\n", - "│ 5 ┆ Michael ┆ female ┆ 22 ┆ … ┆ 1920-11-08 ┆ null ┆ 2022-07-27 ┆ null │\n", - "│ ┆ Barrera ┆ ┆ ┆ ┆ ┆ ┆ 08:57:22 ┆ │\n", + "│ 1 ┆ Steven Solis ┆ male ┆ 26 ┆ … ┆ 1917-10-31 ┆ 16:19:12 ┆ 2022-08-03 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 02:07:29 ┆ │\n", + "│ 2 ┆ Courtney ┆ male ┆ 35 ┆ … ┆ 1931-03-16 ┆ 18:01:24 ┆ 2022-07-31 ┆ null │\n", + "│ ┆ Ramirez ┆ ┆ ┆ ┆ ┆ ┆ 10:38:49 ┆ │\n", + "│ 3 ┆ Yvonne Hansen ┆ male ┆ 30 ┆ … ┆ null ┆ 16:47:23 ┆ 2022-07-15 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 19:29:30 ┆ │\n", + "│ 4 ┆ Mark Green ┆ male ┆ 24 ┆ … ┆ 1906-04-17 ┆ 13:02:33 ┆ 2022-07-15 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 23:48:15 ┆ │\n", + "│ 5 ┆ Tony Reynolds ┆ female ┆ 39 ┆ … ┆ 1910-08-19 ┆ 16:04:09 ┆ 2022-08-03 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 02:35:05 ┆ │\n", "└─────────────┴───────────────┴────────┴─────┴───┴────────────┴────────────┴──────────────┴────────┘" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1342,7 +849,6 @@ "from metasyn.distribution import DiscreteUniformDistribution\n", "\n", "var_specs = [\n", - " VarSpec(\"PassengerId\", unique=True),\n", " VarSpec(\"Name\", distribution=FakerDistribution(\"name\")),\n", " VarSpec(\"Fare\", distribution=\"lognormal\"), # estimate / fit an exponential distribution based on the data\n", " VarSpec(\"Age\", distribution=DiscreteUniformDistribution(20, 40)) # fully specify a distribution for age (uniform between 20 and 40)\n", @@ -1364,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "7b2c4003", "metadata": {}, "outputs": [ @@ -1372,7 +878,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 13/13 [00:00<00:00, 23.80it/s]\n" + "100%|██████████| 13/13 [00:00<00:00, 23.38it/s]\n" ] }, { @@ -1385,7 +891,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (10, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Martin Smith""male"270"983430"1.164238null"S"1905-06-2015:20:362022-07-29 02:33:29null
    2"Jeremy Lee""female"250"OB.A. 554374"0.78469"A61""S"1915-11-1816:31:58nullnull
    3"Megan Wright""female"210"02990"0.316863null"C"1932-01-1810:58:572022-08-12 10:10:27null
    4"Jonathon Ward""male"340"60614"0.256896null"C"1907-07-2013:08:412022-07-26 22:05:29null
    5"Monica White""male"250"319646"1.445188null"S"1909-11-1813:53:402022-07-23 14:19:42null
    6"James Bryant""male"391"8807"1.767202null"S"1923-08-2010:42:542022-07-24 17:42:18null
    7"Patricia Murphy""male"null0"2227"1.57518null"S"1909-08-2914:40:332022-08-09 11:07:34null
    8"Jeremy Kane""female"370"1951"0.399932null"S"1912-04-26null2022-08-10 13:27:33null
    9"Matthew Baker""male"390"712759"3.5895null"S"null18:36:302022-07-20 13:01:07null
    10"Michael Cox""male"380"64820"0.377606null"S"1930-01-1714:44:28nullnull
    " + "shape: (10, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    i64strcati64i64strf64strcatdatetimedatetime[μs]str
    1"Timothy Davidson""male"null0"FU 597164"2.837321null"C"1907-06-1712:23:182022-08-09 15:00:58null
    2"Christine Russell""female"310"61912"1.610465"B042""C"1916-05-1615:33:512022-07-17 05:51:50null
    3"James Morris DVM""male"220"178671"0.835741"E958""S"1934-10-2918:22:11nullnull
    4"Jared Hart""female"290"380514"2.904262"A48""S"null14:07:082022-07-28 05:03:04null
    5"Jamie Medina""male"200"0050"1.432564null"Q"1932-10-06null2022-07-16 00:21:03null
    6"Karen Rodriguez""female"290"9589"0.570791null"Q"1939-12-2316:39:012022-08-04 15:56:22null
    7"Richard Young""female"null1"974010"3.177976null"S"1916-03-11null2022-07-26 15:39:12null
    8"Tiffany Jones""male"250"965653"0.801674null"S"1919-08-2717:00:252022-08-11 19:24:36null
    9"Richard Andrews""male"280"93545"1.798628null"S"1933-01-3017:36:252022-07-23 10:37:15null
    10"Rebecca Bradshaw DDS""female"null0"QAAV 834235"1.917046"D862""Q"1905-09-1512:40:01nullnull
    " ], "text/plain": [ "shape: (10, 13)\n", @@ -1395,28 +901,30 @@ "│ i64 ┆ str ┆ cat ┆ i64 ┆ ┆ date ┆ time ┆ --- ┆ str │\n", "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", "╞═════════════╪══════════════╪════════╪══════╪═══╪════════════╪════════════╪══════════════╪════════╡\n", - "│ 1 ┆ Martin Smith ┆ male ┆ 27 ┆ … ┆ 1905-06-20 ┆ 15:20:36 ┆ 2022-07-29 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 02:33:29 ┆ │\n", - "│ 2 ┆ Jeremy Lee ┆ female ┆ 25 ┆ … ┆ 1915-11-18 ┆ 16:31:58 ┆ null ┆ null │\n", - "│ 3 ┆ Megan Wright ┆ female ┆ 21 ┆ … ┆ 1932-01-18 ┆ 10:58:57 ┆ 2022-08-12 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 10:10:27 ┆ │\n", - "│ 4 ┆ Jonathon ┆ male ┆ 34 ┆ … ┆ 1907-07-20 ┆ 13:08:41 ┆ 2022-07-26 ┆ null │\n", - "│ ┆ Ward ┆ ┆ ┆ ┆ ┆ ┆ 22:05:29 ┆ │\n", - "│ 5 ┆ Monica White ┆ male ┆ 25 ┆ … ┆ 1909-11-18 ┆ 13:53:40 ┆ 2022-07-23 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 14:19:42 ┆ │\n", - "│ 6 ┆ James Bryant ┆ male ┆ 39 ┆ … ┆ 1923-08-20 ┆ 10:42:54 ┆ 2022-07-24 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 17:42:18 ┆ │\n", - "│ 7 ┆ Patricia ┆ male ┆ null ┆ … ┆ 1909-08-29 ┆ 14:40:33 ┆ 2022-08-09 ┆ null │\n", - "│ ┆ Murphy ┆ ┆ ┆ ┆ ┆ ┆ 11:07:34 ┆ │\n", - "│ 8 ┆ Jeremy Kane ┆ female ┆ 37 ┆ … ┆ 1912-04-26 ┆ null ┆ 2022-08-10 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 13:27:33 ┆ │\n", - "│ 9 ┆ Matthew ┆ male ┆ 39 ┆ … ┆ null ┆ 18:36:30 ┆ 2022-07-20 ┆ null │\n", - "│ ┆ Baker ┆ ┆ ┆ ┆ ┆ ┆ 13:01:07 ┆ │\n", - "│ 10 ┆ Michael Cox ┆ male ┆ 38 ┆ … ┆ 1930-01-17 ┆ 14:44:28 ┆ null ┆ null │\n", + "│ 1 ┆ Timothy ┆ male ┆ null ┆ … ┆ 1907-06-17 ┆ 12:23:18 ┆ 2022-08-09 ┆ null │\n", + "│ ┆ Davidson ┆ ┆ ┆ ┆ ┆ ┆ 15:00:58 ┆ │\n", + "│ 2 ┆ Christine ┆ female ┆ 31 ┆ … ┆ 1916-05-16 ┆ 15:33:51 ┆ 2022-07-17 ┆ null │\n", + "│ ┆ Russell ┆ ┆ ┆ ┆ ┆ ┆ 05:51:50 ┆ │\n", + "│ 3 ┆ James Morris ┆ male ┆ 22 ┆ … ┆ 1934-10-29 ┆ 18:22:11 ┆ null ┆ null │\n", + "│ ┆ DVM ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "│ 4 ┆ Jared Hart ┆ female ┆ 29 ┆ … ┆ null ┆ 14:07:08 ┆ 2022-07-28 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 05:03:04 ┆ │\n", + "│ 5 ┆ Jamie Medina ┆ male ┆ 20 ┆ … ┆ 1932-10-06 ┆ null ┆ 2022-07-16 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 00:21:03 ┆ │\n", + "│ 6 ┆ Karen ┆ female ┆ 29 ┆ … ┆ 1939-12-23 ┆ 16:39:01 ┆ 2022-08-04 ┆ null │\n", + "│ ┆ Rodriguez ┆ ┆ ┆ ┆ ┆ ┆ 15:56:22 ┆ │\n", + "│ 7 ┆ Richard ┆ female ┆ null ┆ … ┆ 1916-03-11 ┆ null ┆ 2022-07-26 ┆ null │\n", + "│ ┆ Young ┆ ┆ ┆ ┆ ┆ ┆ 15:39:12 ┆ │\n", + "│ 8 ┆ Tiffany ┆ male ┆ 25 ┆ … ┆ 1919-08-27 ┆ 17:00:25 ┆ 2022-08-11 ┆ null │\n", + "│ ┆ Jones ┆ ┆ ┆ ┆ ┆ ┆ 19:24:36 ┆ │\n", + "│ 9 ┆ Richard ┆ male ┆ 28 ┆ … ┆ 1933-01-30 ┆ 17:36:25 ┆ 2022-07-23 ┆ null │\n", + "│ ┆ Andrews ┆ ┆ ┆ ┆ ┆ ┆ 10:37:15 ┆ │\n", + "│ 10 ┆ Rebecca ┆ female ┆ null ┆ … ┆ 1905-09-15 ┆ 12:40:01 ┆ null ┆ null │\n", + "│ ┆ Bradshaw DDS ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n", "└─────────────┴──────────────┴────────┴──────┴───┴────────────┴────────────┴──────────────┴────────┘" ] }, - "execution_count": 17, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1432,7 +940,6 @@ "\n", "\n", "var_specs = [\n", - " VarSpec(\"PassengerId\", unique=True),\n", " VarSpec(\"Name\", distribution=FakerDistribution(\"name\")),\n", " VarSpec(\"Fare\", distribution=\"lognormal\"), # estimate / fit an exponential distribution based on the data\n", " VarSpec(\"Age\", distribution=DiscreteUniformDistribution(20, 40)), # fully specify a distribution for age (uniform between 20 and 40)\n", @@ -1461,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "a857ee6e", "metadata": {}, "outputs": [ @@ -1491,7 +998,7 @@ "└─────────────┴──────┴──────┴───────────┴───┴───────────────┴──────────────┴──────────────┴────────┘" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1502,7 +1009,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "0ba303f3", "metadata": {}, "outputs": [ @@ -1516,23 +1023,23 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    f64strcatf64f64strf64strcatdatetime[ms]timedatetime[μs]str
    446.0nullnull29.528490.080808null1.633843nullnull1921-05-09 01:59:16.78314:41:29.5340768272022-07-31 02:46:03.870927null
    " + "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    f64strcatf64f64strf64strcatdatetime[ms]timedatetime[μs]str
    446.0nullnull29.3536930.05275null1.573629nullnull1922-02-15 09:31:27.37914:42:30.6123456792022-07-31 05:06:31.243750null
    " ], "text/plain": [ "shape: (1, 13)\n", - "┌─────────────┬──────┬──────┬──────────┬───┬───────────────┬───────────────┬──────────────┬────────┐\n", - "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", - "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", - "│ f64 ┆ str ┆ cat ┆ f64 ┆ ┆ datetime[ms] ┆ time ┆ --- ┆ str │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", - "╞═════════════╪══════╪══════╪══════════╪═══╪═══════════════╪═══════════════╪══════════════╪════════╡\n", - "│ 446.0 ┆ null ┆ null ┆ 29.52849 ┆ … ┆ 1921-05-09 ┆ 14:41:29.5340 ┆ 2022-07-31 ┆ null │\n", - "│ ┆ ┆ ┆ ┆ ┆ 01:59:16.783 ┆ 76827 ┆ 02:46:03.870 ┆ │\n", - "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 927 ┆ │\n", - "└─────────────┴──────┴──────┴──────────┴───┴───────────────┴───────────────┴──────────────┴────────┘" + "┌─────────────┬──────┬──────┬───────────┬───┬───────────────┬──────────────┬──────────────┬────────┐\n", + "│ PassengerId ┆ Name ┆ Sex ┆ Age ┆ … ┆ Birthday ┆ Board time ┆ Married ┆ all_NA │\n", + "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ since ┆ --- │\n", + "│ f64 ┆ str ┆ cat ┆ f64 ┆ ┆ datetime[ms] ┆ time ┆ --- ┆ str │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ datetime[μs] ┆ │\n", + "╞═════════════╪══════╪══════╪═══════════╪═══╪═══════════════╪══════════════╪══════════════╪════════╡\n", + "│ 446.0 ┆ null ┆ null ┆ 29.353693 ┆ … ┆ 1922-02-15 ┆ 14:42:30.612 ┆ 2022-07-31 ┆ null │\n", + "│ ┆ ┆ ┆ ┆ ┆ 09:31:27.379 ┆ 345679 ┆ 05:06:31.243 ┆ │\n", + "│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 750 ┆ │\n", + "└─────────────┴──────┴──────┴───────────┴───┴───────────────┴──────────────┴──────────────┴────────┘" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1551,7 +1058,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "c6ca2918", "metadata": {}, "outputs": [ @@ -1578,7 +1085,7 @@ "└─────────────┴──────┴─────┴─────┴───┴──────────┴────────────┴───────────────┴────────┘" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1589,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "1dd596fb", "metadata": {}, "outputs": [ @@ -1603,7 +1110,7 @@ " white-space: pre-wrap;\n", "}\n", "\n", - "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    u32u32u32u32u32u32u32u32u32u32u32u32u32
    0001850006852876893891
    " + "shape: (1, 13)
    PassengerIdNameSexAgeParchTicketFareCabinEmbarkedBirthdayBoard timeMarried sinceall_NA
    u32u32u32u32u32u32u32u32u32u32u32u32u32
    0001680007028788492891
    " ], "text/plain": [ "shape: (1, 13)\n", @@ -1612,11 +1119,11 @@ "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ u32 ┆ u32 ┆ u32 ┆ u32 ┆ ┆ u32 ┆ u32 ┆ u32 ┆ u32 │\n", "╞═════════════╪══════╪═════╪═════╪═══╪══════════╪════════════╪═══════════════╪════════╡\n", - "│ 0 ┆ 0 ┆ 0 ┆ 185 ┆ … ┆ 87 ┆ 68 ┆ 93 ┆ 891 │\n", + "│ 0 ┆ 0 ┆ 0 ┆ 168 ┆ … ┆ 78 ┆ 84 ┆ 92 ┆ 891 │\n", "└─────────────┴──────┴─────┴─────┴───┴──────────┴────────────┴───────────────┴────────┘" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1649,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "e37faad4df8ffde8", "metadata": { "collapsed": false @@ -1659,15 +1166,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 13/13 [00:00<00:00, 24.19it/s]\n" + "100%|██████████| 13/13 [00:00<00:00, 21.15it/s]\n" ] } ], "source": [ "var_specs = [\n", - " # Ensure unique values for the `PassengerId` column\n", - " VarSpec(\"PassengerId\", unique=True),\n", - "\n", " # Utilize the Faker library to synthesize realistic names for the `Name` column\n", " VarSpec(\"Name\", distribution=FakerDistribution(\"name\")),\n", "\n", @@ -1696,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "694d4474707f7950", "metadata": { "collapsed": false @@ -1726,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "id": "34a8aafc95c0219f", "metadata": { "collapsed": false @@ -1758,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "3b2e873f7362160a", "metadata": { "collapsed": false @@ -1794,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "ab2b34ed6b11578c", "metadata": { "collapsed": false diff --git a/examples/gmf_files/example_gmf_titanic.json b/examples/gmf_files/example_gmf_titanic.json index 776c3b5c..2eeedc2f 100644 --- a/examples/gmf_files/example_gmf_titanic.json +++ b/examples/gmf_files/example_gmf_titanic.json @@ -6,7 +6,7 @@ "name": "metasyn", "version": "1.0.2.dev34+gd68929e" }, - "creation time": "2024-10-03T14:56:33.372136" + "creation time": "2024-10-18T10:35:19.433034" }, "vars": [ { @@ -15,14 +15,14 @@ "dtype": "Int64", "prop_missing": 0.0, "distribution": { - "implements": "core.uniform", + "implements": "core.unique_key", "version": "1.0", "provenance": "builtin", - "class_name": "DiscreteUniformDistribution", - "unique": false, + "class_name": "UniqueKeyDistribution", + "unique": true, "parameters": { "lower": 1, - "upper": 892 + "consecutive": true } }, "creation_method": { diff --git a/metasyn/provider.py b/metasyn/provider.py index 9753e968..2fbaf794 100644 --- a/metasyn/provider.py +++ b/metasyn/provider.py @@ -285,13 +285,19 @@ def _find_best_fit(self, series: pl.Series, var_type: str, if len(dist_list_unq) > 0: dist_inst_unq = [d.fit(series, **privacy.fit_kwargs) for d in dist_list_unq] dist_bic_unq = [d.information_criterion(series) for d in dist_inst_unq] - if np.min(dist_bic_unq) < np.min(dist_bic): + # We don't want to warn about potential uniqueness too easily + # The offset is a heuristic that ensures about 12 rows are needed for uniqueness + # Or 5 rows for consecutive values. + if np.min(dist_bic_unq) + 16 < np.min(dist_bic): + best_dist = dist_inst_unq[np.argmin(dist_bic_unq)] + if best_dist.implements == "core.unique_key" and best_dist.consecutive: # type: ignore + return best_dist warnings.warn( - f"\nVariable '{series.name}' was detected to be unique, but has not" - f" explicitly been set to unique.\n" - f"To generate only unique values for column '{series.name}', " - f"set unique to True.\n" - f"To dismiss this warning, set unique to False.", + f"\nMetasyn detected that variable '{series.name}' is potentially unique.\n" + f"Use var_spec=[VarSpec(\"{series.name}\", unique=True)] to make it unique." + f"\nTo dismiss this warning use [VarSpec(\"{series.name}\", unique=False)]." + "\nIf you are using a configuration file add distribution = {unique = True}" + f" for the variable with name '{series.name}'.", UserWarning ) diff --git a/pyproject.toml b/pyproject.toml index 1226b6ed..7311811a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Development Status :: 4 - Beta", "License :: OSI Approved :: MIT License", ] @@ -52,7 +53,7 @@ extra = ["xlsxwriter", "pandas", "tomlkit"] check = ["ruff", "mypy", "types-tqdm", "types-regex"] test = ["pytest", "nbval"] docs = [ - "sphinx<7.0.0", "sphinx-rtd-theme", "sphinxcontrib-napoleon", + "sphinx<9.0.0", "sphinx-rtd-theme", "sphinxcontrib-napoleon", "sphinx-autodoc-typehints", "sphinx_inline_tabs", "sphinx_copybutton", ] dev = [ From 5b70ef60088761fc556317215bc4bcf5308abd85 Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Mon, 28 Oct 2024 14:05:24 +0100 Subject: [PATCH 10/17] Improve the toml comments (#333) --- metasyn/metaframe.py | 28 ++++++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index 37078a5b..22645fdd 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -381,14 +381,30 @@ def save_toml(self, fp: Optional[Union[pathlib.Path, str]], # The below comment does not work, a tomlkit bug? # doc["vars"][i]["distribution"]["unique"].add(tomlkit.comment( # "Whether to generate unique values or not")) - if "privacy" in var.creation_method: + parameter_comments = [] + multi_default = ( + var.distribution.matches_name("multinoulli") and + len(var.distribution.labels) == len(var.distribution.default_distribution().labels) + and + np.all(var.distribution.labels == var.distribution.default_distribution().labels) + ) + print(var.name, multi_default) + if "parameters" in var.creation_method: + parameter_comments.append(f"The above parameters for column '{var.name}' were " + "manually set by the user, no data was (directly) used.") + elif (var.distribution.matches_name("multinoulli") and multi_default): + parameter_comments.append("This mulinoulli distribution is the default one, no data" + " was used.") + elif "privacy" in var.creation_method: privacy = get_privacy(**var.creation_method["privacy"]) - doc["vars"][i]["distribution"]["parameters"].add(tomlkit.comment(privacy.comment(var))) - if var.distribution.matches_name("multinoulli"): + parameter_comments.append(privacy.comment(var)) + if (var.distribution.matches_name("multinoulli") and not multi_default): counts = (var.distribution.probs*(1-var.prop_missing)*self.n_rows).round() - doc["vars"][i]["distribution"].add(tomlkit.comment( - f"Counts: {counts.astype(int)}\n")) - + parameter_comments.append(f"Counts: {counts.astype(int)}\n") + # doc["vars"][i]["distribution"].add(tomlkit.comment( + # f"Counts: {counts.astype(int)}\n")) + par_comment = "\n# ".join(parameter_comments) + "\n\n" + doc["vars"][i]["distribution"]["parameters"].add(tomlkit.comment(par_comment)) if fp is None: print(tomlkit.dumps(doc)) else: From be1aa229f383c6dd80e8c01a1831e0c89e80fdfa Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Wed, 30 Oct 2024 16:35:13 +0100 Subject: [PATCH 11/17] Change utctimestamp -> timestamp (#336) We don't know the actual time zone, so this seems a better solution. --- metasyn/distribution/datetime.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/metasyn/distribution/datetime.py b/metasyn/distribution/datetime.py index b885ec1c..7ab408bb 100644 --- a/metasyn/distribution/datetime.py +++ b/metasyn/distribution/datetime.py @@ -26,7 +26,7 @@ def convert_numpy_datetime(time_obj: np.datetime64) -> dt.datetime: unix_epoch = np.datetime64(0, 's') one_second = np.timedelta64(1, 's') seconds_since_epoch = (time_obj - unix_epoch) / one_second - return dt.datetime.utcfromtimestamp(float(seconds_since_epoch)) + return dt.datetime.fromtimestamp(float(seconds_since_epoch)) class BaseUniformDistribution(BaseDistribution): From 81905bd449596a159e543401eaab7f10bc27e69b Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Mon, 11 Nov 2024 14:03:33 +0100 Subject: [PATCH 12/17] Improve test documentation (#335) Also fix some quirkiness in the NA distribution fitting. --- examples/config_files/example_all.toml | 11 ++++ metasyn/distribution/na.py | 2 +- metasyn/provider.py | 50 +++++++++------ tests/test_builtin.py | 10 ++- tests/test_cli.py | 11 +++- tests/test_config.py | 6 ++ tests/test_continuous.py | 8 ++- tests/test_dataset.py | 12 +++- tests/test_datetime.py | 4 ++ tests/test_discrete.py | 9 ++- tests/test_distribution.py | 36 ----------- tests/test_na.py | 14 ---- tests/test_privacy.py | 5 +- tests/test_provider.py | 85 ++++++++++++++++++++----- tests/{test_faker.py => test_string.py} | 7 +- tests/test_toml.py | 8 ++- tests/test_var.py | 68 ++++++++++++++++---- 17 files changed, 232 insertions(+), 114 deletions(-) delete mode 100644 tests/test_distribution.py delete mode 100644 tests/test_na.py rename tests/{test_faker.py => test_string.py} (83%) diff --git a/examples/config_files/example_all.toml b/examples/config_files/example_all.toml index c4bfd61f..1e46b7ae 100644 --- a/examples/config_files/example_all.toml +++ b/examples/config_files/example_all.toml @@ -329,3 +329,14 @@ unique = false [var.distribution.parameters] value = "10:39:36" + +[[var]] +name = "NADistribution" +var_type = "continuous" + +[var.distribution] +implements = "core.na" +unique = false + +[var.distribution.parameters] + diff --git a/metasyn/distribution/na.py b/metasyn/distribution/na.py index 05411546..1e541ea1 100644 --- a/metasyn/distribution/na.py +++ b/metasyn/distribution/na.py @@ -9,7 +9,7 @@ from metasyn.distribution.base import BaseDistribution, metadist -@metadist(implements="core.na", var_type="string") +@metadist(implements="core.na", var_type=["continuous", "discrete", "categorical", "string"]) class NADistribution(BaseDistribution): """Distribution that always returns NA values (None).""" diff --git a/metasyn/provider.py b/metasyn/provider.py index 2fbaf794..ad40875a 100644 --- a/metasyn/provider.py +++ b/metasyn/provider.py @@ -155,6 +155,7 @@ class BuiltinDistributionProvider(BaseDistributionProvider): DateTimeConstantDistribution, DateConstantDistribution, TimeConstantDistribution, + NADistribution, ] legacy_distributions = [] @@ -331,12 +332,9 @@ def find_distribution(self, tuple[Type[BaseDistribution], dict[str, Any]]: A distribution and the arguments to create an instance. """ - if NADistribution.matches_name(dist_name): - return NADistribution - versions_found = [] for dist_class in self.get_distributions( - privacy, var_type=var_type, unique=unique) + [NADistribution]: + privacy, var_type=var_type, unique=unique): if dist_class.matches_name(dist_name): if version is None or version == dist_class.version: return dist_class @@ -351,18 +349,7 @@ def find_distribution(self, if dist_class.matches_name(dist_name)] if len(legacy_distribs + versions_found) == 0: - registry = get_registry() - available = {} - for plugin, meta in registry.items(): - if dist_name in meta["distributions"]: - available[plugin] = meta["url"] - if len(available) > 0: - avail_str = "\n".join("{plugin}: {url}" for plugin, url in available.items()) - raise ValueError(f"You are trying to use a distribution named '{dist_name}', \n" - f"but it is not installed.\n" - f"\n" - f"{dist_name} is available from:\n\n{avail_str}\n") - raise ValueError(f"Cannot find distribution with name '{dist_name}'.") + self._handle_distribution_not_found(dist_name, var_type, privacy, unique, version) if len(versions_found) == 0: warnings.warn("Distribution with name '{dist_name}' is deprecated and " @@ -400,6 +387,30 @@ def find_distribution(self, warnings.warn("Version mismatch ({version}) versus ({all_dist[i_max].version}))") return all_dist[i_max] + def _handle_distribution_not_found(self, dist_name, var_type, privacy, unique, version): + """Raise a ValueError with extra information.""" + registry = get_registry() + available = {} + for plugin, meta in registry.items(): + if dist_name in meta["distributions"]: + available[plugin] = meta["url"] + if len(available) > 0: + avail_str = "\n".join("{plugin}: {url}" for plugin, url in available.items()) + raise ValueError(f"You are trying to use a distribution named '{dist_name}', \n" + f"but it is not installed.\n" + f"\n" + f"{dist_name} is available from:\n\n{avail_str}\n") + if var_type is not None: + dist_other_type = self.find_distribution(dist_name, var_type=None, + privacy=privacy, unique=unique, + version=version) + extra_info = ("Distribution is available with different type(s):" + f" {dist_other_type.var_type}") + else: + extra_info = "" + raise ValueError(f"Cannot find distribution with name '{dist_name}'. {extra_info}") + + def _fit_distribution(self, series: pl.Series, dist_spec: DistributionSpec, var_type: str, @@ -436,11 +447,8 @@ def _fit_distribution(self, series: pl.Series, dist_class = self.find_distribution(dist_spec.implements, var_type, privacy=privacy, unique=unique) - if issubclass(dist_class, NADistribution): - dist_instance = dist_class.default_distribution() - else: - fit_kwargs = dist_spec.fit_kwargs - dist_instance = dist_class.fit(series, **privacy.fit_kwargs, **fit_kwargs) + fit_kwargs = dist_spec.fit_kwargs + dist_instance = dist_class.fit(series, **privacy.fit_kwargs, **fit_kwargs) return dist_instance def get_distributions(self, privacy: Optional[BasePrivacy] = None, diff --git a/tests/test_builtin.py b/tests/test_builtin.py index 7d489cef..c2e4a0ba 100644 --- a/tests/test_builtin.py +++ b/tests/test_builtin.py @@ -1,3 +1,4 @@ +"""Testing module for the distributions that are builtin to metasyn directly.""" from pytest import mark, raises from metasyn.distribution import UniformDistribution @@ -7,6 +8,7 @@ def test_builtin_provider(): + """Test the builtin distribution provider with the metasyn test functionality.""" check_distribution_provider("builtin") @@ -14,15 +16,19 @@ def test_builtin_provider(): "distribution", get_distribution_provider("builtin").distributions ) def test_dist_validation(distribution): + """Check all distributions for correctness.""" check_distribution(distribution, privacy=BasicPrivacy(), provenance="builtin") -class Distribution(UniformDistribution): +class BadDistribution(UniformDistribution): + """Distribution that has a broken schema.""" + def schema(): return "[{" def test_schema_val(): + """Check whether a distribution with a broken schema will trigger the error.""" with raises(ValueError): - check_distribution(Distribution, "none", "builtin") + check_distribution(BadDistribution, "none", "builtin") diff --git a/tests/test_cli.py b/tests/test_cli.py index eec4f998..1fe28997 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,3 +1,4 @@ +"""Module with tests for the command line interface.""" import json import subprocess import sys @@ -15,6 +16,7 @@ @fixture(scope="module") def tmp_dir(tmp_path_factory) -> Path: + """Directory with a configuration, dataset and GMF file.""" global TMP_DIR_PATH if TMP_DIR_PATH is None: # Create a temporary input file @@ -36,7 +38,8 @@ def tmp_dir(tmp_path_factory) -> Path: "Fare": float } data_frame = pl.read_csv(csv_fp, schema_overrides=csv_dt)[:100] - meta_frame = MetaFrame.fit_dataframe(data_frame, var_specs=[{"name": "PassengerId", "distribution": {"unique": True}}]) + meta_frame = MetaFrame.fit_dataframe(data_frame, var_specs=[ + {"name": "PassengerId", "distribution": {"unique": True}}]) meta_frame.save_json(json_path) config_fp = TMP_DIR_PATH / "config.ini" with open(config_fp, "w") as handle: @@ -55,7 +58,7 @@ def tmp_dir(tmp_path_factory) -> Path: @mark.parametrize("ext", [".csv", ".feather", ".parquet", ".pkl", ".xlsx"]) def test_cli(tmp_dir, ext): - """A simple integration test for reading and writing using the CLI""" + """A simple integration test for creating synthetic data from a GMF file.""" # create out file path with correct extension out_file = tmp_dir / f"titanic{ext}" @@ -81,6 +84,7 @@ def test_cli(tmp_dir, ext): @mark.parametrize("config", [True, False]) def test_create_meta(tmp_dir, config): + """CLI test on creating metadata from a csv.""" out_file = tmp_dir / "test.json" cmd = [ Path(sys.executable).resolve(), # the python executable @@ -100,6 +104,7 @@ def test_create_meta(tmp_dir, config): def test_schema_list(): + """Test whether all plugins/schemas are listed.""" cmd = [ Path(sys.executable).resolve(), # the python executable Path("metasyn", "__main__.py"), # the cli script @@ -112,6 +117,7 @@ def test_schema_list(): def test_schema_gen(tmp_dir): + """Test whether the metadata schemas can be created.""" titanic_json = tmp_dir / "titanic.json" cmd = [ Path(sys.executable).resolve(), # the python executable @@ -133,6 +139,7 @@ def test_schema_gen(tmp_dir): def test_datafree(tmp_dir): + """Test generating synthetic data from only a configuration file and no real data.""" gmf_fp = tmp_dir / "gmf_out.json" syn_fp = tmp_dir / "test_out.csv" cmd = [ diff --git a/tests/test_config.py b/tests/test_config.py index b2058d69..a0996af8 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -1,3 +1,5 @@ +"""Module for testing parsing configurations (from file or otherwise).""" + from pathlib import Path import pytest @@ -25,6 +27,7 @@ ] ) def test_dist_spec(input, error): + """Test parsing distribution parameters for different inputs.""" if error: with pytest.raises(Exception): DistributionSpec.parse(input) @@ -33,6 +36,7 @@ def test_dist_spec(input, error): assert isinstance(dist_spec, DistributionSpec) def test_var_spec(): + """Test various ways to initialize the VarSpec class.""" var_spec = VarSpec("test", privacy={"name": "none", "parameters": {}}) assert var_spec.name == "test" assert isinstance(var_spec.privacy, BasePrivacy) @@ -47,6 +51,7 @@ def test_var_spec(): def test_meta_config_datafree(): + """Test for creating a MetaConfig class from a .toml file without data.""" meta_config = MetaConfig.from_toml(Path("tests", "data", "no_data_config.toml")) assert meta_config.n_rows == 100 assert len(meta_config.var_specs) == 3 @@ -69,6 +74,7 @@ def test_meta_config_datafree(): def test_meta_config(): + """Test the creation of a MetaConfig class that is not data free.""" meta_config = MetaConfig.from_toml(Path("tests", "data", "example_config.toml")) assert len(meta_config.var_specs) == 5 var_spec = meta_config.get("Cabin") diff --git a/tests/test_continuous.py b/tests/test_continuous.py index 26a9b94e..9ee17637 100644 --- a/tests/test_continuous.py +++ b/tests/test_continuous.py @@ -1,3 +1,4 @@ +"""Testing module for continuous distributions.""" import numpy as np from pytest import mark from scipy import stats @@ -20,12 +21,13 @@ ] ) def test_uniform(lower, upper): + """Test the continuous uniform distribution.""" scale = upper-lower values = stats.uniform(loc=lower, scale=scale).rvs(100) dist = UniformDistribution.fit(values) assert dist.lower <= values.min() assert dist.upper >= values.max() - assert dist.information_criterion(values) < 2*np.log(len(values)) - 200*np.log((upper-lower)**-1) + assert dist.information_criterion(values) < 2*np.log(len(values))-200*np.log((upper-lower)**-1) assert isinstance(dist.draw(), float) @@ -38,6 +40,7 @@ def test_uniform(lower, upper): ] ) def test_normal(mean, sd): + """Test the normal/Gaussian distribution.""" values = stats.norm(loc=mean, scale=sd).rvs(1000) dist = NormalDistribution.fit(values) dist_uniform = UniformDistribution.fit(values) @@ -56,6 +59,7 @@ def test_normal(mean, sd): ] ) def test_log_normal(mean, sd): + """Test the log normal distribution.""" values = stats.lognorm(s=sd, loc=0, scale=np.exp(mean)).rvs(1000) dist = LogNormalDistribution.fit(values) dist_uniform = UniformDistribution.fit(values) @@ -75,6 +79,7 @@ def test_log_normal(mean, sd): ] ) def test_trunc_normal(lower, upper, mean, sd): + """Test the truncated normal distribution.""" a, b = (lower-mean)/sd, (upper-mean)/sd values = stats.truncnorm(a=a, b=b, loc=mean, scale=sd).rvs(5000) dist = TruncatedNormalDistribution.fit(values) @@ -88,6 +93,7 @@ def test_trunc_normal(lower, upper, mean, sd): [0.1, 10, 100, 234.1234] ) def test_exponential(rate): + """Test the exponential distribution.""" values = stats.expon(loc=0, scale=1/rate).rvs(5000) dist = ExponentialDistribution.fit(values) dist_uniform = UniformDistribution.fit(values) diff --git a/tests/test_dataset.py b/tests/test_dataset.py index 0551ce4b..12fe1182 100644 --- a/tests/test_dataset.py +++ b/tests/test_dataset.py @@ -1,3 +1,4 @@ +"""Testing module for fitting dataframes for both pandas and polars.""" from pathlib import Path from random import random @@ -6,7 +7,7 @@ import pytest from pytest import mark -from metasyn.demo.dataset import _get_demo_class, demo_dataframe, demo_file, _AVAILABLE_DATASETS +from metasyn.demo.dataset import _AVAILABLE_DATASETS, _get_demo_class, demo_dataframe, demo_file from metasyn.metaframe import MetaFrame from metasyn.provider import get_distribution_provider from metasyn.var import MetaVar @@ -32,12 +33,14 @@ def _read_csv(fp, dataframe_lib): df = pd.read_csv(fp, dtype=dtypes) return df.iloc[:100] else: - df = pl.read_csv(fp, schema_overrides={x: pl.Categorical for x, x_type in dtypes.items() if x_type == "category"}) + df = pl.read_csv(fp, schema_overrides={x: pl.Categorical for x, x_type in dtypes.items() + if x_type == "category"}) return df[:100] @mark.parametrize("dataframe_lib", ["polars", "pandas"]) def test_dataset(tmp_path, dataframe_lib): + """Integration test that loads the CSV, and creates a MetaFrame from that.""" titanic_fp = Path("tests", "data", "titanic.csv") tmp_fp = tmp_path / "tmp.json" df = _read_csv(titanic_fp, dataframe_lib) @@ -82,6 +85,7 @@ def check_dataset(dataset): dataset = MetaFrame.load_json(tmp_fp) check_dataset(dataset) + # Check the description functionality of metasyn. dataset.descriptions = {"Embarked": "Some description", "Sex": "Other description"} assert dataset.descriptions["Embarked"] == "Some description" with pytest.raises(AssertionError): @@ -106,6 +110,7 @@ def check_dataset(dataset): def test_distributions(tmp_path): + """Create all available distributions and save a metaframe with it.""" tmp_fp = tmp_path / "tmp.json" provider = get_distribution_provider() @@ -120,6 +125,7 @@ def test_distributions(tmp_path): "dataset_name", list(_AVAILABLE_DATASETS) ) def test_demo_datasets(tmp_path, dataset_name): + """Test all built-in demo datasets and see if they can be synthesized.""" demo_fp = demo_file(dataset_name) demo_df = demo_dataframe(dataset_name) demo_class = _get_demo_class(dataset_name) @@ -141,11 +147,13 @@ def test_demo_datasets(tmp_path, dataset_name): def test_demo_non_exist(): + """Check that trying to get a demo dataset that doesn't exist raises an error.""" with pytest.raises(ValueError): demo_file("non-existing-dataset") def test_create_dataset(tmpdir): + """Test re-creation of the test dataset.""" test_class = _get_demo_class("test") test_class.create(tmpdir/"test.csv") df = pl.read_csv(Path(tmpdir/"test.csv")) diff --git a/tests/test_datetime.py b/tests/test_datetime.py index 36d2fa78..8f672552 100644 --- a/tests/test_datetime.py +++ b/tests/test_datetime.py @@ -1,3 +1,4 @@ +"""Module for testing date/time distributions.""" import datetime as dt import numpy as np @@ -25,6 +26,7 @@ ) @mark.parametrize("series_type", [pl.Series, pd.Series]) def test_time(lower, upper, precision, series_type): + """Test for TimeUniformDistribution.""" lower_iso, upper_iso = dt.time.fromisoformat(lower), dt.time.fromisoformat(upper) dist = TimeUniformDistribution(lower, upper, precision) all_times = [] @@ -47,6 +49,7 @@ def test_time(lower, upper, precision, series_type): @mark.parametrize("series_type", [pl.Series, pd.Series]) def test_date(series_type): + """Test for DateUniformDistribution.""" lower_date, upper_date = "2020-07-09", "2023-08-19" lower_iso, upper_iso = dt.date.fromisoformat(lower_date), dt.date.fromisoformat(upper_date) @@ -76,6 +79,7 @@ def test_date(series_type): ) @mark.parametrize("series_type", [pl.Series, pd.Series]) def test_datetime(lower, upper, precision, series_type): + """Test for DateTimeUniformDistribution.""" if isinstance(lower, str): lower_iso, upper_iso = dt.datetime.fromisoformat(lower), dt.datetime.fromisoformat(upper) else: diff --git a/tests/test_discrete.py b/tests/test_discrete.py index 82dabe5b..0393eeda 100644 --- a/tests/test_discrete.py +++ b/tests/test_discrete.py @@ -1,3 +1,4 @@ +"""Module for testing discrete distributions.""" from math import fabs import numpy as np @@ -27,6 +28,7 @@ "series_type", [pd.Series, pl.Series] ) def test_uniform(data, series_type): + """Test discrete uniform distribution.""" series = series_type(data) dist = DiscreteUniformDistribution.fit(series) assert dist.lower == series.min() and dist.upper == series.max()+1 @@ -51,12 +53,14 @@ def test_uniform(data, series_type): ) @mark.parametrize("series_type", [pd.Series, pl.Series]) def test_integer_key(data, better_than_uniform, consecutive, series_type): + """Test unique key distribution (included increasing id's).""" series = series_type(data) dist = UniqueKeyDistribution.fit(series) unif_dist = DiscreteUniformDistribution.fit(series) assert dist.lower == series.min() assert dist.consecutive == consecutive - assert better_than_uniform == (dist.information_criterion(series) < unif_dist.information_criterion(series)) + assert better_than_uniform == (dist.information_criterion(series) + < unif_dist.information_criterion(series)) assert isinstance(dist, UniqueKeyDistribution) drawn_values = np.array([dist.draw() for _ in range(100)]) @@ -66,6 +70,7 @@ def test_integer_key(data, better_than_uniform, consecutive, series_type): @mark.parametrize("series_type", [pd.Series, pl.Series]) def test_poisson(series_type): + """Test Poisson distribution.""" series = series_type(stats.poisson(mu=10).rvs(1000)) dist = PoissonDistribution.fit(series) dist_unif = DiscreteUniformDistribution.fit(series) @@ -81,6 +86,7 @@ def test_poisson(series_type): ] ) def test_trunc_normal(lower, upper, mean, sd): + """Test discrete version of truncated normal distribution.""" a, b = (lower-mean)/sd, (upper-mean)/sd values = pl.Series(stats.truncnorm(a=a, b=b, loc=mean, scale=sd).rvs(5000)).cast(pl.Int64) dist = DiscreteTruncatedNormalDistribution.fit(values) @@ -98,6 +104,7 @@ def test_trunc_normal(lower, upper, mean, sd): ] ) def test_normal(mean, sd): + """Test for discrete version of normal/Gaussian distribution.""" values = pl.Series(stats.norm(loc=mean, scale=sd).rvs(1000)).cast(pl.Int64) dist = DiscreteNormalDistribution.fit(values) dist_uniform = DiscreteUniformDistribution.fit(values) diff --git a/tests/test_distribution.py b/tests/test_distribution.py deleted file mode 100644 index d3238833..00000000 --- a/tests/test_distribution.py +++ /dev/null @@ -1,36 +0,0 @@ -from pytest import mark, raises - -from metasyn.distribution.categorical import MultinoulliDistribution -from metasyn.distribution.continuous import NormalDistribution, UniformDistribution -from metasyn.distribution.discrete import DiscreteNormalDistribution, DiscreteUniformDistribution -from metasyn.distribution.string import ( - FakerDistribution, - RegexDistribution, - UniqueRegexDistribution, -) -from metasyn.provider import DistributionProviderList - - -@mark.parametrize( - "dist_str,var_type,is_unique,dist", - [ - ("multinoulli", "string", False, MultinoulliDistribution), - ("multinoulli", "discrete", False, MultinoulliDistribution), - ("uniform", "continuous", False, UniformDistribution), - ("uniform", "discrete", False, DiscreteUniformDistribution), - ("normal", "continuous", False, NormalDistribution), - ("normal", "discrete", False, DiscreteNormalDistribution), - ("faker", "string", False, FakerDistribution), - ("regex", "string", False, RegexDistribution), - ("regex", "string", True, UniqueRegexDistribution), - ] -) -def test_util(dist_str, var_type, is_unique, dist): - provider_list = DistributionProviderList("builtin") - dist_class = provider_list.find_distribution(dist_str, var_type=var_type, unique=is_unique) - assert dist == dist_class - if "faker" in dist_str: - with raises(ValueError): - provider_list.find_distribution("this is not a distribution", "string") - new_class = provider_list.find_distribution(dist_class.__name__, var_type=var_type, unique=is_unique) - assert new_class == dist_class diff --git a/tests/test_na.py b/tests/test_na.py deleted file mode 100644 index af36c36f..00000000 --- a/tests/test_na.py +++ /dev/null @@ -1,14 +0,0 @@ - -import polars as pl -from pytest import mark - -from metasyn import MetaFrame -from metasyn.distribution.na import NADistribution - - -@mark.parametrize("dtype", [pl.Float32, pl.Float64, pl.Int32, pl.Int64, pl.Categorical, pl.Utf8]) -def test_na(dtype): - df = pl.DataFrame({"data": pl.Series([None, None, None], dtype=dtype)}) - metadata = MetaFrame.fit_dataframe(df) - print(metadata.to_dict()) - assert isinstance(metadata["data"].distribution, NADistribution) diff --git a/tests/test_privacy.py b/tests/test_privacy.py index c83935f4..357a10b8 100644 --- a/tests/test_privacy.py +++ b/tests/test_privacy.py @@ -1,3 +1,4 @@ +"""Test whether the privacy functionality works.""" import pytest from metasyn.distribution import MultinoulliDistribution @@ -7,10 +8,11 @@ @metadist(privacy="test") class OtherMultinoulli(MultinoulliDistribution): - pass + """Test privacy version of multinoulli.""" def test_base_privacy(): + """Test whether the builtin version of privacy (no extra) works as expected.""" privacy = BasicPrivacy() priv_dict = privacy.to_dict() assert isinstance(priv_dict, dict) @@ -24,5 +26,6 @@ def test_base_privacy(): def test_import_error(): + """Test whether trying to use a non-existing privacy type raises an error.""" with pytest.raises(ImportError): get_privacy("nonexistentprivacyplugin") diff --git a/tests/test_provider.py b/tests/test_provider.py index 562d6700..211761f1 100644 --- a/tests/test_provider.py +++ b/tests/test_provider.py @@ -1,61 +1,114 @@ +"""Module to test the provider and provider list classes.""" + import pytest -from pytest import mark +from pytest import mark, raises from metasyn.distribution import MultinoulliDistribution, UniformDistribution from metasyn.distribution.base import BaseDistribution, metadist +from metasyn.distribution.continuous import NormalDistribution +from metasyn.distribution.discrete import DiscreteNormalDistribution, DiscreteUniformDistribution +from metasyn.distribution.string import ( + FakerDistribution, + RegexDistribution, + UniqueRegexDistribution, +) from metasyn.provider import BuiltinDistributionProvider, DistributionProviderList @mark.parametrize("input", ["builtin", "fake-name", BuiltinDistributionProvider, BuiltinDistributionProvider()]) def test_plist_init(input): + """Test whether the initialization works correctly.""" if input == "fake-name": with pytest.raises(ValueError): DistributionProviderList(input) else: plist = DistributionProviderList(input) - assert issubclass(plist.find_distribution("core.regex", var_type="string"), BaseDistribution) + assert issubclass(plist.find_distribution("core.regex", var_type="string"), + BaseDistribution) @metadist(version="1.0") class UniformTest1(UniformDistribution): - pass + """Version 1.0 for legacy tests.""" @metadist(version="1.1") class UniformTest11(UniformDistribution): - pass + """Version 1.1 for legacy tests.""" @metadist(version="2.0") class UniformTest2(UniformDistribution): - pass + """Version 2.0 for legacy tests.""" class CheckProvider(BuiltinDistributionProvider): + """Provider for legacy tests.""" + distributions = [UniformTest2] legacy_distributions = [UniformTest1, UniformTest11] class LegacyOnly(BuiltinDistributionProvider): + """Provider with deprecated uniform distributions.""" + distributions = [MultinoulliDistribution] legacy_distributions = [UniformTest1, UniformTest11, UniformTest2] def test_legacy(): + """Test whether the legacy system/distributions work.""" plist = DistributionProviderList(CheckProvider) assert issubclass(plist.find_distribution("core.uniform", var_type="continuous"), UniformTest2) - with pytest.warns(): - assert issubclass(plist.find_distribution("core.uniform", var_type="continuous", version="1.1"), UniformTest11) - with pytest.warns(): - assert issubclass(plist.find_distribution("core.uniform", var_type="continuous", version="1.0"), UniformTest1) - with pytest.warns(): - assert issubclass(plist.find_distribution("core.uniform", var_type="continuous", version="1.2"), UniformTest11) - with pytest.raises(ValueError): + with pytest.warns(): # version 1.1 is deprecated + assert issubclass( + plist.find_distribution( + "core.uniform", var_type="continuous", version="1.1"), UniformTest11) + with pytest.warns(): # version 1.0 is deprecated + assert issubclass( + plist.find_distribution("core.uniform", var_type="continuous", version="1.0"), + UniformTest1) + with pytest.warns(): # version 1.2 does not exist, so 1.1 should be chosen. + assert issubclass( + plist.find_distribution("core.uniform", var_type="continuous", version="1.2"), + UniformTest11) + with pytest.raises(ValueError): # No version 0.x available plist.find_distribution("core.uniform", var_type="continuous", version="0.9") - with pytest.warns(): - assert issubclass(plist.find_distribution("core.uniform", var_type="continuous", version="2.1"), UniformTest2) + with pytest.warns(): # No version 2.1 available, but 2.0 is. + assert issubclass( + plist.find_distribution("core.uniform", var_type="continuous", version="2.1"), + UniformTest2) plist = DistributionProviderList(LegacyOnly) - with pytest.warns(): - assert issubclass(plist.find_distribution("core.uniform", var_type="continuous"), UniformTest2) + with pytest.warns(): # core.uniform is deprecated. + assert issubclass( + plist.find_distribution("core.uniform", var_type="continuous"), + UniformTest2) + + +@mark.parametrize( + "dist_str,var_type,is_unique,dist", + [ + ("multinoulli", "string", False, MultinoulliDistribution), + ("multinoulli", "discrete", False, MultinoulliDistribution), + ("uniform", "continuous", False, UniformDistribution), + ("uniform", "discrete", False, DiscreteUniformDistribution), + ("normal", "continuous", False, NormalDistribution), + ("normal", "discrete", False, DiscreteNormalDistribution), + ("faker", "string", False, FakerDistribution), + ("regex", "string", False, RegexDistribution), + ("regex", "string", True, UniqueRegexDistribution), + ] +) +def test_find_distribution(dist_str, var_type, is_unique, dist): + """Test whether distributions can be found using the providers.""" + provider_list = DistributionProviderList("builtin") + dist_class = provider_list.find_distribution(dist_str, var_type=var_type, unique=is_unique) + assert dist == dist_class + if "faker" in dist_str: + with raises(ValueError): + provider_list.find_distribution("this is not a distribution", "string") + new_class = provider_list.find_distribution(dist_class.__name__, var_type=var_type, + unique=is_unique) + assert new_class == dist_class diff --git a/tests/test_faker.py b/tests/test_string.py similarity index 83% rename from tests/test_faker.py rename to tests/test_string.py index bf1c0fa3..2148effc 100644 --- a/tests/test_faker.py +++ b/tests/test_string.py @@ -1,3 +1,4 @@ +"""Test the string type distribution inference.""" import pandas as pd import polars as pl from pytest import mark @@ -7,6 +8,7 @@ @mark.parametrize("series_type", [pd.Series, pl.Series]) def test_faker(series_type): + """Test the faker distribution.""" var = FakerDistribution.fit(series_type([1, 2, 3])) assert isinstance(var.to_dict(), dict) assert isinstance(var.draw(), str) @@ -22,12 +24,13 @@ def test_faker(series_type): "sink", "dark", "crossbar", "toilet", "grow", "patient"]), "EN", None, 1), (pl.Series(["hotdog mother", "yes tip", "crate sink", "dark crossbar", "toilet grow", "patient is"]), "EN", None, 2), - (pl.Series(["hotdog mother.", "yes tip.", "crate sink.", "dark crossbar.", "toilet grow.", - "patient is."]), "EN", 1, 2), + (pl.Series(["hotdog mother.", "yes tip.", "crate sink.", "dark crossbar.", + "toilet grow.", "patient is."]), "EN", 1, 2), (pl.Series(["gaat naar school. Ik ben benieuwd.", "Wat is vraag? Wanneer komt hij."]), "NL", 2, 6) ]) def test_free_text(series, lang, avg_sentences, avg_words): + """Test whether the free text distribution does the right inference.""" dist = FreeTextDistribution.fit(series) assert dist.locale == lang assert dist.avg_sentences == avg_sentences diff --git a/tests/test_toml.py b/tests/test_toml.py index 3842e3b0..f66506b5 100644 --- a/tests/test_toml.py +++ b/tests/test_toml.py @@ -1,7 +1,9 @@ +"""Tests I/O for toml configuration and GMF files.""" from filecmp import cmp from pathlib import Path import pytest + try: import tomllib except ImportError: @@ -9,14 +11,13 @@ from pytest import mark -from metasyn.demo import demo_dataframe from metasyn.metaframe import MetaFrame from metasyn.provider import BuiltinDistributionProvider from metasyn.testutils import create_input_toml, create_md_report def test_datafree_create(tmpdir): - + """Test whether a synthetic dataset can be generated without data.""" temp_toml = tmpdir / "test.toml" create_input_toml(temp_toml) assert cmp(temp_toml, Path("examples", "config_files", "example_all.toml")) @@ -33,6 +34,7 @@ def test_datafree_create(tmpdir): ] ) def test_toml_save_load(tmpdir, toml_input, data): + """Test whether TOML GMF files can be saved/loaded.""" mf = MetaFrame.fit_dataframe(data, toml_input) mf.save(tmpdir/"test.toml") new_mf = MetaFrame.load(tmpdir/"test.toml") @@ -45,11 +47,13 @@ def test_toml_save_load(tmpdir, toml_input, data): ] ) def test_md_output(tmpdir, gmf_file): + """Test whether the markdown report can be generated from the GMF file.""" create_md_report(gmf_file, tmpdir/"test.md") assert Path(tmpdir/"test.md").is_file() assert isinstance(MetaFrame.load(gmf_file), MetaFrame) def test_toml_err(): + """Test whether errors are correctly handled.""" with pytest.raises(FileNotFoundError): MetaFrame.from_config("doesnotexist.toml") diff --git a/tests/test_var.py b/tests/test_var.py index 3ba56331..a7670c05 100644 --- a/tests/test_var.py +++ b/tests/test_var.py @@ -1,3 +1,7 @@ +"""Module to test the MetaVar object. + +Most of the test functions try to infer the correct distribution from a series. +""" import datetime as dt import json import string @@ -28,28 +32,33 @@ def _series_drop_nans(series): return series.dropna() -def _series_element_classname(series): - series = _series_drop_nans(series) +def _series_element_classname(series, all_nan): + if not all_nan: + series = _series_drop_nans(series) if isinstance(series, pl.Series): return series[0].__class__.__name__ return series.iloc[0].__class__.__name__ -def check_var(series, var_type, temp_path): +def check_var(series, var_type, temp_path, all_nan=False): + """Checker for all variables whether series are inferred correctly.""" def check_similar(series_a, series_b): assert isinstance(series_a, (pd.Series, pl.Series)) assert isinstance(series_b, (pd.Series, pl.Series)) assert len(series_a) == len(series_b) - base_type_a = _series_element_classname(series_a) - base_type_b = _series_element_classname(series_b) + base_type_a = _series_element_classname(series_a, all_nan) + base_type_b = _series_element_classname(series_b, all_nan) if type(series_a) == type(series_b): assert base_type_a == base_type_b - assert (len(series_a)-len(_series_drop_nans(series_a)) > 0) == (len(series_b) - len(_series_drop_nans(series_b)) > 0) + has_nans_a = len(series_a) - len(_series_drop_nans(series_a)) > 0 + has_nans_b = len(series_b) - len(_series_drop_nans(series_b)) > 0 + assert has_nans_a == has_nans_b assert isinstance(series, (pd.Series, pl.Series)) var = MetaVar.fit(series) new_series = var.draw_series(len(series)) + print(new_series) check_similar(series, new_series) assert var.var_type == var_type assert var_type in var.distribution.var_type @@ -60,7 +69,7 @@ def check_similar(series_a, series_b): with raises(ValueError): var_dict = var.to_dict() var_dict.update({"type": "unknown"}) - MetaVar.from_dict(var_dict) + print(MetaVar.from_dict(var_dict)) with raises(ValueError): var_dict = var.to_dict() @@ -99,11 +108,13 @@ def check_similar(series_a, series_b): ] ) def test_categorical(tmp_path, series): + """Test the inference of categorical variables.""" new_series = check_var(series, "categorical", tmp_path) assert set(_series_drop_nans(series)) == set(np.unique(_series_drop_nans(new_series))) @mark.parametrize("dtype", ["int8", "int16", "int32", "int64", "int"]) def test_integer(dtype, tmp_path): + """Test the inference of integer variables.""" series = pd.Series([np.random.randint(0, 10) for _ in range(300)], dtype=dtype) new_series = check_var(series, "discrete", tmp_path) assert new_series.min() >= 0 @@ -112,6 +123,7 @@ def test_integer(dtype, tmp_path): @mark.parametrize("dtype", ["Int8", "Int16", "Int32", "Int64"]) def test_nullable_integer(dtype, tmp_path): + """Test for pandas nullable integer data types (no applicable to polars).""" series = pd.Series([np.random.randint(0, 10) if np.random.rand() > 0.5 else None for _ in range(500)], dtype=dtype) new_series = check_var(series, "discrete", tmp_path) @@ -123,6 +135,7 @@ def test_nullable_integer(dtype, tmp_path): pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64] ) def test_polars_discrete(dtype, tmp_path): + """Test for all different variables of polars discrete types.""" series = pl.Series("ser", [np.random.randint(0, 10) for _ in range(100)], dtype=dtype) new_series = check_var(series, "discrete", tmp_path) assert new_series.min() >= 0 @@ -132,6 +145,7 @@ def test_polars_discrete(dtype, tmp_path): "dtype", [pl.Float32, pl.Float64] ) def test_polars_continuous(dtype, tmp_path): + """Test for all polars continuous data types.""" series = pl.Series("ser", np.random.rand(100), dtype=dtype) new_series = check_var(series, "continuous", tmp_path) assert new_series.min() > -0.5 @@ -142,6 +156,7 @@ def test_polars_continuous(dtype, tmp_path): "dtype", [pl.String, pl.Utf8] ) def test_polars_string(dtype, tmp_path): + """Test polars string types.""" series = pl.Series("series", string.printable, dtype=dtype) check_var(series, "string", tmp_path) @@ -149,6 +164,7 @@ def test_polars_string(dtype, tmp_path): "dtype", [pl.Categorical] ) def test_polars_categorical(dtype, tmp_path): + """Test whether polars categorical data types are parsed correctly.""" series = pl.Series("series", string.printable, dtype=dtype) check_var(series, "categorical", tmp_path) @@ -157,6 +173,7 @@ def test_polars_categorical(dtype, tmp_path): [pl.Series, pd.Series], ) def test_float(tmp_path, series_type): + """Test for continuous variables, pandas or polars.""" np.random.seed(3727442) series = series_type([np.random.rand() for _ in range(10000)]) new_series = check_var(series, "continuous", tmp_path) @@ -172,9 +189,26 @@ def test_float(tmp_path, series_type): [pl.Series, pd.Series], ) def test_string(tmp_path, series_type): + """Test polars/pandas string type fitting.""" series = series_type(np.random.choice(["a", "b", "c", None], size=100).tolist()) new_series = check_var(series, "string", tmp_path) - assert set(np.unique(_series_drop_nans(series))) == set(np.unique(_series_drop_nans(new_series))) + unq_values = set(np.unique(_series_drop_nans(series))) + new_unq_values = set(np.unique(_series_drop_nans(new_series))) + assert unq_values == new_unq_values + +@mark.parametrize("dtype,var_type", [ + (pl.Float32, "continuous"), + (pl.Float64, "continuous"), + (pl.Int32, "discrete"), + (pl.Int64, "discrete"), + (pl.Categorical, "categorical"), + (pl.Utf8, "string")]) +def test_na(tmp_path, dtype, var_type): + """Test the various NA type distributions.""" + series = pl.Series("x", [None, None, None], dtype=dtype) + print(series) + new_series = check_var(series, var_type, tmp_path, all_nan=True) + assert all(x is None for x in new_series) @mark.parametrize( @@ -182,6 +216,7 @@ def test_string(tmp_path, series_type): [pl.Series, pd.Series] ) def test_bool(tmp_path, series_type): + """Test whether booleans are correctly handled as categorical variables.""" series = series_type(np.random.choice([True, False], size=100)) check_var(series, "categorical", tmp_path) var = MetaVar.fit(series) @@ -194,7 +229,7 @@ def test_bool(tmp_path, series_type): [-1, -0.1, 1.2], ) def test_invalid_prop(prop_missing): - # with raises(ValueError): + """Ensure that errors are thrown for prop_missing outside of [0, 1].""" MetaVar("test", "discrete", DiscreteUniformDistribution.default_distribution()) with raises(ValueError): MetaVar("test", "discrete", DiscreteUniformDistribution.default_distribution(), @@ -218,6 +253,7 @@ def test_invalid_prop(prop_missing): ] ) def test_get_var_type(series, var_type): + """Test get_var_type method of MetaVar.""" assert MetaVar.get_var_type(series) == var_type @@ -227,6 +263,7 @@ def test_get_var_type(series, var_type): pl.Series([np.random.rand() for _ in range(5000)])] ) def test_manual_fit(series): + """Test adding dist_spec to MetaVar.fit call.""" var = MetaVar.fit(series) assert isinstance(var.distribution, (UniformDistribution, TruncatedNormalDistribution)) var = MetaVar.fit(series, dist_spec={"implements": "normal"}) @@ -235,8 +272,8 @@ def test_manual_fit(series): assert isinstance(var.distribution, UniformDistribution) var = MetaVar.fit(series, dist_spec=NormalDistribution(0, 1)) assert isinstance(var.distribution, NormalDistribution) - # with raises(TypeError): - # var.fit(10) + with raises(TypeError): + var.fit(10) @mark.parametrize( @@ -245,6 +282,7 @@ def test_manual_fit(series): pl.Series([None for _ in range(10)])] ) def test_na_zero(series): + """Test whether fitting a series with only NA works.""" var = MetaVar.fit(series) assert var.var_type == "continuous" assert var.prop_missing == 1.0 @@ -256,6 +294,7 @@ def test_na_zero(series): pl.Series([None if i != 0 else 1.0 for i in range(10)])] ) def test_na_one(series): + """Test whether fitting a series with exactly one non-NA values works.""" var = MetaVar.fit(series) assert var.var_type == "continuous" assert abs(var.prop_missing-0.9) < 1e7 @@ -267,6 +306,7 @@ def test_na_one(series): pl.Series([None if i < 2 else 0.123*i for i in range(10)])] ) def test_na_two(series): + """Test whether fitting a series with exactly two non-NA values works.""" var = MetaVar.fit(series) assert var.var_type == "continuous" assert abs(var.prop_missing-0.8) < 1e7 @@ -278,6 +318,7 @@ def test_na_two(series): pl.Series(np.random.randint(0, 1000, size=500))] ) def test_manual_unique_integer(series): + """Test whether discrete uniform/unique key are correctly inferred.""" var = MetaVar.fit(series) assert isinstance(var.distribution, DiscreteUniformDistribution) var = MetaVar.fit(series, dist_spec = {"unique": True}) @@ -292,7 +333,7 @@ def test_manual_unique_integer(series): ] ) def test_manual_unique_string(series): - # series = pd.Series(["x213", "2dh2", "4k2kk"]) + """Test which regexmodel is used dependent on the uniqueness.""" var = MetaVar.fit(series) assert isinstance(var.distribution, RegexDistribution) var = MetaVar.fit(series, dist_spec={"unique": True}) @@ -304,16 +345,17 @@ def test_manual_unique_string(series): [ pl.Series([2]*100 + [4]*100 + [12394]*100), pl.Series([-123]*12 + [123]*12), - # pl.Series(["2"]*100 + ["5"]*100 + ["123"]*100), pl.Series(["2"]*100 + ["5"]*100 + ["123"]*100, dtype=pl.Categorical) ] ) def test_int_multinoulli(series): + """Test whether the multinoulli distribution is correctly inferred.""" var = MetaVar.fit(series) assert isinstance(var.distribution, MultinoulliDistribution) def test_error_multinoulli(): + """Test whether incorrect probabilities give a warning/error.""" with raises(ValueError): MultinoulliDistribution(["1", "2"], [-0.1, 1.1]) with pytest.warns(): From 633afe559547cb9915373e6cafe838b45cef9ca3 Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Wed, 13 Nov 2024 16:30:42 +0100 Subject: [PATCH 13/17] Run core ci on push (#341) --- .github/workflows/core-ci.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/core-ci.yml b/.github/workflows/core-ci.yml index 686197e5..c596e25c 100644 --- a/.github/workflows/core-ci.yml +++ b/.github/workflows/core-ci.yml @@ -3,6 +3,8 @@ name: Continuous integration for development on: pull_request: branches: [ main, develop ] + push: + branches: [ main, develop ] jobs: check: From 1e53df61628362b3d14344d2133eac05cd825818 Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Fri, 15 Nov 2024 09:42:28 +0100 Subject: [PATCH 14/17] Simplified paper description and added relevant examples (#337) * Update paper with more examples - added hospital data example - added drug use data example * Small fix in hospital dataset description --- .gitignore | 3 + docs/paper/paper.bib | 17 ++-- docs/paper/paper.md | 141 ++++++++++++++--------------- docs/paper/paper.pdf | Bin 342257 -> 333038 bytes metasyn/demo/dataset.py | 158 ++++++++++++++++++++++++--------- metasyn/demo/demo_druguse.csv | 13 +++ metasyn/demo/demo_hospital.csv | 19 ++++ 7 files changed, 230 insertions(+), 121 deletions(-) create mode 100644 metasyn/demo/demo_druguse.csv create mode 100644 metasyn/demo/demo_hospital.csv diff --git a/.gitignore b/.gitignore index b03d5aea..fb8f0c2d 100644 --- a/.gitignore +++ b/.gitignore @@ -151,3 +151,6 @@ docs/paper/media # Generated api docs stuff docs/source/api/generated + +# uv stuff +uv.lock diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib index 7b49c275..304e0ab9 100644 --- a/docs/paper/paper.bib +++ b/docs/paper/paper.bib @@ -150,9 +150,16 @@ @inproceedings{ping2017datasynthesizer } @article{vankesteren2024democratize, - title={To democratize research with sensitive data, we should make synthetic data more accessible}, - author={{van Kesteren}, Erik-Jan}, - journal={arXiv preprint arXiv:2404.17271}, - year={2024}, - doi={10.48550/arXiv.2404.17271} + title = {To democratize research with sensitive data, we should make synthetic data more accessible}, + volume = {5}, + ISSN = {2666-3899}, + url = {http://dx.doi.org/10.1016/j.patter.2024.101049}, + DOI = {10.1016/j.patter.2024.101049}, + number = {9}, + journal = {Patterns}, + publisher = {Elsevier BV}, + author = {{van Kesteren}, Erik-Jan}, + year = {2024}, + month = sep, + pages = {101049} } \ No newline at end of file diff --git a/docs/paper/paper.md b/docs/paper/paper.md index 3c3804cf..ef7cde22 100644 --- a/docs/paper/paper.md +++ b/docs/paper/paper.md @@ -43,36 +43,33 @@ These choices enable the software to generate synthetic data with __privacy and # Software features -At its core, `metasyn` has three main functions: - -1. __Estimation__: Fit a generative model to a properly formatted tabular dataset, optionally with privacy guarantees. -2. __(De)serialization__: Create an intermediate representation of the fitted model for auditing, editing, and saving. -3. __Generation__: Synthesize new datasets based on a fitted model. +At its core, `metasyn` has three main functions: __estimation__, to fit a model to a properly formatted tabular dataset; __generation__, to synthesize new datasets based on a fitted model; and __(de)serialization__, to create a file from the model for auditing, editing, and saving. ## Estimation -The generative model in `metasyn` makes the assumption of marginal independence: each column is considered separately, similar to naïve Bayes classifiers [@hastie2009elements]. Some key advantages of this naïve approach are transparency and explainability, flexibility in handling mixed data types, and computational scalability to high-dimensional datasets. Formally, the generative model for $K$-variate data $\mathbf{x}$ is: +Model estimation starts with an appropriately pre-processed data frame, meaning it is tidy [@wickham2014tidy], each column has the correct data type, and missing data are represented by a missing value. Accordingly, `metasyn` is built on the `polars` data frame library [@vink2024polars]. As an example, the first records of the "hospital" data built into `metasyn` are printed below: -\begin{equation} \label{eq:model} -p(\mathbf{x}) = \prod_{k = 1}^K p(x_k) -\end{equation} +``` +┌────────────┬───────────────┬───────────────┬──────┬──────┬───────────────┐ +│ patient_id ┆ date_admitted ┆ time_admitted ┆ type ┆ age ┆ hours_in_room │ +│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ +│ str ┆ date ┆ time ┆ cat ┆ i64 ┆ f64 │ +╞════════════╪═══════════════╪═══════════════╪══════╪══════╪═══════════════╡ +│ A5909X0 ┆ 2024-01-01 ┆ 10:30:00 ┆ IVT ┆ null ┆ 3.633531 │ +│ B4025X2 ┆ 2024-01-01 ┆ 11:23:00 ┆ IVT ┆ 59 ┆ 6.932891 │ +│ B6999X2 ┆ 2024-01-01 ┆ 11:58:00 ┆ IVT ┆ 77 ┆ 1.970654 │ +│ B9525X2 ┆ 2024-01-01 ┆ 16:56:00 ┆ MYE ┆ null ┆ 1.620047 │ +│ … ┆ … ┆ … ┆ … ┆ … ┆ … │ +└────────────┴───────────────┴───────────────┴──────┴──────┴───────────────┘ +``` -Model estimation starts with an appropriately pre-processed data frame, meaning it is tidy [@wickham2014tidy], each column has the correct data type, and missing data are represented by a missing value. Internally, our software uses the `polars` data frame library [@vink2024polars], as it is performant, has consistent data types, and natively supports missing data (i.e., `null` values). An example source table is printed below (NB: categorical data are appropriately encoded as `cat`, not `str`): +Note that categorical data are encoded as `cat` (not `str`) and missing data is represented by `null` values. Model estimation with `metasyn` is then performed as follows: -``` -┌─────┬────────┬─────┬────────┬──────────┐ -│ ID ┆ fruits ┆ B ┆ cars ┆ optional │ -│ --- ┆ --- ┆ --- ┆ --- ┆ --- │ -│ i64 ┆ cat ┆ i64 ┆ cat ┆ i64 │ -╞═════╪════════╪═════╪════════╪══════════╡ -│ 1 ┆ banana ┆ 5 ┆ beetle ┆ 28 │ -│ 2 ┆ banana ┆ 4 ┆ audi ┆ 300 │ -│ 3 ┆ apple ┆ 3 ┆ beetle ┆ null │ -│ 4 ┆ apple ┆ 2 ┆ beetle ┆ 2 │ -│ 5 ┆ banana ┆ 1 ┆ beetle ┆ -30 │ -└─────┴────────┴─────┴────────┴──────────┘ +```python +from metasyn import MetaFrame +mf = MetaFrame.fit_dataframe(df_hospital) ``` -For each data type, a set of candidate distributions is fitted (see \autoref{tbl:dist}), and then `metasyn` selects the one with the lowest BIC [@neath2012bayesian]. For distributions where BIC computation is impossible (e.g., for the string data type) a pseudo-BIC is created that trades off fit and complexity of the underlying models. +The generative model in `metasyn` makes the simplifying assumption of _marginal independence_: each column is considered separately, similar to naïve Bayes classifiers [@hastie2009elements]. For each column, a set of candidate distributions is fitted (see \autoref{tbl:dist}), and then `metasyn` selects the one that fits best (usually having the lowest BIC [@neath2012bayesian]). Key advantages of this approach are transparency and explainability, flexibility in handling mixed data types, and computational scalability to high-dimensional datasets. Table: \label{tbl:dist} Candidate distributions associated with data types in the core `metasyn` package. @@ -84,77 +81,71 @@ Table: \label{tbl:dist} Candidate distributions associated with data types in th | String | Regex, Categorical, Faker, FreeText, Constant | | Date/time | Uniform, Constant | -From this table, the string distributions deserve special attention as they are not common probability distributions. The regex (regular expression) distribution uses the package [`regexmodel`](https://pypi.org/project/regexmodel/) to automatically detect structure such as room numbers (A108, C122, B109), e-mail addresses, or websites. The FreeText distribution detects the language (using [lingua](https://pypi.org/project/lingua-language-detector/)) and randomly picks words from that language. The [Faker](https://pypi.org/project/Faker/) distribution can generate specific data types such as localized names and addresses pre-specified by the user. +From this table, the string distributions deserve special attention as they are not common probability distributions. The regex (regular expression) distribution uses the package [`regexmodel`](https://pypi.org/project/regexmodel/) to automatically detect structure such as room numbers (A108, C122, B109), identifiers, e-mail addresses, or websites. The FreeText distribution detects the language (using [lingua](https://pypi.org/project/lingua-language-detector/)) and randomly picks words from that language. The [Faker](https://pypi.org/project/Faker/) distribution can generate specific data types such as localized names and addresses pre-specified by the user. -Generative model estimation with `metasyn` can be performed as follows: -```python -from metasyn import MetaFrame -mf = MetaFrame.fit_dataframe(df) -``` +## Data generation -## Serialization and deserialization -After fitting a model, `metasyn` can transparently store it in a human- and machine-readable `.json` metadata file. This file contains dataset-level descriptive information as well as the following variable-level information: - -```json -{ - "name": "fruits", - "type": "categorical", - "dtype": "Categorical(ordering='physical')", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "version": "1.0", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "unique": false, - "parameters": { - "labels": ["apple", "banana"], - "probs": [0.4, 0.6] - } - }, - "creation_method": { "created_by": "metasyn" } -} +After creating a `MetaFrame`, `metasyn` can randomly sample synthetic datapoints from it. This is done using the `synthesize()` method: + +```python +df_syn = mf.synthesize(3) ``` -This `.json` can be manually audited, edited, and after saving this file, an unlimited number of synthetic records can be created without incurring additional privacy risks. Serialization and deserialization with `metasyn` can be performed as follows: +This may result in the following data frame. Note that missing values in the `age` column are appropriately reproduced as well. -```python -mf.save("fruits.json") -mf_new = MetaFrame.load("fruits.json") +``` +┌────────────┬───────────────┬───────────────┬──────┬──────┬───────────────┐ +│ patient_id ┆ date_admitted ┆ time_admitted ┆ type ┆ age ┆ hours_in_room │ +│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │ +│ str ┆ date ┆ time ┆ cat ┆ i64 ┆ f64 │ +╞════════════╪═══════════════╪═══════════════╪══════╪══════╪═══════════════╡ +│ B7906X1 ┆ 2024-01-04 ┆ 13:32:00 ┆ IVT ┆ 37 ┆ 4.955418 │ +│ B0553X2 ┆ 2024-01-02 ┆ 10:54:00 ┆ IVT ┆ 39 ┆ 3.872872 │ +│ A5397X7 ┆ 2024-01-03 ┆ 18:16:00 ┆ CAT ┆ null ┆ 6.569082 │ +└────────────┴───────────────┴───────────────┴──────┴──────┴───────────────┘ ``` -## Data generation -For each variable in a `MetaFrame` object, `metasyn` can randomly sample synthetic datapoints. Data generation (or synthetization) in `metasyn` can be performed as follows: +## Serialization and deserialization +`MetaFrame`s can also be transparently stored in a human- and machine-readable `.json` metadata file. This file contains dataset-level descriptive information as well as variable-level information. This `.json` can be manually audited, edited, and after saving this file, an unlimited number of synthetic records can be created without incurring additional privacy risks. Serialization and deserialization with `metasyn` is done using the `save()` and `load()` methods: ```python -df_syn = mf.synthesize(3) +mf.save("hospital_admissions.json") +mf_new = MetaFrame.load("hospital_admissions.json") ``` -This may result in the following data frame. Note that missing values in the `optional` column are appropriately reproduced as well. +# Privacy +As a general principle, `metasyn` errs on the side of privacy by default, aiming to recreate the structure but not all content and relations in the source data. For example, take the following sensitive dataset where study participants state how they use drugs in daily life: ``` -┌─────┬────────┬─────┬────────┬──────────┐ -│ ID ┆ fruits ┆ B ┆ cars ┆ optional │ -│ --- ┆ --- ┆ --- ┆ --- ┆ --- │ -│ i64 ┆ cat ┆ i64 ┆ cat ┆ i64 │ -╞═════╪════════╪═════╪════════╪══════════╡ -│ 1 ┆ banana ┆ 4 ┆ beetle ┆ null │ -│ 2 ┆ banana ┆ 3 ┆ audi ┆ null │ -│ 3 ┆ banana ┆ 2 ┆ beetle ┆ 172 │ -└─────┴────────┴─────┴────────┴──────────┘ +┌────────────────┬─────────────────────────────────┐ +│ participant_id ┆ drug_use │ +│ --- ┆ --- │ +│ str ┆ str │ +╞════════════════╪═════════════════════════════════╡ +│ OOWJAHA4 ┆ I use marijuana in the evening… │ +│ 8CA1RV4P ┆ I occasionally take CBD to hel… │ +│ FMSVAKPM ┆ Prescription medication helps … │ +│ … ┆ … │ +└────────────────┴─────────────────────────────────┘ ``` -# Plug-ins and automatic privacy -The `metasyn` package also allows for plug-ins: packages that alter the distribution fitting behaviour. Through this system, privacy guarantees can be built into `metasyn` ([privacy plug-in template](https://github.com/sodascience/metasyn-privacy-template)) and additional distributions can be supported ([distribution plug-in template](https://github.com/sodascience/metasyn-distribution-template)). The [`metasyn-disclosure-control`](https://github.com/sodascience/metasyn-disclosure-control) plug-in implements output guidelines from Eurostat [@bond2015guidelines] by including micro-aggregation. In this way, information transfer from the sensitive real data to the synthetic public data can be further limited. Disclosure control is performed as follows: - -```python -from metasyn import MetaFrame -from metasyncontrib.disclosure import DisclosurePrivacy +When creating synthetic data for this example, the information in the open answers is removed, and using our standard `FreeText` distribution this information is replaced by words from the detected language (English): -mf = MetaFrame.fit_dataframe(df, privacy=DisclosurePrivacy()) ``` +┌────────────────┬─────────────────────────────────┐ +│ participant_id ┆ drug_use │ +│ --- ┆ --- │ +│ str ┆ str │ +╞════════════════╪═════════════════════════════════╡ +│ ZQJZQAB7 ┆ Lawyer let sort her yet line e… │ +│ 7KDLEL0S ┆ Particularly third myself edge… │ +│ QBZKGXC7 ┆ Put color against call researc… │ +└────────────────┴─────────────────────────────────┘ +``` + +Additionally, the `metasyn` package supports [plug-ins](https://github.com/sodascience/metasyn-privacy-template) which alter the estimation behaviour. Through this system, privacy guarantees can be built into `metasyn` and additional distributions can be supported. For example, [`metasyn-disclosure-control`](https://github.com/sodascience/metasyn-disclosure-control) implements output guidelines from Eurostat [@bond2015guidelines] through _micro-aggregation_. # Acknowledgements diff --git a/docs/paper/paper.pdf b/docs/paper/paper.pdf index d5c6ea79049ce485b336db97ef626af1cada28d0..864a26a31a994b09f8c40a815a09722c491e94ba 100644 GIT binary patch delta 72818 zcmV(^K-ItT@f7Z<6tEx*0XLT)<^mOe?OjWcB)N^g*RO~_kQ%)oK%s!Fm$AOhx`90y zKJ~)8hi)6*%l`eP9;7HmWMpLKc(4Wv(>0l)(1W5Viuyms z)S|o+M%hIxC0x?W_n%MycoIPdnU?l(8Xt2x$R$Nw@Gn=)9$ED#tpCIsc>VT&^vkC* zW>GeZsA5{a{q-V2-+nHCewI=@DUE?o4SxZ?yO+Ow`{R>bb?oEYUf+I&5;^l9HcEDy ztr6PC#knZFj*TZNC2LC-_O-lH&X)(5MDUpW%fJ29%lqG<#l1Fi;oLAzu8lzEoV}M1BlYSMKaZq9TXD(rFs2YU zS!Jw{K4D|f7im80fL1m@w|!`gv8r_qbG=s51k@?qqC{<+J!YbI-!i?SO%hJ}#ak^D zXuLehm#3B+^7ijg4ODA?QgqAzE;$qZlfpZhQr3qu!0S9?4e+uwy!{G@$P*l-WH|vZ zG%$Smx%4zIw3S{j_<6!Nn3)p3FHcH@lz67xU8Ai z%)cTY3jFu-^w;GI=YK5>Q&|@B+xi94wFn9(_HuF}+9-8qh6Y}Lc~G*H_JgHqVI$-( 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zzq;_cSF5LUNM?e}Qn8@#?z_qnbU(ecf8fqaGZ>;qkj!fE<@^5rqeXxvxmxim;Q{G6 zzI-++g<2NR&q1CqZQyCUxAs@y%2xI%0q+$6c3N`Yauj++{Dmz9MrVXizXxM)y+4c? z$sq8rcU{tQKb2zG#=5GqDg=-$0XQ8#nYT2JTDuDqb?=5A#9FN~_%h)0q#JjYwN1eH zpUGMl|5U2NPOyn9av(Ea}=2Brda45e#OZ98`|Bz+8ye`K+pOm ze=Xjw>FV;fI~UJB9 Create a csv with 12 rows and 2 columns: participant_id, and drug_use. The participant_id + has a standard alphanumeric structure, and the drug_use contains participant's responses on + how they use drugs in their daily life. + """ + + @property + def name(self): + return "druguse" + + @property + def schema(self): + return {} + + @register class TestDataset(BaseDataset): """Test dataset with all supported data types.""" @@ -146,8 +186,10 @@ def name(self): @property def schema(self): columns = pl.read_csv(self.file_location).columns - return {col_name: (getattr(pl, col_name[3:]) if col_name != "NA" else pl.String) - for col_name in columns} + return { + col_name: (getattr(pl, col_name[3:]) if col_name != "NA" else pl.String) + for col_name in columns + } @classmethod def create(cls, csv_file): @@ -155,47 +197,81 @@ def create(cls, csv_file): n_rows = 100 for int_val in [8, 16, 32, 64]: - all_series.append(pl.Series(f"pl.Int{int_val}", - [np.random.randint(-10, 10) for _ in range(n_rows)], - dtype=getattr(pl, f"Int{int_val}"))) - all_series.append(pl.Series(f"pl.UInt{int_val}", - [np.random.randint(10) for _ in range(n_rows)], - dtype=getattr(pl, f"UInt{int_val}"))) + all_series.append( + pl.Series( + f"pl.Int{int_val}", + [np.random.randint(-10, 10) for _ in range(n_rows)], + dtype=getattr(pl, f"Int{int_val}"), + ) + ) + all_series.append( + pl.Series( + f"pl.UInt{int_val}", + [np.random.randint(10) for _ in range(n_rows)], + dtype=getattr(pl, f"UInt{int_val}"), + ) + ) for float_val in [32, 64]: - all_series.append(pl.Series(f"pl.Float{float_val}", - np.random.randn(n_rows), - dtype=getattr(pl, f"Float{float_val}"))) - - all_series.append(pl.Series("pl.Date", [date(2024, 9, 4) + timedelta(days=i) - for i in range(n_rows)], - dtype=pl.Date)) - all_series.append(pl.Series("pl.Datetime", - [datetime(2024, 9, 4, 12, 30, 12) - + timedelta(hours=i, minutes=i*2, seconds=i*3) - for i in range(n_rows)], - dtype=pl.Datetime)) - all_series.append(pl.Series("pl.Time", - [time(3+i//20, 6+i//12, 12+i//35) for i in range(n_rows)], - dtype=pl.Time)) - all_series.append(pl.Series("pl.String", - np.random.choice(list(string.printable), size=n_rows), - dtype=pl.String)) - all_series.append(pl.Series("pl.Utf8", - np.random.choice(list(string.printable), size=n_rows), - dtype=pl.Utf8)) - all_series.append(pl.Series("pl.Categorical", - np.random.choice(list(string.ascii_uppercase[:5]), size=n_rows), - dtype=pl.Categorical)) - all_series.append(pl.Series("pl.Boolean", - np.random.choice([True, False], size=n_rows), - dtype=pl.Boolean)) + all_series.append( + pl.Series( + f"pl.Float{float_val}", + np.random.randn(n_rows), + dtype=getattr(pl, f"Float{float_val}"), + ) + ) + + all_series.append( + pl.Series( + "pl.Date", + [date(2024, 9, 4) + timedelta(days=i) for i in range(n_rows)], + dtype=pl.Date, + ) + ) + all_series.append( + pl.Series( + "pl.Datetime", + [ + datetime(2024, 9, 4, 12, 30, 12) + + timedelta(hours=i, minutes=i * 2, seconds=i * 3) + for i in range(n_rows) + ], + dtype=pl.Datetime, + ) + ) + all_series.append( + pl.Series( + "pl.Time", + [time(3 + i // 20, 6 + i // 12, 12 + i // 35) for i in range(n_rows)], + dtype=pl.Time, + ) + ) + all_series.append( + pl.Series( + "pl.String", np.random.choice(list(string.printable), size=n_rows), dtype=pl.String + ) + ) + all_series.append( + pl.Series( + "pl.Utf8", np.random.choice(list(string.printable), size=n_rows), dtype=pl.Utf8 + ) + ) + all_series.append( + pl.Series( + "pl.Categorical", + np.random.choice(list(string.ascii_uppercase[:5]), size=n_rows), + dtype=pl.Categorical, + ) + ) + all_series.append( + pl.Series("pl.Boolean", np.random.choice([True, False], size=n_rows), dtype=pl.Boolean) + ) all_series.append(pl.Series("NA", [None for _ in range(n_rows)], dtype=pl.String)) # Add NA's for all series except the categorical for series in all_series: if series.name != "pl.Categorical": - none_idx = np.random.choice(np.arange(n_rows), size=n_rows//10, replace=False) + none_idx = np.random.choice(np.arange(n_rows), size=n_rows // 10, replace=False) none_idx.sort() series[none_idx] = None diff --git a/metasyn/demo/demo_druguse.csv b/metasyn/demo/demo_druguse.csv new file mode 100644 index 00000000..1eaea06b --- /dev/null +++ b/metasyn/demo/demo_druguse.csv @@ -0,0 +1,13 @@ +participant_id,daily_drug_use +OOWJAHA4,I use marijuana in the evenings to relax and manage my anxiety. +8CA1RV4P,I occasionally take CBD to help with sleep and muscle recovery after workouts. +FMSVAKPM,Prescription medication helps me manage chronic pain; I take it as directed daily. +JLTGIXS2,I use caffeine throughout the day to keep my energy up at work. +U5CI8Y5F,I smoke cigarettes during breaks; it’s part of my daily routine. +8YMDTW83,I use Adderall as prescribed to help with focus for my studies. +C4UVCR0B,"I drink alcohol socially, but sometimes have a glass of wine at dinner to unwind." +A0Q515ZQ,I take anti-anxiety medication every morning as part of my mental health regimen. +OY50H1JG,"Occasionally, I use edibles on weekends to help me relax after a stressful week." +ADU9F363,I use nicotine patches to help reduce cigarette cravings. +P66WD839,"I use melatonin at night to help with sleep, especially on stressful days." +68WPHNB4,"I take a prescription stimulant daily for ADHD, which helps me stay productive." diff --git a/metasyn/demo/demo_hospital.csv b/metasyn/demo/demo_hospital.csv new file mode 100644 index 00000000..182492ff --- /dev/null +++ b/metasyn/demo/demo_hospital.csv @@ -0,0 +1,19 @@ +patient_id,date_admitted,time_admitted,type,age,hours_in_room +A5909X0,2024-01-01,10:30:00.000000000,IVT,,3.6335314585568366 +B4025X2,2024-01-01,11:23:00.000000000,IVT,59,6.93289094489419 +B6999X2,2024-01-01,11:58:00.000000000,IVT,77,1.970654097305811 +B9525X2,2024-01-01,16:56:00.000000000,MYE,,1.6200472977499425 +B0453X1,2024-01-02,11:50:00.000000000,IVT,,3.344935472600846 +A6441X2,2024-01-02,16:11:00.000000000,IVT,,5.28264145694029 +A9260X4,2024-01-02,17:08:00.000000000,IVT,68,2.2405604565294372 +B7526X0,2024-01-02,17:12:00.000000000,IVT,,7.268823575595286 +B4675X8,2024-01-03,11:10:00.000000000,MYE,49,2.8933899306166166 +A3206X1,2024-01-04,10:12:00.000000000,CAT,40,5.766570385883389 +A4363X0,2024-01-04,10:59:00.000000000,IVT,77,6.931131403828807 +A9418X9,2024-01-04,13:12:00.000000000,IVT,82,3.1923453535590203 +B4309X2,2024-01-04,13:51:00.000000000,IVT,30,1.828827719602017 +B3830X7,2024-01-04,16:03:00.000000000,CAT,87,3.8709200461975373 +A7952X3,2024-01-04,18:00:00.000000000,IVT,60,4.332882463068666 +A0013X2,2024-01-05,10:06:00.000000000,CAT,,2.1333391075365316 +B2077X8,2024-01-05,10:43:00.000000000,IVT,,6.026762991290889 +A4700X7,2024-01-05,14:53:00.000000000,MYE,84,6.193777477178557 From 04d390189b9bac4916b8dd1c39f253c6ce35fd16 Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Fri, 15 Nov 2024 20:27:50 +0100 Subject: [PATCH 15/17] Documentation update 1.1 (#340) * General readthedocs documentation rewrite to make things easier to find. * Fix bug in assigning privacy --- docs/source/{about/license.rst => about.rst} | 48 ++- docs/source/about/about.rst | 11 - docs/source/about/contact.rst | 40 -- docs/source/api/developer_reference.rst | 2 +- docs/source/api/metasyn.rst | 2 +- docs/source/{usage => }/cli.rst | 38 +- docs/source/datafree.rst | 58 +++ docs/source/developer/GMF.rst | 30 -- docs/source/developer/contributing.rst | 2 +- docs/source/developer/developer.rst | 7 +- docs/source/developer/distributions.rst | 8 - docs/source/developer/overview.rst | 12 +- docs/source/developer/plugins.rst | 2 +- docs/source/{usage => }/extensions.rst | 3 +- docs/source/faq.rst | 8 +- docs/source/improve_synth.rst | 315 ++++++++++++++++ docs/source/index.rst | 106 ++---- docs/source/installation.rst | 36 ++ docs/source/metasyn_in_detail.rst | 296 --------------- docs/source/{usage => }/quick_start.rst | 96 ++--- ...nteractive_tutorials.rst => tutorials.rst} | 7 +- docs/source/usage/config_files.rst | 145 -------- docs/source/usage/generating_metaframes.rst | 152 -------- .../usage/generating_synthetic_data.rst | 39 -- docs/source/usage/installation.rst | 78 ---- docs/source/usage/saving_metaframes.rst | 343 ------------------ docs/source/usage/usage.rst | 19 - docs/source/what_is_metasyn.rst | 276 ++++++++++++++ metasyn/config.py | 2 +- metasyn/distribution/categorical.py | 2 +- metasyn/metaframe.py | 2 +- 31 files changed, 846 insertions(+), 1339 deletions(-) rename docs/source/{about/license.rst => about.rst} (51%) delete mode 100644 docs/source/about/about.rst delete mode 100644 docs/source/about/contact.rst rename docs/source/{usage => }/cli.rst (78%) create mode 100644 docs/source/datafree.rst delete mode 100644 docs/source/developer/GMF.rst rename docs/source/{usage => }/extensions.rst (99%) create mode 100644 docs/source/improve_synth.rst create mode 100644 docs/source/installation.rst delete mode 100644 docs/source/metasyn_in_detail.rst rename docs/source/{usage => }/quick_start.rst (60%) rename docs/source/{usage/interactive_tutorials.rst => tutorials.rst} (55%) delete mode 100644 docs/source/usage/config_files.rst delete mode 100644 docs/source/usage/generating_metaframes.rst delete mode 100644 docs/source/usage/generating_synthetic_data.rst delete mode 100644 docs/source/usage/installation.rst delete mode 100644 docs/source/usage/saving_metaframes.rst delete mode 100644 docs/source/usage/usage.rst create mode 100644 docs/source/what_is_metasyn.rst diff --git a/docs/source/about/license.rst b/docs/source/about.rst similarity index 51% rename from docs/source/about/license.rst rename to docs/source/about.rst index 0691d8da..20e07fdc 100644 --- a/docs/source/about/license.rst +++ b/docs/source/about.rst @@ -1,5 +1,51 @@ +About +===== + +.. _contact us: + +Contact us +---------- + +**Metasyn** is a project by the `ODISSEI Social Data Science (SoDa) `_ team and is currently being maintained by Erik-Jan van Kesteren, Raoul Schram and Samuel Spithorst. + +.. image:: /images/logos/soda.png + :alt: SODA_logo + :width: 200 + :align: center + :target: https://odissei-data.nl/nl/soda/ + +Do you have questions, suggestions, or remarks on the technical implementation? We welcome your feedback and contributions! + +GitHub repository and issue tracker +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Feel free to check out our `GitHub repository `_ for the latest updates and to browse the source code. +If you encounter any problems or have ideas for improvements, please file an issue on our `GitHub issue tracker `_. + +.. image:: https://img.shields.io/badge/GitHub-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn + :alt: GitHub Repository Button + :target: https://github.com/sodascience/metasyn + +.. image:: https://img.shields.io/badge/GitHub-Issue_Tracker-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn%2Fissues + :alt: GitHub Issue Tracker Button + :target: https://github.com/sodascience/metasyn/issues + +Contributing +^^^^^^^^^^^^ + +We highly encourage and appreciate pull requests (PRs) from the community. If you want to discover how you can contribute to metasyn, please refer to our :doc:`/developer/developer` + +Maintainers +^^^^^^^^^^^ + +Feel free to contact one of the maintainers directly: + +* Erik-Jan van Kesteren: `https://github.com/vankesteren `_ + +* Raoul Schram: `https://github.com/qubixes `_ + License -======== +------- Metasyn is released under the MIT License. diff --git a/docs/source/about/about.rst b/docs/source/about/about.rst deleted file mode 100644 index 21f07d98..00000000 --- a/docs/source/about/about.rst +++ /dev/null @@ -1,11 +0,0 @@ -About -===== - -Welcome to the ``metasyn`` About section. This section is meant to provide more information on the background and context of metasyn. - -.. toctree:: - :maxdepth: 1 - :caption: Sections: - - contact - license \ No newline at end of file diff --git a/docs/source/about/contact.rst b/docs/source/about/contact.rst deleted file mode 100644 index 3bc36b19..00000000 --- a/docs/source/about/contact.rst +++ /dev/null @@ -1,40 +0,0 @@ -Contact Us -========== - -**Metasyn** is a project by the `ODISSEI Social Data Science (SoDa) `_ team and is currently being maintained by Erik-Jan van Kesteren, Raoul Schram and Samuel Spithorst. - -.. image:: /images/logos/soda.png - :alt: SODA_logo - :width: 200 - :align: center - :target: https://odissei-data.nl/nl/soda/ - -Do you have questions, suggestions, or remarks on the technical implementation? We welcome your feedback and contributions! - -GitHub Repository and Issue Tracker ------------------------------------ -Feel free to check out our `GitHub repository `_ for the latest updates and to browse the source code. -If you encounter any problems or have ideas for improvements, please file an issue on our `GitHub issue tracker `_. - -.. image:: https://img.shields.io/badge/GitHub-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn - :alt: GitHub Repository Button - :target: https://github.com/sodascience/metasyn - -.. image:: https://img.shields.io/badge/GitHub-Issue_Tracker-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn%2Fissues - :alt: GitHub Issue Tracker Button - :target: https://github.com/sodascience/metasyn/issues - -Contributing ------------- -We highly encourage and appreciate pull requests (PRs) from the community. If you want to discover how you can contribute to metasyn, please refer to our :doc:`/developer/developer` - -Maintainers ------------ -Feel free to contact one of the maintainers directly: - -* Erik-Jan van Kesteren: `https://github.com/vankesteren `_ - -* Raoul Schram: `https://github.com/qubixes `_ - -* Samuel Spithorst: `https://github.com/samuwhale `_ - diff --git a/docs/source/api/developer_reference.rst b/docs/source/api/developer_reference.rst index 5099b6e0..4a4af172 100644 --- a/docs/source/api/developer_reference.rst +++ b/docs/source/api/developer_reference.rst @@ -1,4 +1,4 @@ -Developer Reference +Developer reference =================== This section is intended for those that want to contribute to the metasyn package, or simply want a deeper understanding of how it works. It contains the classes, functions and modules that are not in the rest of the reference API. These are mostly elements that are not directly used by users, but are important for developers of the metasyn package to understand the architecture. diff --git a/docs/source/api/metasyn.rst b/docs/source/api/metasyn.rst index 67ec0d26..62b96591 100644 --- a/docs/source/api/metasyn.rst +++ b/docs/source/api/metasyn.rst @@ -1,4 +1,4 @@ -API Reference +API reference ============= This section aims to give an overview of all classes, functions and methods in the metasyn package. diff --git a/docs/source/usage/cli.rst b/docs/source/cli.rst similarity index 78% rename from docs/source/usage/cli.rst rename to docs/source/cli.rst index 0765ab6e..01f169f3 100644 --- a/docs/source/usage/cli.rst +++ b/docs/source/cli.rst @@ -1,4 +1,4 @@ -Command-line Interface +Command-Line Interface ====================== Metasyn provides a command-line interface (CLI) for accessing core functionality without needing to write any Python code. @@ -28,7 +28,7 @@ Accessing the CLI ----------------- If you have installed the main ``metasyn`` package, the CLI should be available automatically. You can find instructions on how to install ``metasyn`` in the :doc:`installation` section of the documentation. -Alternatively, the CLI can be accessed through a Docker container, allowing you to run ``metasyn`` in an isolated environment without installing the package on your system. This can be useful, for example, when trying out ``metasyn`` without affecting your existing Python environment. +Alternatively, the CLI can be accessed through a Docker container, allowing you to run ``metasyn`` in an isolated environment without installing the package on your system. Here's how you can use Docker to access Metasyn's CLI: @@ -81,9 +81,9 @@ The ``metasyn`` CLI should now be up and running within the Docker container and docker run -v $(pwd):/wd sodateam/metasyn:v1.0.3 --help -Creating Generative Metadata +Creating generative metadata ---------------------------- -The ``create-meta`` subcommand combines the :doc:`estimation     ` and :doc:`serialization ` steps in the pipeline into one, this allows you to generate generative metadata for a tabular dataset (in CSV format), and store it in a GMF (Generative Metadata Format) file. +The ``create-meta`` subcommand combines the estimation and serialization steps in the pipeline into one, this allows you to generate generative metadata for a tabular dataset (in CSV format), and store it in a GMF (Generative Metadata Format) file. .. image:: /images/pipeline_cli_create_meta.png :alt: Creating Generative Metadata using the CLI @@ -130,35 +130,16 @@ An example of how to use the ``create-meta`` subcommand is as follows: The ``create-meta`` command also takes one optional argument: -* ``--config [config-file]``: The filepath and name of a .toml configuration file that specifies distribution behavior. For example, if we want to set a column to be unique or to have a specific distribution, we can do so by specifying it in the configuration file. Information on how to use these files can be found in the :doc:`/usage/config_files` section. +* ``--config [config-file]``: The filepath and name of a .toml configuration file that specifies distribution behavior. For example, if we want to set a column to be unique or to have a specific distribution, we can do so by specifying it in the configuration file. Information on how to use these files can be found in the :doc:`improve_synth` section. .. admonition:: Generating a GMF file without a dataset - It is also possible to create a GMF file (and to generate synthetic data from there) without every inputting a dataset. Adding columns not present in the input dataset is also possible using the same method. + See our :doc:`section` for how to create a configuration file without using a dataset. In this case, you will not need to supply any ``[input]`` to the ``create-meta`` command. - This can be done by supplying a configuration file that fully specifies the columns that should be generated. For each to be generated column, you need also need to set the `data_free` parameter to `true`. - It is also required to set the number of rows under the `general` section. - - For example, to create a GMF file that can be used to generate 100 rows of synthetic data with a single column `PassengerId`, that is unique and has a discrete distribution, you can use the following configuration file: - - .. code-block:: toml - - n_rows = 100 - - [[var]] - - name = "PassengerId" - data_free = true - prop_missing = 0.0 - description = "ID of the unfortunate passenger." - var_type = "discrete" - distribution = {implements = "core.unique_key", unique = true, parameters = {consecutive = true, low = 0}} - - -Generating Synthetic Data +Generating synthetic data ------------------------- -The ``synthesize`` subcommand combines the :doc:`deserialization ` and :doc:`generation ` steps in the pipeline into one, and allows you to generate a synthetic dataset from a previously saved MetaFrame (stored as GMF file). +The ``synthesize`` subcommand combines the deserialization and generation steps in the pipeline into one, and allows you to generate a synthetic dataset from a previously saved MetaFrame (stored as GMF file). .. image:: /images/pipeline_cli_synthesize.png :alt: Creating Synthetic Data from a GMF file using the CLI @@ -207,6 +188,7 @@ An example of how to use the ``synthesize`` subcommand is as follows: The ``synthesize`` command also takes two optional arguments: + - ``-n [rows]`` or ``--num_rows [rows]``: To generate a specific number of data rows. - ``-p`` or ``--preview``: To preview the first six rows of synthesized data. This can be extremely useful for quick data validation without saving it to a file. @@ -217,7 +199,7 @@ The ``synthesize`` command also takes two optional arguments: -Creating Validation schemas +Creating validation schemas --------------------------- The ``schema`` subcommand generates a schema that describes the expected format of the GMF files. These can be used to validate GMF files before importing and loading them into a :obj:`MetaFrame`. diff --git a/docs/source/datafree.rst b/docs/source/datafree.rst new file mode 100644 index 00000000..df973592 --- /dev/null +++ b/docs/source/datafree.rst @@ -0,0 +1,58 @@ +Synthetic data without raw data +=============================== + +It is also possible to create a GMF file without any input dataset, or to add additional fictive columns to those already present in a dataset. + +To do so, you need to fully specify each column (variable) you want to generate. You will also need to set the ``data_free`` parameter to true, +to indicate that the variable will be generated from scratch, instead of being based on existing data. +Finally, you will need to set the number of rows to generate. + +For example, the following configuration file will generate a GMF file with 100 rows of synthetic data, with a unique key column named ``PassengerId``: + +.. code-block:: toml + + n_rows = 100 + + [[var]] + + name = "PassengerId" + data_free = true + prop_missing = 0.0 + description = "ID of the unfortunate passenger." + var_type = "discrete" + distribution = {implements = "core.unique_key", unique = true, parameters = {consecutive = true, low = 0}} + +See :doc:`distribution page ` for a list of distributions that can be chosen from. + +Setting defaults +---------------- + +Writing the same things for every distribution can be tedious work, but you can also create defaults for +variables. The following can be set by default: ``data_free``, ``prop_missing``, ``distribution`` and ``privacy``. +Since the distribution depends on the type of the column, you can set the default distribution per column type. +Below is an example on how to set defaults: + +.. code-block:: toml + + [defaults] + + data_free = true + prop_missing = 0.1 + + [defaults.distribution] + discrete = {implements = "core.uniform", parameters = {lower = 1, upper = 30}} + continuous = {implements = "core.normal", parameters = {mean = 0, stdev = 1}} + string = {implements = "core.faker", parameters = {faker_type = "name", locale = "en_US"}} + +With this block, you won't have to set the ``data_free`` parameter, and the default +proportion of missing values is set to 0.1. For discrete columns, the distribution +will be set to a uniform distribution between 1 and 30, etc. + +With the defaults set as above, you only need to specify the ``name`` and ``var_type`` of +the columns: + +.. code-block:: toml + + [[var]] + name = "some discrete variable" + var_type = "discrete" diff --git a/docs/source/developer/GMF.rst b/docs/source/developer/GMF.rst deleted file mode 100644 index 75a7ed0c..00000000 --- a/docs/source/developer/GMF.rst +++ /dev/null @@ -1,30 +0,0 @@ -Generative Metadata Format (GMF) -================================ - -At the core of ``metasyn`` lies its ability to :doc:`generate `, -:doc:`save` and :ref:`load` statistical metadata -for a given dataset, which can then be used to :doc:`generate synthetic datasets `. To achieve this, ``metasyn`` uses the Generative Metadata Format (GMF), an open source format (available on `GitHub `_) designed to store statistical metadata for tabular datasets. The GMF standard is designed to be modular and extensible, with more distributions and privacy-enhancing mechanisms. -Due to its open nature, GMF can be used by other software too. - - - -On a base level, the generative metadata generated by ``metasyn`` contains the following information: - -* ``n_rows``: Number of rows -* ``n_columns``: Number of columns -* ``provenance``: Details on how the file was created. -* ``vars``: Description of all variables (columns) of the dataset. - -Each variable in ``vars`` contains at least the following attributes: - -* ``name``: Name of the variable (column name). -* ``type``: Type of variable -* ``dtype``: This is the polars dtype that the variable has to be converted back to. -* ``prop_missing``: The proportion of values that are missing (NA). -* ``distribution``: The distribution that describes the variable. This distribution has at least two attributes: - - * ``name``: Name of the distribution. - * ``parameters``: Dictionary containing the parameters of the distribution. - -The full metadata schema used in ``metasyn`` can be found on the `metasyn GitHub repository `_. - diff --git a/docs/source/developer/contributing.rst b/docs/source/developer/contributing.rst index cb3bbbc0..80ad0bfa 100644 --- a/docs/source/developer/contributing.rst +++ b/docs/source/developer/contributing.rst @@ -5,7 +5,7 @@ Thank you for your interest in contributing to metasyn! We greatly appreciate an Feedback, suggestions and issues: --------------------------------- -If you encounter a bug or have a feature request, you can report it in the `issue tracker `_. Detailed bug reports and well-defined feature requests are highly appreciated. Additionally, you can help us by leaving suggestions or feedback on how to enhance ``metasyn`` on the project's `GitHub repository `_. More information on getting in touch with us can be found on our :doc:`contact page `. +If you encounter a bug or have a feature request, you can report it in the `issue tracker `_. Detailed bug reports and well-defined feature requests are highly appreciated. Additionally, you can help us by leaving suggestions or feedback on how to enhance ``metasyn`` on the project's `GitHub repository `_. More information on getting in touch with us can be found on our :ref:`contact page `. .. image:: https://img.shields.io/badge/GitHub-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn :alt: GitHub Repository Button diff --git a/docs/source/developer/developer.rst b/docs/source/developer/developer.rst index a24028e6..17bbbdc9 100644 --- a/docs/source/developer/developer.rst +++ b/docs/source/developer/developer.rst @@ -1,20 +1,15 @@ -Developer Guide +Developer guide ================= This guide is mainly directed at developers working on the ``metasyn`` package, but it may be useful for users that want more control over the workings of the ``metasyn`` package. -.. warning:: - This section is intended for developers and advanced users. If you are new to ``metasyn``, please refer to the :doc:`/usage/usage` first. - - .. toctree:: :maxdepth: 1 :caption: Sections: contributing overview - GMF distributions plugins diff --git a/docs/source/developer/distributions.rst b/docs/source/developer/distributions.rst index 7d0dfd09..9f514452 100644 --- a/docs/source/developer/distributions.rst +++ b/docs/source/developer/distributions.rst @@ -62,10 +62,6 @@ If the distribution has subsequently draws that are not independent, it is recom It is recommended to also implement :meth:`~metasyn.distribution.base.BaseDistribution.information_criterion`. This is a class method used to determine which distribution gets selected during the fitting process for a series of values. The distribution with the lowest information criterion of the correct variable type will be selected. For discrete and continuous distributions it is currently implemented as `BIC `_). -.. warning:: - - Despite not being an abstract method in :class:`~metasyn.distribution.base.BaseDistribution`, it is recommended to implement a constructor (``__init__``) method in derived classes to initialize a distribution with a set of (distribution specific) parameters. - There are more methods, but this is a good starting point when implementing a new distribution. For an overview of the rest of the methods and implementation details, refer to the :class:`~metasyn.distribution.base.BaseDistribution` class. @@ -121,10 +117,6 @@ For example, the unique variants of the :class:`~metasyn.distribution.regex.Rege @metadist(implements="core.faker", var_type="string") class UniqueFakerDistribution(UniqueDistributionMixin, FakerDistribution): -.. warning:: - - This mixin class has a default implementation that will work for many distributions, but it may not be appropriate for all. Be sure to check the implementation before using it. - Other modules ~~~~~~~~~~~~~ diff --git a/docs/source/developer/overview.rst b/docs/source/developer/overview.rst index fc28827e..43eae836 100644 --- a/docs/source/developer/overview.rst +++ b/docs/source/developer/overview.rst @@ -1,16 +1,12 @@ -Overview -======== +Metasyn package overview +======================== This page aims to introduce the main classes and modules of the ``metasyn`` package. It is intended to be a high-level overview of the package's structure and functionality. For a thorough overview, please refer to the :doc:`API reference `. Clicking on a class or module name will automatically take you to its API reference section. -.. warning:: - This page is intended for developers and advanced users. If you are new to ``metasyn``, please refer to the :doc:`/usage/usage` first. - - MetaFrame class --------------- -The :class:`~metasyn.MetaFrame` class is a core component of the ``metasyn`` package. It represents a metadata frame, which is a structure that holds statistical metadata about a dataset. The data contained in a :obj:`~metasyn.MetaFrame` is in line with the :doc:`/developer/GMF`. +The :class:`metasyn.MetaFrame` class is a core component of the ``metasyn`` package. It represents a metadata frame, which is a structure that holds statistical metadata about a dataset. The data contained in a :obj:`~metasyn.MetaFrame` is in line with the :ref:`GMF format `. Essentially, a :obj:`~metasyn.MetaFrame` is a collection of :obj:`~metasyn.MetaVar` objects, each representing a column in a dataset. It contains methods that allow for the following: @@ -38,7 +34,7 @@ Subpackages There are currently three subpackages in the ``metasyn`` package. These are the :mod:`~metasyn.distribution`, :mod:`~metasyn.schema`, and :mod:`~metasyn.demo` packages. * the :mod:`~metasyn.distribution` subpackage contains (submodules with) the classes that are used to fit distributions to the data and draw random values from them. More information on distributions and how to implement them can be found in the :doc:`/developer/distributions` documentation page. -* The :mod:`~metasyn.schema` package simply contains the JSON-schema used to validate metadata, and ensure that it is in line with the :doc:`/developer/GMF`. +* The :mod:`~metasyn.schema` package simply contains the JSON-schema used to validate metadata, and ensure that it is in line with the :ref:`GMF format `. * The :mod:`~metasyn.demo` package is meant for demo and tutorial purposes. It contains only two functions, :meth:`~metasyn.demo.create_titanic_demo`, which can be used to create a demo dataset based on the `Titanic dataset `_, and :meth:`~metasyn.demo.demo_file`, which retrieves the filepath to this demo dataset allowing users to quickly access it. :meth:`~metasyn.demo.demo_file` is imported automatically as part of the main ``metasyn`` package, as such it can be accessed through :meth:`metasyn.demo_file`, as opposed to :meth:`metasyn.demo.demo_file`. diff --git a/docs/source/developer/plugins.rst b/docs/source/developer/plugins.rst index 0a135588..4821e17b 100644 --- a/docs/source/developer/plugins.rst +++ b/docs/source/developer/plugins.rst @@ -1,7 +1,7 @@ Creating plug-ins ================== -As seen on the :doc:`/usage/extensions` page, metasyn can be extended by plug-ins. This page describes how to create your own plug-ins. +As seen on the :doc:`/extensions` page, metasyn can be extended by plug-ins. This page describes how to create your own plug-ins. .. _privacy_plugin: diff --git a/docs/source/usage/extensions.rst b/docs/source/extensions.rst similarity index 99% rename from docs/source/usage/extensions.rst rename to docs/source/extensions.rst index 17167f41..dd0ef85a 100644 --- a/docs/source/usage/extensions.rst +++ b/docs/source/extensions.rst @@ -21,8 +21,7 @@ Custom privacy plug-ins If you wish to create a custom distribution plugin, please refer to the :ref:`privacy_plugin` section on the :doc:`/developer/plugins` developer documentation page. - -Distribution Plug-ins +Distribution plug-ins --------------------- It is possible to add additional distribution types through plug-ins. Unfortunately, at this moment, no additional packages with distributions are publicly available. However, we encourage developers and users to explore the possibilities and contribute their custom distributions to enrich the ``metasyn`` ecosystem further. diff --git a/docs/source/faq.rst b/docs/source/faq.rst index 5d58de8f..6ea5c3c5 100644 --- a/docs/source/faq.rst +++ b/docs/source/faq.rst @@ -1,7 +1,7 @@ FAQ === -Here, we've compiled answers to commonly asked questions about ``metasyn`` and its development. If you have any other questions, need further assistance, or want to discuss something related to metasyn, don't hesitate to contact us directly. You can find our contact details on the :doc:`contact page `. We're more than happy to assist you and provide any additional information you may need. +Here, we've compiled answers to commonly asked questions about ``metasyn`` and its development. If you have any other questions, need further assistance, or want to discuss something related to metasyn, don't hesitate to contact us directly. You can find our contact details on the :ref:`contact page `. We're more than happy to assist you and provide any additional information you may need. **Can I use pandas DataFrames with metasyn?** ----------------------------------------------- @@ -38,13 +38,13 @@ This warning occurs when ``metasyn`` detects a column, that seems to have unique # Call the fit_dataframe() function, passing in the `var_spec` dictionary as the `spec` argument mf = MetaFrame.fit_dataframe(df, var_specs=var_specs) -More information on how to use the optional parameters in the :meth:`metasyn.MetaFrame.fit_dataframe() ` function can be found in :doc:`/usage/generating_metaframes` under :ref:`optionalparams`. +More information on how to use the optional parameters in the :meth:`metasyn.MetaFrame.fit_dataframe() ` function can be found in :doc:`improve_synth`. -You can also set the uniqueness of a variable in the :doc:`configuration file ` +You can also set the uniqueness of a variable in the :doc:`configuration file ` **I found a bug/issue, where can I report it?** ----------------------------------------------- -If you encounter any bugs or have identified an issue with metasyn, we encourage you to report it on our `GitHub issue tracker `_. This allows us to track and address the problem efficiently. Alternatively, you can find out how to contact us through the details provided in our :doc:`contact page `. +If you encounter any bugs or have identified an issue with metasyn, we encourage you to report it on our `GitHub issue tracker `_. This allows us to track and address the problem efficiently. Alternatively, you can find out how to contact us through the details provided in our :ref:`contact page `. **I would like to contribute to the project, how do I get started?** --------------------------------------------------------------------- diff --git a/docs/source/improve_synth.rst b/docs/source/improve_synth.rst new file mode 100644 index 00000000..450b4b14 --- /dev/null +++ b/docs/source/improve_synth.rst @@ -0,0 +1,315 @@ +Improve your synthetic data +=========================== + +When you run metasyn on your dataframe, by default it will attempt +to find the best distribution for each of your columns. This could be sub-optimal: +for example, metasyn won't know whether a column contains names of people. +The column can also be too privacy-sensitive to fit with default methods. + +Metasyn provides two paths to improving the quality of your synthetic data: by further +specifying information directly in python, or by providing a configuration file in the +TOML format. For interactive use, we foresee using python directly, and for programmatic +use the configuration file is a more appropriate interface (see also our :doc:`cli`). + + +.. tab:: Python + + .. code-block:: python + + from metasyn import MetaFrame, VarSpec + from metasyn.distribution import FakerDistribution + from metasyncontrib.disclosure import DisclosurePrivacy + + specs = [ + VarSpec(name="Name", distribution=FakerDistribution(faker_type="name")), + ] + + mf = MetaFrame.fit_dataframe( + df, + privacy=DisclosurePrivacy(), + var_specs=specs, + ) + +.. tab:: Configuration file + + .. code-block:: python + + MetaFrame.fit_dataframe( + df, + var_specs="your_config_file.toml" + ) + + This refers to a configuration file called ``your_config_file.toml``: + + .. code-block:: toml + + privacy = "disclosure" + + [[var]] + name = "Name" + description = "Name of the unfortunate passenger of the titanic." + distribution = {implements = "core.faker", parameters = {faker_type = "name"}} + + More examples for metasyn configuration files are available on our + `GitHub page `_. + +.. admonition:: What is the TOML file format? + + The `TOML `_ file format can be read with any text editor, and is natural to comprehend. + You should be able to create your own TOML files from the examples below, but for more details refer to the TOML + `Documentation `_. One important thing to note is that the TOML format is case sensitive. + + +The remainder of this page serves as a reference for the different options to improve synthetic data quality. + + +General specifications +---------------------- + +Three general options can be set: ``privacy``, ``n_rows``, and ``dist_providers``. +In our python interface, these are arguments to :py:meth:`~MetaFrame.fit_dataframe()`; in the +configuration file these are mentioned at the top of the file. + +Privacy: ``privacy`` +^^^^^^^^^^^^^^^^^^^^ + +Using privacy plug-ins (see :doc:`extensions`), metasyn can increase the level of privacy. +An example is ``disclosure`` privacy, which limits the influence of various problematic +situations such as outliers. + +.. tab:: Python + + .. code-block:: python + + from metasyncontrib.disclosure import DisclosurePrivacy + MetaFrame.fit_dataframe( + df, + privacy=DisclosurePrivacy(partition_size=11) + ) + +.. tab:: Configuration file + + .. code-block:: toml + + privacy = "disclosure" + parameters = {partition_size = 11} + + +Number of rows: ``n_rows`` +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +By default metasyn will set the number of rows to the number of rows of your dataframe. This can be disclosive +or undesirable. In this case you can specify it manually: + +.. tab:: Python + + .. code-block:: python + + MetaFrame.fit_dataframe( + df, + n_rows=100 + ) + +.. tab:: Configuration file + + .. code-block:: toml + + n_rows = 100 + + +Distribution providers: ``dist_providers`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Extra distribution providers can be added using plugins. By default all installed distribution providers +will be used. For reproducibility, it is a good idea to set the distribution providers explicitly, so that +other people using your configuration file understand which plugins/providers were used. This can be done +as follows: + +.. tab:: Python + + .. code-block:: python + + MetaFrame.fit_dataframe( + df, + dist_providers=["builtin", "disclosure"], + ) + +.. tab:: Configuration file + + .. code-block:: toml + + dist_providers = ["builtin", "disclosure"] + + +Adding column specifications +---------------------------- + +In addition to specifications that apply to all columns, you can also specify the behavior for individual columns. +The most common use-case for this is to set the distribution type and/or parameters. + +.. tab:: Python + + .. code-block:: python + + # we suggest using the VarSpec object like so: + from metasyn import MetaFrame, VarSpec + from metasyn.distribution import RegexDistribution + + specs = [ + VarSpec( + name="Cabin", + description="Cabin number of the passenger.", + distribution=RegexDistribution("[A-F][0-9]{2,3}"), + prop_missing=0.2, + ), + VarSpec( + name=..., + description=..., + distribution=..., + ), + ... + ] + + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: python + + # In this example you put the specifications in the toml file. + MetaFrame.fit_dataframe(df, var_specs="your_config_file.toml") + + .. code-block:: toml + + [[var]] + name = "Cabin" + description = "Cabin number of the passenger." + distribution = {implements = "core.regex", parameters = {regex_data = "[A-F][0-9]{2,3}"}} + prop_missing = 0.2 + + [[var]] + name = "Another column name" + description = "With descriptions." + # And more specifications for that column after this. + + +Description: ``description`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can add a description about your column. This will not be used in the estimation phase of metasyn, +but it will be present in the resulting GMF file so that others can more easily understand what is +in the data. + +.. tab:: Python + + .. code-block:: python + + specs = [ VarSpec(name="Cabin", description="Cabin number of the passenger.") ] + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: toml + + [[var]] + name = "Cabin" + description = "Cabin number of the passenger." + + +Missing values: ``prop_missing`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +By default metasyn will estimate the proportion of missing values from the data, but you can +overwrite this with the ``prop_missing`` parameter: + +.. tab:: Python + + .. code-block:: python + + specs = [ VarSpec(name="Cabin", prop_missing=0.2) ] + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: toml + + [[var]] + name = "Cabin" + prop_missing = 0.2 + + +Privacy: ``privacy`` +^^^^^^^^^^^^^^^^^^^^ + +You can set the privacy only for specific columns: + +.. tab:: Python + + .. code-block:: python + + from metasyncontrib.disclosure import DisclosurePrivacy + + specs = [ VarSpec(name="Cabin", privacy=DisclosurePrivacy()) ] + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: toml + + [[var]] + name = "Cabin" + privacy = "disclosure" + + +Uniqueness: ``unique`` +^^^^^^^^^^^^^^^^^^^^^^ + +Some distributions produce only values that are unique without any repeats (see distributions starting with ``Unique`` +in :doc:`api/metasyn.distribution`). By default, metasyn will not select any unique distributions. An exception +is the :class:`metasyn.distribution.UniqueKeyDistribution `; if values in the column are sequentially +increasing. When the column represents a variable that is known to be unique (such as IDs), this can be represented with: + +.. tab:: Python + + .. code-block:: python + + specs = [ VarSpec(name="Cabin", unique=True) ] + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: toml + + [[var]] + name = "Cabin" + unique = true # Notice the lower case for TOML + + + +Distribution: ``distribution`` +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +You can specify the distribution for a column in two different ways: either specify only the type of distribution +and let metasyn find the parameters or specify both the type and parameters of the distribution. + +.. tab:: API + + .. code-block:: python + + from metasyn.distribution import RegexDistribution + + cabin_dist = RegexDistribution("[A-F][0-9]{2,3}") + specs = [ VarSpec(name="Cabin", distribution=cabin_dist) ] + MetaFrame.fit_dataframe(df, var_specs=specs) + +.. tab:: Configuration file + + .. code-block:: toml + + [[var]] + + name = "Cabin" + distribution = {implements = "core.regex", parameters = {regex_data = "[A-F][0-9]{2,3}"}} + +Ensure that the column type matches the type of the distribution, for example if the column has string values, use a distribution +that supports the string type. An overview of all distributions sorted by type can be found in the :doc:`API` diff --git a/docs/source/index.rst b/docs/source/index.rst index 6a2483f9..01be6d9d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,6 +13,14 @@ |break| +Metasyn Documentation +======================= + +``Metasyn`` is a Python package for generating synthetic tabular data with a focus on privacy. +It is designed for owners of sensitive datasets who want to share approximations of their data so that +others can perform exploratory analysis and testing without disclosing real values. + + .. image:: https://img.shields.io/badge/GitHub-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn :alt: GitHub Repository Button :target: https://github.com/sodascience/metasyn @@ -20,92 +28,42 @@ .. image:: https://img.shields.io/badge/GitHub-Issue_Tracker-blue?logo=github&link=https%3A%2F%2Fgithub.com%2Fsodascience%2Fmetasyn%2Fissues :alt: GitHub Issue Tracker Button :target: https://github.com/sodascience/metasyn/issues + -|break| - -Metasyn Documentation -======================= -Welcome to the `metasyn `_ documentation. - -``Metasyn`` is a Python package for generating synthetic tabular data with a focus on privacy. It is designed for owners of sensitive datasets who want to share approximations of their data so that others can perform exploratory analysis and testing without disclosing real values. -``Metasyn`` has three main functionalities: - -.. image:: /images/pipeline_basic.png - :width: 100% - :alt: ``Metasyn`` Pipeline - :align: center - -1. **Estimation**: ``Metasyn`` can **create a MetaFrame**, from a dataset. A MetaFrame is metadata describing a table, augmented with statistical information on the columns. It captures individual distributions and features and enables the generation of synthetic data based on it. -2. **Generation**: ``Metasyn`` can **generate synthetic data** based on a MetaFrame. The synthetic data produced solely depends on the MetaFrame, thereby maintaining a critical separation between the original sensitive data and the generated synthetic data. -3. **Serialization**: ``Metasyn`` can **save a MetaFrame** into an easy-to-read :doc:`/developer/GMF` file. This allows users to audit, understand, and modify their data generation model. These GMF files can also be imported back into Metasyn to generate synthetic data. - -Researchers and data owners can use ``metasyn`` to generate and share synthetic versions of their sensitive datasets, mitigating privacy concerns. Additionally, ``metasyn`` facilitates transparency and reproducibility by allowing the underlying MetaFrames to be saved and shared. Other researchers can use these to regenerate consistent synthetic datasets, validating published work without requiring sensitive data. - - - -.. admonition:: Key Features - - - **MetaFrame Generation**: ``Metasyn`` allows the creation of a MetaFrame from a dataset provided as a `Polars `_ or `Pandas `_ DataFrame. MetaFrames include key characteristics such as *variable names*, *data types*, *percentage of missing values*, and *distribution parameters*. - - **Saving MetaFrames**: ``Metasyn`` can save and load MetaFrames to GMF files. These are JSON files that follow the easy-to-read and understand :doc:`/developer/GMF`. - - **Synthetic Data Generation**: ``Metasyn`` allows for the generation of a Polars DataFrame with synthetic data that resembles the original data. - - **Distribution Fitting**: ``Metasyn`` allows for manual and automatic distribution fitting. - - **Data Type Support**: ``Metasyn`` supports generating synthetic data for a variety of common data types including ``categorical``, ``string``, ``integer``, ``float``, ``date``, ``time``, and ``datetime``. - - **Integration with Faker**: ``Metasyn`` integrates with the `faker `__ package, a Python library for generating fake data such as names and emails. Allowing for more realistic synthetic data. - - **Structured String Detection**: ``Metasyn`` identifies structured strings within your dataset, which can include formatted text, codes, identifiers, or any string that follows a specific pattern. - - **Handling Unique Values**: ``Metasyn`` can identify and process variables with unique values or keys in the data, preserving their uniqueness in the synthetic dataset. +.. toctree:: + :hidden: + :maxdepth: 2 + :caption: Getting started -.. admonition:: Want to know more? + what_is_metasyn + installation + quick_start + tutorials - For more information on ``metasyn`` and its features, check out the :doc:`/about/about` section. +.. toctree:: + :hidden: + :maxdepth: 2 + :caption: Usage -Documentation Outline ---------------------- + improve_synth + datafree .. toctree:: :hidden: :maxdepth: 2 + :caption: Advanced usage - metasyn_in_detail - usage/usage + cli developer/developer api/metasyn - faq - about/about - - -This documentation is designed to help you easily navigate and find the information you need. It is organized into the following four sections: - - -:doc:`metasyn_in_detail` -^^^^^^^^^^^^^^^^^^^^^^^^ - -The :doc:`/metasyn_in_detail` section provides an overview of metasyn's purpose and functionality. - -:doc:`/usage/usage` -^^^^^^^^^^^^^^^^^^^ -The :doc:`/usage/usage` contains detailed, step-by-step instructions, as well as best practices for using the package. If you're new to metasyn, we recommend you start here! + extensions -:doc:`/api/metasyn` -^^^^^^^^^^^^^^^^^^^^^ -The :doc:`/api/metasyn` is a technical reference for metasyn. Here, each function, class, and module is outlined in detail, giving you a comprehensive understanding of how the package works and how to use its various functionalities. If, for example, you'd like to discover which parameters can be used for which function, you can find that here. - -:doc:`/developer/developer` -^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The :doc:`/developer/developer` provides resources for those interested in contributing to metasyn's development. This section includes guidance on how to build upon the existing ``metasyn`` codebase, add new modules, functions, or even integrate other packages. - -:doc:`/faq` -^^^^^^^^^^^^^^^^^^^ -The :doc:`/faq` contains commonly asked questions and answers about metasyn. - -:doc:`/about/about` -^^^^^^^^^^^^^^^^^^^ -The :doc:`About Section ` provides contact information, and licensing details. - - -Indices and tables -================== +.. toctree:: + :hidden: + :maxdepth: 2 + :caption: Extras -* :ref:`genindex` -* :ref:`modindex` + faq + about diff --git a/docs/source/installation.rst b/docs/source/installation.rst new file mode 100644 index 00000000..132cc639 --- /dev/null +++ b/docs/source/installation.rst @@ -0,0 +1,36 @@ +Installation +============ + +This document will guide you through the process of installing the ``metasyn`` package. + +.. note:: + If you're new to Python or simply want to quickly explore the basic features of ``metasyn`` without installing it locally, you can try one of our :doc:`/tutorials`. + + +Step 1: Install Python +---------------------- + +Metasyn requires Python 3.8 or higher. If you don't have Python installed or your Python version is lower than 3.8, you will need to install or upgrade Python. +If you're not familiar with Python or you've never installed it before, refer to the `Python Guide `_ for detailed instructions. +The Python installation will most likely install `pip`, but if not refer to the `Pip Installation Guide `_ for instructions. + +Step 2: Install metasyn +----------------------- + +Install metasyn from the command prompt or terminal with: + + .. code-block:: console + + pip install metasyn + + +Step 3: Verify installation +--------------------------- + +To ensure ``metasyn`` has been successfully installed, run the following command in a Python console: + +.. code-block:: python + + import metasyn + +If the command runs without any errors, you have successfully installed metasyn. diff --git a/docs/source/metasyn_in_detail.rst b/docs/source/metasyn_in_detail.rst deleted file mode 100644 index 572f64e8..00000000 --- a/docs/source/metasyn_in_detail.rst +++ /dev/null @@ -1,296 +0,0 @@ -How does metasyn work? -====================== - -``Metasyn`` is a python package for generating synthetic data with a focus on maintaining privacy. It is aimed at owners of sensitive datasets such as public organisations, research groups, and individual researchers who want to improve the accessibility, reproducibility and reusability of their data. The goal of ``metasyn`` is to make it easy for data owners to share the structure and approximation of the content of their data with others with fewer privacy concerns. - -With this goal in mind, ``metasyn`` restricts itself to the `'synthetically-augmented plausible' `__ category of synthetic data, as categorized by the Office for National Statistics (ONS). - -.. admonition:: ONS criteria for a Synthetically-augmented plausible dataset: - - * Preserve the format and record-level plausibility as detailed previously and replicate marginal (univariate) distributions where possible. - * Constructed based on the real dataset, values are generated based on observed distributions (with added fuzziness and smoothing) but no attempt made to preserve relationships. - * Missing value codes and their frequency is to be preserved. - * Disclosure control evaluation is necessary case by case, special care to be taken with names and so on. - * To be used for extended code testing, minimal analytical value, non-negligible disclosure risk. - - -The ``metasyn`` package also incorporates a **plug-in** system, which enables implementations to generate synthetic data with -stricter and formal privacy and disclosure guarantees within the same framework. Moreover, our system provides an **auditable and editable intermediate representation** in the form of a human- and machine-readable ``.json`` metadata file from which new data can be synthesized. - -Through our focus on privacy and transparency, ``metasyn`` explicitly avoids generating synthetic data with high analytical validity. The data generated by our system is realistic in terms of data structure and plausible in terms of values for each variable, but any multivariate relations or conditional patterns are excluded. This has implications for how this synthetic data can be used: not for statistical analysis and inference, but rather for initial exploration, analysis script development, and communication outside the data owner’s institution. In the intended use case, external researchers can make use of the synthetic data to assess the feasibility of their intended research before making the (often time-consuming) step of requesting access to the sensitive source data for the final analysis. - -The ``metasyn`` Pipeline ------------------------- -The three key stages of the ``metasyn`` pipeline include the **estimation** of the MetaFrame from the original data, the **serialization** of the MetaFrame into an auditable and editable intermediate representation, and the **generation** of the synthetic data from the model represented by the MetaFrame or its serialized representation. This section provides a walkthrough of these steps. - -.. image:: /images/pipeline_basic.png - :alt: Metasyn Pipeline - :align: center - -.. admonition:: An example use case - - The following illustrates a simple example of ``metasyn`` being used, and where the steps in the pipeline (estimation, serialization/deserialization and generation) occur. - - A public health researcher, aiming to conduct research on a sensitive dataset could: - - #. Conduct statistical research on a sensitive dataset of medical records. - #. Fit a MetaFrame to the sensitive dataset. (Estimation) - #. Export the MetaFrame to a JSON file following the GMF standard. (Serialization) - #. Check the GMF file to ensure no private information is present anymore. - #. Use the MetaFrame to generate a synthetic dataset. (Generation) - #. Share the research report alongside the synthetic dataset, GMF file, their script(s) used for the analysis and the outcomes of those scripts with both the real and synthetic dataset. - - Other researchers can then: - - #. Load the MetaFrame by importing the GMF file. (Deserialization) - #. Use the MetaFrame to generate a synthetic dataset. (Generation) - #. Check that the outcomes of the analysis scripts are reproducible on the synthetic data. - - This approach builds confidence that the results and conclusions are accurate, without the need to release sensitive data. - - -Estimation -^^^^^^^^^^^^^ -.. image:: /images/pipeline_estimation_code.png - :alt: Metasyn Estimation Step in Pipeline - :align: center - -The generative model for multivariate datasets in ``metasyn`` makes the simplifying assumption of marginal independence: each column is considered separately, just as is done in e.g., `naïve Bayes classifiers `_. Formally, this leads to the following generative model for the :math:`K`-variate data :math:`\mathbf{x}`: - -.. math:: - - p(x) = \prod_{k=1}^K p(x_k) - -There are many advantages to this naïve approach when compared to more advanced generative models: it is transparent and explainable, it is able to flexibly handle data of mixed types, and it is computationally scalable to high-dimensional datasets. As mentioned before, the tradeoff is the limited analytical validity when the independence assumption does -not hold: in the synthetic data, the expected value of correlations, regression parameters, and other measures of association is 0. - -Model estimation starts with an appropriately pre-processed data frame. For ``metasyn``, this means the data frame is `tidy `_, each column has the correct data type, and missing data are represented by a missing value. Internally, our software uses the `Polars `_ data frame library, as it is fast, has consistent data types, and native support for missing data (``null``). A simple example source table could look like this (note that categorical data has the appropriate ``cat`` data type, not ``str``): - -.. list-table:: - :widths: 10 20 10 20 20 - :header-rows: 1 - - * - ID (i64) - - fruits (cat) - - B (i64) - - cars (cat) - - optional (i64) - * - 1 - - banana - - 5 - - beetle - - 28 - * - 2 - - banana - - 4 - - audi - - 300 - * - 3 - - apple - - 3 - - beetle - - null - * - 4 - - apple - - 2 - - beetle - - 2 - * - 5 - - banana - - 1 - - beetle - - -30 - - -For each data type supported by ``metasyn``, there is a set of candidate distributions that can be fitted to that data -type (see Table below). For each variable, the software fits all available distributions with the same variable type. -From all those fits, the distribution with the lowest `AIC `_ is chosen. - -.. list-table:: - :header-rows: 1 - - * - Variable type - - Data type - - Example values - - Example distribution - * - continuous - - float - - 1.0, 2.1, ... - - UniformDistribution - * - discrete - - int - - 1, 2, ... - - DiscreteUniformDistribution - * - categorical - - pl.Categorical - - Yes, No, Maybe, No - - MultinoulliDistribution - * - string - - str - - A108, C122, B312 - - RegexDistribution - * - time - - time - - 01:40:12 - - UniformTimeDistribution - * - date - - date - - 1937-10-28 - - UniformDateDistribution - * - datetime - - datetime - - 2022-07-23 08:04:22 - - UniformDateTimeDistribution - -.. note:: - See the :doc:`/usage/generating_metaframes` page for information on *how* to generate a MetaFrame. - -Serialization and deserialization -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. image:: /images/pipeline_serialization_code.png - :alt: Metasyn Serialization Step in Pipeline - :align: center - :width: 400px - -After a ``MetaFrame`` object is created, ``metasyn`` allows it to be stored in a human- and machine-readable ``.json`` file. This file contains all the (statistical) metadata as input for the generation step. -Exported :obj:`MetaFrames ` follow the :doc:`/developer/GMF`, a standard designed to be easy to read and understand. -This allows for manual and automatic editing, as well as sharing. - -.. raw:: html - -
    - An example of a saved MetaFrame [click to expand]: - -.. code-block:: json - - { - "n_rows": 5, - "n_columns": 5, - "provenance": { - "created by": { - "name": "metasyn", - "version": "0.4.0" - }, - "creation time": "2023-08-07T12:14:06.232957" - }, - "vars": [ - { - "name": "ID", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.0, - "distribution": { - "implements": "core.unique_key", - "provenance": "builtin", - "class_name": "UniqueKeyDistribution", - "parameters": { - "low": 1, - "consecutive": 1 - } - } - }, - { - "name": "fruits", - "type": "categorical", - "dtype": "Categorical", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "parameters": { - "labels": [ - "apple", - "banana" - ], - "probs": [ - 0.4, - 0.6 - ] - } - } - }, - { - "name": "B", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.0, - "distribution": { - "implements": "core.poisson", - "provenance": "builtin", - "class_name": "PoissonDistribution", - "parameters": { - "mu": 3.0 - } - } - }, - { - "name": "cars", - "type": "categorical", - "dtype": "Categorical", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "parameters": { - "labels": [ - "audi", - "beetle" - ], - "probs": [ - 0.2, - 0.8 - ] - } - } - }, - { - "name": "optional", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.2, - "distribution": { - "implements": "core.discrete_uniform", - "provenance": "builtin", - "class_name": "DiscreteUniformDistribution", - "parameters": { - "low": -30, - "high": 301 - } - } - } - ] - } - -.. raw:: html - -
    -
    - -.. note:: - See the :doc:`/usage/saving_metaframes` page for information on *how* to save and load MetaFrame to and from JSON files. - -Data generation -^^^^^^^^^^^^^^^^ - -.. image:: /images/pipeline_generation_code.png - :alt: Metasyn Estimation Step in Pipeline - :align: center - -Once a MetaFrame model has been created or loaded from a JSON file, new synthetic datasets can be generated from it. - -This process involves repeatedly sampling values from the statistical distributions specified in the MetaFrame. For each variable, values are drawn randomly based on the modeled distribution for that variable. The software handles missing data by occasionally generating null values based on the missing data percentage. - -These per-variable synthetic value samples are collected into a dictionary, with the variable names as keys. This dictionary is then converted into a `Polars `_ DataFrame to create the full synthetic dataset. - - -.. note:: - See the :doc:`/usage/generating_synthetic_data` page for information on *how* to generate synthetic data based on a MetaFrame. - - -Metasyn Extensions ---------------------- -The privacy capacities of ``metasyn`` are extensible through a plug-in system, recognizing that different data owners have different needs and definitions of privacy. A data owner can define under which conditions they would accept open distribution of their synthetic data — be it based on `differential privacy `_, `statistical disclosure control `_, `k-anonymity `_, or another specific definition of privacy. - -Currently released extensions and more information on them is available on the :doc:`/usage/extensions` page. \ No newline at end of file diff --git a/docs/source/usage/quick_start.rst b/docs/source/quick_start.rst similarity index 60% rename from docs/source/usage/quick_start.rst rename to docs/source/quick_start.rst index 585a9f58..11c0ae81 100644 --- a/docs/source/usage/quick_start.rst +++ b/docs/source/quick_start.rst @@ -1,15 +1,15 @@ -Quick start guide -================= +Quickstart +========== -Get started quickly with ``metasyn`` using the following example. In this concise demonstration, you'll learn the basic functionality of ``metasyn`` by generating synthetic data from `titanic `_ dataset. +In this concise demonstration, you'll learn the basic functionality of metasyn by generating synthetic data from `titanic `_ dataset. .. note:: - A more elaborate version of this page is also available as an interactive tutorial available on the :doc:`/usage/interactive_tutorials` page. + A more elaborate version of this page is also available as an interactive tutorial available on the :doc:`/tutorials` page. -Importing Libraries +Importing libraries ------------------- -The first step is to import the required Python libraries. For this example, we will need Polars and metasyn. +The first step is to import the required Python libraries. For this example, we will need Polars (for dataframes) and metasyn. .. code:: python @@ -18,29 +18,35 @@ The first step is to import the required Python libraries. For this example, we from metasyn import MetaFrame, demo_file -Loading the Dataset +Loading the dataset ------------------- Next, load the Titanic CSV file, for this we can use the built-in :meth:`metasyn.demo_file` function. .. code-block:: python - dataset_csv = demo_file() + csv_path = demo_file("titanic") -When loading in the DataFrame, it is important to tell Polars which columns contain categorical values, as it cannot automatically infer this. This is important, as metasyn can later read this information, and use it to generate categorical values where necessary. For this we create a dictionary with the column names that we want to specify as categorical. +When loading in the DataFrame, it is important to use the correct data types. For example, we need to tell Polars which columns are +`categorical `_, as it cannot automatically infer this. This is important, as +metasyn can later read this information and use it to generate categorical values where necessary. For this we create a dictionary +with the column names that we want to specify as categorical. For this example, we specify which columns are dates times as well. .. code-block:: python data_types = {  - "Sex": pl.Categorical, - "Embarked": pl.Categorical, + "Sex": pl.Categorical, + "Embarked": pl.Categorical, + "Birthday": pl.Date, + "Board time": pl.Time, + "Married since": pl.DateTime, } -To finish loading the dataset, we simply use the :meth:`polars.read_csv` function, passing in our CSV file and the ``data_types`` dictionary as parameters. +To finish loading the dataset, we simply use the :meth:`polars.read_csv` function, passing in our CSV file and the ``data_types`` dictionary as an argument. .. code-block:: python - df = pl.read_csv(dataset_csv, schema_overrides=data_types) + df = pl.read_csv(csv_path, schema_overrides=data_types) This converts the CSV file into a DataFrame named ``df``. @@ -51,19 +57,19 @@ This converts the CSV file into a DataFrame named ``df``. The dataset should now be loaded into the DataFrame, and we can verify this by inspecting the first 5 rows of the DataFrame using the ``df.head(5)`` function (or ``print(df.head(5)`` when running from a script). This will output the following table: -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ -| PassengerId | Name | Sex | Age | Parch | Ticket | Fare | Cabin | Embarked | Birthday | Board time | Married since | all\_NA | -+=============+=========================================================+==========+=====+=======+====================+=========+========+==========+==============+============+=======================+=========+ -| 1 | "Braund, Mr. Owen Harris" | "male" | 22 | 0 | "A/5 21171" | 7.25 | null | "S" | "1937-10-28" | "15:53:04" | "2022-08-05 04:43:34" | null | -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ -| 2 | "Cumings, Mrs. John Bradley \(Florence Briggs Thayer\)" | "female" | 38 | 0 | "PC 17599" | 71.2833 | "C85" | "C" | null | "12:26:00" | "2022-08-07 01:56:33" | null | -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ -| 3 | "Heikkinen, Miss. Laina" | "female" | 26 | 0 | "STON/O2. 3101282" | 7.925 | null | "S" | "1931-09-24" | "16:08:25" | "2022-08-04 20:27:37" | null | -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ -| 4 | "Futrelle, Mrs. Jacques Heath \(Lily May Peel\)" | "female" | 35 | 0 | "113803" | 53.1 | "C123" | "S" | "1936-11-30" | null | "2022-08-07 07:05:55" | null | -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ -| 5 | "Allen, Mr. William Henry" | "male" | 35 | 0 | "373450" | 8.05 | null | "S" | "1918-11-07" | "10:59:08" | "2022-08-02 15:13:34" | null | -+-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+--------------+------------+-----------------------+---------+ ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ +| PassengerId | Name | Sex | Age | Parch | Ticket | Fare | Cabin | Embarked | Birthday | Board time | Married since | all\_NA | ++=============+=========================================================+==========+=====+=======+====================+=========+========+==========+============+============+=====================+=========+ +| 1 | "Braund, Mr. Owen Harris" | "male" | 22 | 0 | "A/5 21171" | 7.25 | null | "S" | 1937-10-28 | 15:53:04 | 2022-08-05 04:43:34 | null | ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ +| 2 | "Cumings, Mrs. John Bradley \(Florence Briggs Thayer\)" | "female" | 38 | 0 | "PC 17599" | 71.2833 | "C85" | "C" | null | 12:26:00 | 2022-08-07 01:56:33 | null | ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ +| 3 | "Heikkinen, Miss. Laina" | "female" | 26 | 0 | "STON/O2. 3101282" | 7.925 | null | "S" | 1931-09-24 | 16:08:25 | 2022-08-04 20:27:37 | null | ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ +| 4 | "Futrelle, Mrs. Jacques Heath \(Lily May Peel\)" | "female" | 35 | 0 | "113803" | 53.1 | "C123" | "S" | 1936-11-30 | null | 2022-08-07 07:05:55 | null | ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ +| 5 | "Allen, Mr. William Henry" | "male" | 35 | 0 | "373450" | 8.05 | null | "S" | 1918-11-07 | 10:59:08 | 2022-08-02 15:13:34 | null | ++-------------+---------------------------------------------------------+----------+-----+-------+--------------------+---------+--------+----------+------------+------------+---------------------+---------+ @@ -78,7 +84,7 @@ With the DataFrame loaded, you can now generate a :obj:`MetaFrame ` loaded, you can synthesize new data. To do so, we simply call the :meth:`~metasyn.metaframe.MetaFrame.synthesize` method on the MetaFrame, and pass in the number of rows you would like to generate as a parameter. For example, to generate five rows of synthetic data we can use: @@ -129,21 +135,21 @@ With the :obj:`MetaFrame ` loaded, you can synthesi We can inspect our synthesized data by printing it (``print(synthetic_data)``). This will output a table similar to the following, but with different values as it is randomly generated: -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ -| PassengerId | Name | Sex | Age | Parch | Ticket | Fare | Cabin | Embarked | Birthday | Board time | Married since | all\_NA | -+=============+====================================+========+=====+=======+==========+===========+=======+==========+================+==============+========================+=========+ -| 19 | "Certain. Nearly." | "male" | 30 | 0 | "11941" | 2.025903 | null | "S" | "190-1-228" | "10:1:209" | null | null | -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ -| 795 | "Between. Nature." | "male" | 43 | 0 | "2067" | 16.766045 | null | "S" | "19228-0-014" | "12:507:47" | "2022-08-01 12:558:30" | null | -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ -| 257 | "Country. View. Evidence." | "male" | 44 | 0 | "451553" | 3.687185 | null | "S" | "1937-01-110" | "16:537:18" | null | null | -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ -| 575 | "Scene. Reason. Low. Recent." | "male" | 34 | 1 | "8659" | 25.834306 | null | "S" | "1918-004-205" | "111:137:11" | "2022-08-33 204:26:01" | null | -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ -| 495 | "Morning. Nice. Large. Challenge." | "male" | 8 | 0 | "9582" | 9.150979 | "G01" | "S" | "190-017-05" | "11:1:24" | "2022-07-02 205:0:52" | null | -+-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+----------------+--------------+------------------------+---------+ - -Of course, it's easy to see some flaws with the generated dataset, such as the names, dates and times not making a lot of sense. The page on :doc:`/usage/generating_metaframes` shows how to improve the quality of the synthesized data, such as for example generating fake names using Faker, or generating proper DateTime formatted values. ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ +| PassengerId | Name | Sex | Age | Parch | Ticket | Fare | Cabin | Embarked | Birthday | Board time | Married since | all\_NA | ++=============+====================================+========+=====+=======+==========+===========+=======+==========+============+============+=====================+=========+ +| 19 | "Certain. Nearly." | "male" | 30 | 0 | "11941" | 2.025903 | null | "S" | 1921-10-19 | 14:06:13 | 2022-08-03 15:51:11 | null | ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ +| 795 | "Between. Nature." | "male" | 43 | 0 | "2067" | 16.766045 | null | "S" | 1936-04-09 | 12:26:26 | 2022-07-27 15:15:46 | null | ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ +| 257 | "Country. View. Evidence." | "male" | 44 | 0 | "451553" | 3.687185 | null | "S" | 1930-10-18 | 11:58:39 | null | null | ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ +| 575 | "Scene. Reason. Low. Recent." | "male" | 34 | 1 | "8659" | 25.834306 | null | "S" | 1914-06-14 | 15:43:05 | 2022-08-08 05:50:39 | null | ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ +| 495 | "Morning. Nice. Large. Challenge." | "male" | 8 | 0 | "9582" | 9.150979 | "G01" | "S" | 1914-06-23 | 12:16:21 | 2022-07-19 09:34:07 | null | ++-------------+------------------------------------+--------+-----+-------+----------+-----------+-------+----------+------------+------------+---------------------+---------+ + +Of course, it's easy to see some flaws with the generated dataset, such as the names not making a lot of sense. The page on :doc:`improve_synth` shows how to improve the quality of the synthesized data, such as for example generating fake names using Faker. Conclusion ---------- diff --git a/docs/source/usage/interactive_tutorials.rst b/docs/source/tutorials.rst similarity index 55% rename from docs/source/usage/interactive_tutorials.rst rename to docs/source/tutorials.rst index 062bbda0..7ff3bbb4 100644 --- a/docs/source/usage/interactive_tutorials.rst +++ b/docs/source/tutorials.rst @@ -1,9 +1,10 @@ -Interactive Tutorials +Interactive tutorials ===================== -If you're new to Python or simply want to quickly explore the basic features of metasyn, you can try our interactive tutorial. It provides a step-by-step walkthrough and example dataset to help you get started. +If you're new to Python or simply want to quickly explore the basic features of metasyn, +you can try our interactive tutorial. It provides a step-by-step walkthrough and example dataset +to help you get started. You can access it here: - - Binder: `Open in Binder `__ - Google Colab: `Open in Google Colab `__ - GitHub: `View on GitHub `__ diff --git a/docs/source/usage/config_files.rst b/docs/source/usage/config_files.rst deleted file mode 100644 index c5ae0d59..00000000 --- a/docs/source/usage/config_files.rst +++ /dev/null @@ -1,145 +0,0 @@ -Configuration Files -=================== - -Configuration files are designed to be used in conjunction with the :doc:`/usage/cli` (though they can be used with the Python API too) to help specify distribution behavior when creating generative metadata. - -In the configuration file, you can specify the variable specification, distribution providers, privacy level and number of rows of data to generate. - -The configuration file must be in the `.toml` format. - -.. admonition:: What is TOML? - - TOML, short for Tom's Obvious, Minimal Language, aims to be a minimal configuration file with easy readability. - - A TOML file consists of key/value pairs, tables (groups of key/value pairs), and optionally, arrays of tables. Values can be strings, numbers, booleans, dates/times, or arrays. TOML is **case sensitive**, but does not care about indentation or whitespace. - - Refer to the `TOML documentation `_ to learn more about the syntax and structure of TOML files. - - -Configuration File Structure ----------------------------- - - -Specifying rows -^^^^^^^^^^^^^^^ - -To specify the number of rows to generate, use the ``n_rows`` key. - -For example: - -.. code-block:: toml - - n_rows = 100 - -Variable specification -^^^^^^^^^^^^^^^^^^^^^^^ - -To set the variable spec for a variable, create an entry in the ``[[var]]`` array. Each entry should include the following: - -#. ``name``: The name of the variable. -#. ``description`` (optional): A description of the variable. -#. ``distribution``: The distribution specification for the variable. This is a dictionary that includes the ``implements`` key specifying the distribution type, and optionally a ``parameters`` key specifying parameters for the distribution. To find distributions, and their parameters, refer to the :doc:`/api/metasyn.distribution` page. -#. ``privacy`` (optional): The privacy specification for the variable. This is a dictionary that includes the ``name`` of the privacy, and a ``parameters`` key for specifying its parameters. -#. ``prop_missing`` (optional): The proportion of missing values for the variable. - -For example: - -.. code-block:: toml - - [[var]] - name = "Cabin" - description = "Cabin number of the passenger." - distribution = {implements = "core.regex", parameters = {regex_data = "[A-F][0-9]{2,3}"}} - prop_missing = 0.1 - privacy = {name = "disclosure", parameters = {partition_size = 21}} - - -Distribution providers -^^^^^^^^^^^^^^^^^^^^^^ - -To specify the distribution providers, use the ``dist_providers`` key. This is an array of strings, where each string is the name of a distribution provider. Note that this is only necessary if you install distribution plugins. - -For example: - -.. code-block:: toml - - dist_providers = ["builtin", "metasyn-disclosure"] - -Privacy level -^^^^^^^^^^^^^ - -To specify the privacy level, use the ``privacy`` key. This is a dictionary that includes the ``name`` key specifying the privacy level, and optionally a ``parameters`` key specifying parameters for the privacy level. - -For example: - -.. code-block:: toml - - [privacy] - name = "disclosure" - parameters = {partition_size = 11} - - -Example Configuration File --------------------------- - -The following is an example which specifies the distribution providers, privacy level, variable specifications and number of rows of data to generate (for the :doc:`Titanic demo dataset     `): - - -.. code-block:: toml - - dist_providers = ["builtin", "metasyn-disclosure"] - - n_rows = 100 - - [privacy] - name = "disclosure" - parameters = {partition_size = 11} - - - [[var]] - name = "PassengerId" - distribution = {unique = true} # Notice booleans are lower case in .toml files. - - [[var]] - name = "Name" - prop_missing = 0.1 - description = "Name of the unfortunate passenger of the titanic." - distribution = {implements = "core.faker", parameters = {faker_type = "name", locale = "en_US"}} - - [[var]] - name = "Fare" - distribution = {implements = "core.exponential"} - - [[var]] - name = "Age" - distribution = {implements = "core.uniform", parameters = {lower = 20, upper = 40}} - - [[var]] - name = "Cabin" - distribution = {implements = "core.regex", parameters = {regex_data = "[A-F][0-9]{2,3}"}} - privacy = {name = "disclosure", parameters = {partition_size = 21}} - - -Synthetic data without input file ---------------------------------- -It is also possible to create a GMF file without inputting a dataset, or to add additional fictive columns to those already present in a dataset. - -To do so, you need to fully specify each column (variable) you want to generate. You will also need to set the data_free parameter to true, to indicate that the variable will be generated from scratch, instead of being based on existing data. -Finally, you will need to set the number of rows to generate. - -For example, the following configuration file will generate a GMF file with 100 rows of synthetic data, with a unique key column named ``PassengerId``: - - .. code-block:: toml - - n_rows = 100 - - [[var]] - - name = "PassengerId" - data_free = true - prop_missing = 0.0 - description = "ID of the unfortunate passenger." - var_type = "discrete" - distribution = {implements = "core.unique_key", unique = true, parameters = {consecutive = true, low = 0}} - - diff --git a/docs/source/usage/generating_metaframes.rst b/docs/source/usage/generating_metaframes.rst deleted file mode 100644 index 79d97b22..00000000 --- a/docs/source/usage/generating_metaframes.rst +++ /dev/null @@ -1,152 +0,0 @@ -Generating MetaFrames -===================== - -One of the main features of ``metasyn`` is to create a :obj:`MetaFrame `, an object which captures the essential aspects of the dataset, including variable names, types, data types, the percentage of missing values, and distribution attributes. :obj:`MetaFrame ` objects essentially capture all the information needed to generate a synthetic dataset that aligns with the original dataset. - -.. image:: /images/pipeline_estimation_code.png - :alt: MetaFrame Generation Flow - :align: center - - -.. admonition:: Want to learn more? - - This page focuses on using ``metasyn`` to generate MetaFrames. If you're interested in learning more about how MetaFrames are generated behind the scenes and the assumptions involved, see the :doc:`/metasyn_in_detail` page for details. - -Preparing the Dataset ---------------------- - -Before we can pass a dataset into metasyn, we need to convert it to a `Polars `__ DataFrame. In doing so, we can indicate which columns contain categorical values. We can also tell Polars to find columns that may contain dates or timestamps. Metasyn later uses this information to generate categorical or date-like values where appropriate. For more information on how to use Polars, check out the `Polars documentation `__. - -For example, if we want to load a dataset named 'dataset.csv' into a Polars DataFrame, set the columns ``Color`` and ``Fruit`` to be categorical and parse dates in the DataFrame. We can use the following code: - -.. code:: python - - # Create a Polars DataFrame - df = pl.read_csv( - source="dataset.csv", - schema_overrides={"Color": pl.Categorical, "Fruit": pl.Categorical}, - try_parse_dates=True, - ) - -.. admonition:: Note on Pandas and Polars DataFrames - - Internally, ``metasyn`` uses Polars (instead of Pandas) mainly because typing and the handling of non-existing data is more consistent. It is possible to supply a Pandas DataFrame instead of a Polars DataFrame to the ``MetaFrame.fit_dataframe`` method. However, this uses the automatic Polars conversion functionality, which for some edge cases results in problems. Therefore, we recommend users to create Polars DataFrames. The resulting synthetic dataset is always a Polars DataFrame, but this can be easily converted back to a Pandas DataFrame by using ``df_pandas = df_polars.to_pandas()``. - -Generating a MetaFrame ----------------------- -With the DataFrame in place, we can now generate a :obj:`MetaFrame ` object using the :meth:`metasyn.MetaFrame.fit_dataframe(df) ` class method, passing in a DataFrame as a parameter. - -.. image:: /images/pipeline_estimation_code.png - :alt: MetaFrame Generation With Code Snippet - :align: center - -The following code returns a :obj:`MetaFrame` object named :obj:`mf`, based on a DataFrame named :obj:`df`. - -.. code-block:: python - - mf = metasyn.MetaFrame.fit_dataframe(df) - - - -It is possible to print the statistical metadata contained in the :obj:`MetaFrame ` to the console/output log. This can simply be done by calling the Python built-in `print` function on a :obj:`MetaFrame `: - -.. code-block:: python - - print(mf) - - -.. _OptionalParams: - -Optional Parameters ----------------------- -The :meth:`metasyn.MetaFrame.fit_dataframe() ` class method -allows you to have more control over how your synthetic dataset is generated with additional (optional) -parameters: - -Besides the required `df` parameter, :meth:`metasyn.MetaFrame.fit_dataframe() ` -accepts three parameters: ``var_specs``, ``dist_providers`` and ``privacy``. - -Let's take a look at each optional parameter individually: - -var_specs -^^^^^^^^^ -**var_specs** is an optional list that outlines specific directives for columns (variables) in the DataFrame. -This list can also be generated from a .toml file. In that case you have to provide a string of path instead of -a list. -The potential directives include: - - - ``name``: This specifies the column name and is mandatory. - - - ``distribution``: Allows you to specify the statistical distribution of each column. To see what distributions are available refer to the :doc:`distribution package API reference`. - - - ``unique``: Declare whether the column in the synthetic dataset should contain unique values. By default no column is set to unique. - - .. admonition:: Detection of unique variables - - When generating a MetaFrame, ``metasyn`` will automatically analyze the columns of the input DataFrame to detect ones that contain only unique values. - If such a column is found, and it has not manually been set to unique in the ``var_specs`` list, the user will be notified with the following warning: - ``Metasyn detected that variable '{variable name}' is potentially unique."`` - - It is safe to ignore this warning - however, be aware that without setting the column as unique, ``metasyn`` may generate duplicate values for that column when synthesizing data. - - To remove the warning and ensure the values in the synthesized column are unique, set the column to be unique (``unique = True``) in the ``var_specs`` list. - - - ``description``: Includes a description for each column in the DataFrame. - - - - ``privacy``: Set the privacy level for each column to bypass potential privacy concerns. - - - - ``prop_missing``: Set the intended proportion of missing values in the synthetic data for each column. - - -.. admonition:: Example use of the ``var_specs`` parameter - - - For the column ``PassengerId``, we want unique values. - - The ``Name`` column should be populated with realistic fake names using the `Faker `_ library. - - In the ``Fare`` column, we aim for an exponential distribution. - - Age values in the ``Age`` column should follow a discrete uniform distribution, ranging between 20 and 40. - - The following code to achieve this would look like: - - .. code-block:: python - - from metasyn.distribution import FakerDistribution, DiscreteUniformDistribution - from metasyn.config import VarSpec - - # Create a specification list for generating synthetic data - var_specs = [ - # Ensure unique values for the `PassengerId` column - VarSpec("PassengerId", unique=True), - - # Utilize the Faker library to synthesize realistic names for the `Name` column - VarSpec("Name", distribution=FakerDistribution("name")), - - # Fit `Fare` to an log-normal distribution, but base the parameters on the data - VarSpec("Name", distribution="lognormal"), - - # Set the `Age` column to a discrete uniform distribution ranging from 20 to 40 - VarSpec("Age", distribution=DiscreteUniformDistribution(20, 40)), - ] - - mf = MetaFrame.fit_dataframe(df, var_specs=var_specs) - - -dist_providers -^^^^^^^^^^^^^^^^ - -The parameter **dist_providers** determines which plug-ins should be loaded and in which order. By default all plug-ins will be loaded and available for fitting, which -is what most users will probably want. It can be helpful for reproducibility to specify which providers were used. The distributions that are available through the `metasyn` -package itself (without installing any plug-ins) is called "builtin". - -privacy -^^^^^^^^^ -**privacy** allows you to set the global privacy level for synthetic data generation. If it's not provided, the function defaults it to ``None``. - -.. warning:: - Privacy features (such as differential privacy or other forms of disclosure control) are currently under active development. More information on currently available extensions can be found in the :doc:`/usage/extensions` section. - - -Config Files -^^^^^^^^^^^^ -It is also possible specify variable specifications, distribution providers and privacy levels through a .toml config file. This is mostly intended for working with the :doc:`/usage/cli`, but can also be used in the Python API. Information on how to use config files can be found in the :doc:`/usage/config_files` section. diff --git a/docs/source/usage/generating_synthetic_data.rst b/docs/source/usage/generating_synthetic_data.rst deleted file mode 100644 index add360ef..00000000 --- a/docs/source/usage/generating_synthetic_data.rst +++ /dev/null @@ -1,39 +0,0 @@ -Generating Synthetic Data -========================= - -Metasyn can **generate synthetic data** from any :obj:`MetaFrame ` object. - -.. image:: /images/pipeline_generation_code.png - :alt: Synthetic Data Generation - :align: center - -The generated synthetic data, emulates the original data's format and plausibility at the individual record -level and attempts to reproduce marginal (univariate) distributions where possible. -Generated values are based on the observed distributions. -The frequency of missing values is also maintained in the synthetically-augmented dataset. - -The generated data does **not** preserve any relationships between variables. - -.. admonition:: Prerequisite - - Before synthetic data can be generated, a :obj:`MetaFrame ` object must be :doc:`created     ` or :doc:`loaded `. - -To generate a synthetic dataset, simply call the :meth:`MetaFrame.synthesize(n) ` method on a :obj:`MetaFrame ` object. This method takes a parameter `n` which represents the number of rows of data that should be generated. By default (when `n` is not provided), metasyn tries to generate as many rows as in the original dataset. - -.. image:: /images/pipeline_generation_code.png - :alt: Synthetic Data Generation With Code Snippet - :align: center - -The following code generates 5 rows of data based on a :obj:`MetaFrame ` named ``mf``. - -.. code-block:: python - - mf.synthesize(5) - -Upon succesful execution of the :meth:`MetaFrame.synthesize(n)` method, a `Polars DataFrame `_ will be returned. - - - -.. admonition:: Command-Line Interface - - Though we generally recommend using the Python API for using metasyn, you can also generate synthetic data using the CLI. Instructions for doing so can be found in the :doc:`CLI documentation `. \ No newline at end of file diff --git a/docs/source/usage/installation.rst b/docs/source/usage/installation.rst deleted file mode 100644 index 8bd37e06..00000000 --- a/docs/source/usage/installation.rst +++ /dev/null @@ -1,78 +0,0 @@ -Installation Guide -================== - -This document will guide you through the process of installing the ``metasyn`` package. - -.. note:: - If you're new to Python or simply want to quickly explore the basic features of ``metasyn`` without installing it locally, you can try one of our :doc:`/usage/interactive_tutorials`. - - -Step 1: Python Installation ---------------------------- - -Metasyn requires Python 3.8 or higher. If you don't have Python installed or your Python version is lower than 3.8, you will need to install or upgrade Python. - -If you're not familiar with Python or you've never installed it before, refer to the `Python Guide `_ for detailed instructions. - -Step 2: Installing PIP ----------------------- - -Metasyn uses `PyPI `_ for distribution, which requires pip, the Python package installer, to install. If you haven't installed pip, refer to the `Pip Installation Guide `_ for instructions. - -Step 3: Installing Metasyn ----------------------------- - -There are two ways to install metasyn, you can either download the latest official version from PyPI or directly from our GitHub repository. - -.. tab:: PyPI - - .. code-block:: console - - pip install metasyn - -.. tab:: GitHub - - .. code-block:: console - - pip install git+https://github.com/sodascience/meta-synth.git - -Choose the method that best suits your needs. If you're unsure, using PyPI is the simplest and most straightforward method. - -Step 4: Verifying Installation -------------------------------- - -To ensure ``metasyn`` has been successfully installed, run the following command in a Python console: - -.. code-block:: python - - import metasyn - -If the command runs without any errors, you have successfully installed metasyn. - -Optional: Creating a Virtual Environment ----------------------------------------- - -Although not necessary, it's a good practice to create a virtual environment for each Python project to manage dependencies. You can create a virtual environment using either venv (built into Python) or a similar tool like `Conda `_. If you're unfamiliar with this concept, refer to the `Python Virtual Environments Guide `_. - -To create a virtual environment using venv: - -.. code-block:: console - - python3 -m venv metasyn-env - -To activate the environment: - -.. tab:: Windows - - .. code-block:: console - - metasyn-env\Scripts\activate - -.. tab:: Unix or MacOS: - - .. code-block:: console - - source metasyn-env/bin/activate - -With the virtual environment activated, you can then install ``metasyn`` as described in Step 3. To exit the virtual environment, simply type `deactivate` in your console. - diff --git a/docs/source/usage/saving_metaframes.rst b/docs/source/usage/saving_metaframes.rst deleted file mode 100644 index 55d3241f..00000000 --- a/docs/source/usage/saving_metaframes.rst +++ /dev/null @@ -1,343 +0,0 @@ -.. |break| raw:: html - -
    - -Exporting and importing MetaFrames -=================================== - -Metasyn can serialize and **save a MetaFrame** into a GMF file. GMF files are JSON files that follow the :doc:`/developer/GMF` and have been designed to be easy to read and understand. This allows users to audit, understand, modify and share their data generation model with ease. - -.. image:: /images/pipeline_serialization_code.png - :alt: MetaFrame Serialization Flow - :align: center - :width: 400px - -Exporting a MetaFrame ----------------------- -MetaFrames can be serialized and saved to a GMF file by calling the :meth:`metasyn.metaframe.MetaFrame.save` method on a :obj:`MetaFrame`. - -The following code saves a generated :obj:`MetaFrame` object named ``mf`` to a GMF file named ``saved_metaframe``. - -.. code-block:: python - - mf.save("saved_metaframe.json") - -.. raw:: html - -
    - An example of a MetaFrame that has been saved to a GMF file: - -.. code-block:: json - - { - "n_rows": 5, - "n_columns": 5, - "provenance": { - "created by": { - "name": "metasyn", - "version": "0.8.1" - }, - "creation time": "2024-04-29T12:07:36.646884" - }, - "vars": [ - { - "name": "ID", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.0, - "distribution": { - "implements": "core.unique_key", - "version": "1.0", - "provenance": "builtin", - "class_name": "UniqueKeyDistribution", - "unique": true, - "parameters": { - "lower": 1, - "consecutive": true - } - }, - "creation_method": { - "created_by": "metasyn", - "unique": true - } - }, - { - "name": "fruits", - "type": "categorical", - "dtype": "Categorical(ordering='physical')", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "version": "1.0", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "unique": false, - "parameters": { - "labels": [ - "apple", - "banana" - ], - "probs": [ - 0.4, - 0.6 - ] - } - }, - "creation_method": { - "created_by": "metasyn" - } - }, - { - "name": "B", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.0, - "distribution": { - "implements": "core.uniform", - "version": "1.0", - "provenance": "builtin", - "class_name": "DiscreteUniformDistribution", - "unique": false, - "parameters": { - "lower": 1, - "upper": 6 - } - }, - "creation_method": { - "created_by": "metasyn", - "unique": false - } - }, - { - "name": "cars", - "type": "categorical", - "dtype": "Categorical(ordering='physical')", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "version": "1.0", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "unique": false, - "parameters": { - "labels": [ - "audi", - "beetle" - ], - "probs": [ - 0.2, - 0.8 - ] - } - }, - "creation_method": { - "created_by": "metasyn" - } - }, - { - "name": "optional", - "type": "discrete", - "dtype": "Int64", - "prop_missing": 0.2, - "distribution": { - "implements": "core.uniform", - "version": "1.0", - "provenance": "builtin", - "class_name": "DiscreteUniformDistribution", - "unique": false, - "parameters": { - "lower": -30, - "upper": 301 - } - }, - "creation_method": { - "created_by": "metasyn" - } - } - ] - } - - -.. raw:: html - -
    - -|break| - - -It is possible to preview the GMF file, without having to save it. This can be done by calling the Python built-in :func:`repr ` function on a :obj:`MetaFrame` object, and printing its output. - -.. code-block:: python - - gmf_preview = repr(mf) - print(gmf_preview) - - -.. _loading-a-metaframe: - -Loading a MetaFrame -------------------- -You can load a MetaFrame from a GMF file using the :meth:`MetaFrame.load ` classmethod. - -The following code loads a :obj:`MetaFrame` object named ``mf`` from a GMF file named ``saved_metaframe``. - -.. code-block:: python - - mf = metasyn.MetaFrame.load("saved_metaframe.json") - - -Tweaking an saved MetaFrame ------------------------------------ -Since the JSON is formatted in an easy to read way (for both humans *and* computers), it is easy to manually edit the metadata, or to automatically edit the metadata using a script. - -For example, you can: - -* Change variable names -* Add or remove variables -* Change variable types -* Modify distribution parameters -* Adjust missing data rates - -Let's say we import a MetaFrame from the GMF (from earlier on this page) and use it to synthesize 5 rows of data. This results in the following dataset (note that the resulting dataset will be different every time you run this code, since the data is randomly generated): - -.. list-table:: - :widths: 10 20 10 20 20 - :header-rows: 1 - - * - ID (i64) - - fruits (cat) - - B (i64) - - cars (cat) - - optional (i64) - * - 1 - - apple - - 1 - - beetle - - 287 - * - 2 - - banana - - 2 - - beetle - - 265 - * - 3 - - apple - - 6 - - beetle - - 152 - * - 4 - - banana - - 0 - - beetle - - null - * - 5 - - banana - - 5 - - audi - - 87 - -Well, what if we wanted to change the distribution of the ``fruits`` variable to instead be 30% ``apple``, 30% ``banana``, and introduce a new fruit ``orange`` with a distribution of 40%? We can do this by editing the ``probs`` and ``labels`` attributes of the ``fruits`` variable in the saved MetaFrame. The following is the edited MetaFrame: - - -.. tab:: GMF file before - - .. code-block:: json - - // ... - { - "name": "fruits", - "type": "categorical", - "dtype": "Categorical", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "parameters": { - "labels": [ - "apple", - "banana" - ], - "probs": [ - 0.4, - 0.6 - ] - } - } - }, - // ... - -.. tab:: GMF file after - - .. code-block:: json - :emphasize-lines: 15, 18, 19, 20 - - // ... - { - "name": "fruits", - "type": "categorical", - "dtype": "Categorical", - "prop_missing": 0.0, - "distribution": { - "implements": "core.multinoulli", - "provenance": "builtin", - "class_name": "MultinoulliDistribution", - "parameters": { - "labels": [ - "apple", - "banana", - "orange" - ], - "probs": [ - 0.3, - 0.3, - 0.4 - ] - } - } - }, - // ... - - -If we now synthesize five rows of data based on a MetaFrame loaded from the edited GMF file, we get the following dataset, which as you can see contains the added ``orange`` fruit, and follows the new distribution: - -.. list-table:: - :widths: 10 20 10 20 20 - :header-rows: 1 - - * - ID (i64) - - fruits (cat) - - B (i64) - - cars (cat) - - optional (i64) - * - 1 - - orange - - 4 - - beetle - - 208 - * - 2 - - banana - - 1 - - beetle - - 78 - * - 3 - - orange - - 3 - - audi - - -30 - * - 4 - - apple - - 2 - - beetle - - 164 - * - 5 - - orange - - 5 - - audi - - 51 - - -As you can see, you can modify the metadata to change how data is synthesized. Similarly to this example, any other aspect of the MetaFrame can be edited, including the variable names, the variable types, the data types, the percentage of missing values, and the distribution attributes. - -.. warning:: - Be extra careful when manually editing GMF files as errors in names, values, or formatting can cause problems. In this case, metasyn will most likely produce JSON validation errors. - diff --git a/docs/source/usage/usage.rst b/docs/source/usage/usage.rst deleted file mode 100644 index e124609b..00000000 --- a/docs/source/usage/usage.rst +++ /dev/null @@ -1,19 +0,0 @@ -User Guide -============ - -Welcome to the ``metasyn`` User Guide. This section features everything you need to know about how to use metasyn. -information on installation, quickstart, tutorials and information on the core features of the package. - -.. toctree:: - :maxdepth: 1 - :caption: Sections: - - installation - quick_start - generating_metaframes - saving_metaframes - generating_synthetic_data - config_files - cli - interactive_tutorials - extensions \ No newline at end of file diff --git a/docs/source/what_is_metasyn.rst b/docs/source/what_is_metasyn.rst new file mode 100644 index 00000000..1c6d2e30 --- /dev/null +++ b/docs/source/what_is_metasyn.rst @@ -0,0 +1,276 @@ +What is metasyn? +================ + +Metasyn is a python package for generating synthetic data with a focus on maintaining privacy. +It is aimed at owners of sensitive datasets such as public organisations, research groups, and individual researchers who +want to improve the accessibility, reproducibility and reusability of their data. The goal of metasyn is to make it easy +for data owners to share the structure and approximation of the content of their data with others with fewer privacy concerns. + +Use cases +--------- + +Below are a few possible use cases for metasyn: + +- A researcher who cannot share their dataset because of privacy concerns, but shares a synthetic version for reproducibility. +- A data provider wants to show a preview of their sensitive data using synthetic versions of their datasets. +- A developer can create synthetic data before they have real data so they can write and test data analysis scripts. + +Metasyn is specifically **not** designed for creating highly accurate synthetic data, where all relationships between columns are reproduced. + +Key features +------------ +- **Easy**: Creating your first synthetic dataset shouldn't take more than 15 minutes with our :doc:`quickstart `. +- **Fast**: Creating synthetic data takes mere seconds for medium sized (~1000 rows) datasets. +- **Safe and Understandable Synthetic Data**: As little information as possible is retained from the original dataset, and you can inspect and understand exactly which information is used to create your synthetic data. +- **Flexible**: You can :doc:`adjust ` the synthetic data columns to your liking, create your own distributions, extension and more. +- **Data Type Support**: Metasyn supports generating synthetic data for a variety of common data types including ``categorical``, ``string``, ``integer``, ``float``, ``date``, ``time``, and ``datetime``. +- **Integration with Faker**: Metasyn integrates with the `faker `__ package, a Python library for generating fake data such as names and emails. Allowing for more realistic synthetic data. +- **Structured String Detection**: Metasyn identifies structured strings within your dataset, which can include formatted text, codes, identifiers, or any string that follows a specific pattern. +- **Handling Unique Values**: Metasyn can identify and process variables with unique values or keys in the data, preserving their uniqueness in the synthetic dataset. + + +For more detail on how metasyn works, see our `paper `_. + +.. _metaframes and GMF: + +MetaFrames and GMF files +------------------------ + +.. image:: /images/pipeline_basic.png + :alt: Metasyn Pipeline + :align: center + +One of the main distinguishing features of metasyn is the ability to save the model for +your dataset in a standardized, human understandable and machine readable format: the Generative +Metadata Format (GMF). The equivalent object in Python is the :class:`metasyn.MetaFrame`. + +.. admonition:: Why do I need the MetaFrame or GMF file? + + MetaFrames are a core part of the design philosphy of metasyn: you should inspect the model before + releasing any potentially private information. While metasyn will generally provide safe synthetic + output, this cannot be guaranteed; whether something is considered privacy sensitive depends on the + context, which metasyn does not have access to. We also generally recommend saving the GMF file + for reproducibility purposes. + +The GMF format is available in two file formats: .json and .toml. + +.. raw:: html + +
    + An example of a .json GMF file [click to expand]: + +.. code-block:: json + + { + "n_rows": 5, + "n_columns": 5, + "provenance": { + "created by": { + "name": "metasyn", + "version": "1.1.0" + }, + "creation time": "2024-10-01T09:57:15.595769" + }, + "vars": [ + { + "name": "ID", + "type": "discrete", + "dtype": "Int64", + "prop_missing": 0.0, + "distribution": { + "implements": "core.unique_key", + "version": "1.0", + "provenance": "builtin", + "class_name": "UniqueKeyDistribution", + "unique": true, + "parameters": { + "lower": 1, + "consecutive": true + } + }, + "creation_method": { + "created_by": "metasyn", + "unique": true + } + }, + { + "name": "fruits", + "type": "categorical", + "dtype": "Categorical(ordering='physical')", + "prop_missing": 0.0, + "distribution": { + "implements": "core.multinoulli", + "version": "1.0", + "provenance": "builtin", + "class_name": "MultinoulliDistribution", + "unique": false, + "parameters": { + "labels": [ + "apple", + "banana" + ], + "probs": [ + 0.4, + 0.6 + ] + } + }, + "creation_method": { + "created_by": "metasyn" + } + }, + { + "name": "B", + "type": "discrete", + "dtype": "Int64", + "prop_missing": 0.0, + "distribution": { + "implements": "core.uniform", + "version": "1.0", + "provenance": "builtin", + "class_name": "DiscreteUniformDistribution", + "unique": false, + "parameters": { + "lower": 1, + "upper": 6 + } + }, + "creation_method": { + "created_by": "metasyn", + "unique": false + } + } + ] + } + +.. raw:: html + +
    +
    + +.. raw:: html + +
    + An example of a .toml GMF file [click to expand]: + +.. code-block:: toml + + n_rows = 891 # Number of rows + n_columns = 13 # Number of columns + + [provenance] + "creation time" = "2024-10-28T13:58:04.604396" + [provenance."created by"] + name = "metasyn" + version = "1.0.2.dev34+gd68929e" + + [[vars]] # Metadata for column with name PassengerId + name = "PassengerId" + type = "discrete" + dtype = "Int64" + prop_missing = 0.0 # Fraction of missing values, remaining: 891 values + + [vars.distribution] + implements = "core.unique_key" + version = "1.0" + provenance = "metasyn-disclosure" + class_name = "DisclosureUniqueKey" + unique = true + + [vars.distribution.parameters] + lower = 0 + consecutive = true + + # The above parameters for column 'PassengerId' were generated using disclosure control + # with a maximum dominance of 0.5 and data aggregated into partitions of size 11 + # before any parameters of the distribution were estimated + # The parameter(s) lower were estimated by the average of the 11 lowest or highest values. + + + [vars.creation_method] + created_by = "metasyn" + unique = true + + [vars.creation_method.privacy] + name = "disclosure" + + [vars.creation_method.privacy.parameters] + partition_size = 11 + + [[vars]] # Metadata for column with name Name + name = "Name" + type = "string" + dtype = "String" + prop_missing = 0.1 # Fraction of missing values, remaining: 802 values + description = "Name of the unfortunate passenger of the titanic." + + [vars.distribution] + implements = "core.faker" + version = "1.0" + provenance = "builtin" + class_name = "FakerDistribution" + unique = false + + [vars.distribution.parameters] + faker_type = "name" + locale = "en_US" + + # The above parameters for column 'Name' were manually set by the user, no data was (directly) used. + + + [vars.creation_method] + created_by = "metasyn" + implements = "core.faker" + + [vars.creation_method.parameters] + faker_type = "name" + locale = "en_US" + + [vars.creation_method.privacy] + name = "disclosure" + + [vars.creation_method.privacy.parameters] + partition_size = 11 + + [[vars]] # Metadata for column with name Sex + name = "Sex" + type = "string" + dtype = "String" + prop_missing = 0.0 # Fraction of missing values, remaining: 891 values + + [vars.distribution] + implements = "core.multinoulli" + version = "1.0" + provenance = "metasyn-disclosure" + class_name = "DisclosureMultinoulli" + unique = false + + [vars.distribution.parameters] + labels = ["female", "male"] + probs = [0.35241301907968575, 0.6475869809203143] + + # The above parameters for column 'Sex' were generated using disclosure control + # with a maximum dominance of 0.5 and data aggregated into partitions of size 11 + # before any parameters of the distribution were estimated. + # Counts: [314 577] + + + + [vars.creation_method] + created_by = "metasyn" + + [vars.creation_method.privacy] + name = "disclosure" + + [vars.creation_method.privacy.parameters] + partition_size = 11 + + +.. raw:: html + +
    +
    + +Metasyn supports both the .toml and .json variant equally. The advantage of the .json file is that it has better +compatibility with programming languages. On the other hand, .toml files are generally easier to read and edit +manually. diff --git a/metasyn/config.py b/metasyn/config.py index 59149d15..58c1b268 100644 --- a/metasyn/config.py +++ b/metasyn/config.py @@ -196,7 +196,7 @@ def __init__(self, var_spec: VarSpec, meta_config: MetaConfig): self.meta_config = meta_config def __getattribute__(self, attr): - if attr in ["privacy", "data_free", "prop_missing", "privacy"]: + if attr in ["privacy", "data_free", "prop_missing"]: if getattr(self.var_spec, attr) is None: return getattr(self.meta_config.defaults, attr) return getattr(self.var_spec, attr) diff --git a/metasyn/distribution/categorical.py b/metasyn/distribution/categorical.py index bad52dfa..6af2f006 100644 --- a/metasyn/distribution/categorical.py +++ b/metasyn/distribution/categorical.py @@ -26,7 +26,7 @@ class MultinoulliDistribution(BaseDistribution): labels : list of str The labels for each category in the distribution, representing the possible outcomes. - probs : list of int + probs : list of float The probabilities or frequencies of each category. These will be normalized internally. diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index 22645fdd..f8fe29a1 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -118,7 +118,7 @@ def fit_dataframe( # noqa: PLR0912 if dist_providers is not None: meta_config.dist_providers = dist_providers # type: ignore if privacy is not None: - meta_config.defaults["privacy"] = privacy # type: ignore + meta_config.defaults.privacy = privacy # type: ignore if df is not None and not isinstance(df, pl.DataFrame): if isinstance(df, (str, pathlib.Path)): From c423382fa14749baebfb115a64023151d562fea5 Mon Sep 17 00:00:00 2001 From: qubixes <44498096+qubixes@users.noreply.github.com> Date: Thu, 21 Nov 2024 09:27:44 +0100 Subject: [PATCH 16/17] Remove stray print statement and add .toml general comment. (#343) --- metasyn/metaframe.py | 22 ++++++++++++++++------ metasyn/privacy.py | 1 + 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/metasyn/metaframe.py b/metasyn/metaframe.py index f8fe29a1..c46681bb 100644 --- a/metasyn/metaframe.py +++ b/metasyn/metaframe.py @@ -371,7 +371,18 @@ def save_toml(self, fp: Optional[Union[pathlib.Path, str]], doc = tomlkit.loads(tomlkit.dumps(self_dict)) doc["n_rows"].comment("Number of rows") - doc["n_columns"].comment("Number of columns") + doc["n_columns"].comment("""Number of columns + +# This is a metadata file with (limited) statistical information about each column separately in +# a dataset. No information about correlations or other relationships between columns is included. +# This file can be used to generate privacy-conscious synthetic data, which consequently has zero +# expected correlations and relationships between columns. +# For each column, the statistics can be either manually specified or estimated from real data. +# This information, including how the estimation was done, is shown in the metadata below. +# +# For more information, see https://github.com/sodascience/metasyn +""" + ) for i in range(self.n_columns): var = self.meta_vars[i] doc["vars"][i].comment(f"Metadata for column with name {var.name}") @@ -388,10 +399,11 @@ def save_toml(self, fp: Optional[Union[pathlib.Path, str]], and np.all(var.distribution.labels == var.distribution.default_distribution().labels) ) - print(var.name, multi_default) if "parameters" in var.creation_method: - parameter_comments.append(f"The above parameters for column '{var.name}' were " - "manually set by the user, no data was (directly) used.") + parameters = ", ".join(var.creation_method["parameters"]) + parameter_comments.append( + f"The parameters {parameters} for column '{var.name}' were " + "manually set by the user, no data was (directly) used.") elif (var.distribution.matches_name("multinoulli") and multi_default): parameter_comments.append("This mulinoulli distribution is the default one, no data" " was used.") @@ -401,8 +413,6 @@ def save_toml(self, fp: Optional[Union[pathlib.Path, str]], if (var.distribution.matches_name("multinoulli") and not multi_default): counts = (var.distribution.probs*(1-var.prop_missing)*self.n_rows).round() parameter_comments.append(f"Counts: {counts.astype(int)}\n") - # doc["vars"][i]["distribution"].add(tomlkit.comment( - # f"Counts: {counts.astype(int)}\n")) par_comment = "\n# ".join(parameter_comments) + "\n\n" doc["vars"][i]["distribution"]["parameters"].add(tomlkit.comment(par_comment)) if fp is None: diff --git a/metasyn/privacy.py b/metasyn/privacy.py index e1b1975f..52db2cb3 100644 --- a/metasyn/privacy.py +++ b/metasyn/privacy.py @@ -65,6 +65,7 @@ def comment(self, var): # noqa """ return "Above are the parameters for the column {var.name}" + class BasicPrivacy(BasePrivacy): """Class representing no privacy level. From 57f5bc1298b02e85adafc7c733ba2df93f24b0c4 Mon Sep 17 00:00:00 2001 From: Erik-Jan van Kesteren Date: Mon, 25 Nov 2024 11:36:59 +0100 Subject: [PATCH 17/17] Bump version to 1.1.0 --- docs/source/cli.rst | 8 ++++---- docs/source/conf.py | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/cli.rst b/docs/source/cli.rst index 01f169f3..f5ac7a48 100644 --- a/docs/source/cli.rst +++ b/docs/source/cli.rst @@ -58,13 +58,13 @@ Here's how you can use Docker to access Metasyn's CLI: The ``metasyn`` CLI should now be up and running within the Docker container and ready for use! .. note:: - You can also specify which ``metasyn`` version to use in docker, by adding a tag to the docker image. For example, to use version 1.0.3, you can use the following command: + You can also specify which ``metasyn`` version to use in docker, by adding a tag to the docker image. For example, to use version 1.1.0, you can use the following command: .. tab:: Installing a specific version .. code-block:: console - docker pull sodateam/metasyn:v1.0.3 + docker pull sodateam/metasyn:v1.1.0 .. tab:: Using a command on a specific version @@ -72,13 +72,13 @@ The ``metasyn`` CLI should now be up and running within the Docker container and .. code-block:: console - docker run -v %cd%:/wd sodateam/metasyn:v1.0.3 --help + docker run -v %cd%:/wd sodateam/metasyn:v1.1.0 --help .. tab:: Unix or MacOS: .. code-block:: console - docker run -v $(pwd):/wd sodateam/metasyn:v1.0.3 --help + docker run -v $(pwd):/wd sodateam/metasyn:v1.1.0 --help Creating generative metadata diff --git a/docs/source/conf.py b/docs/source/conf.py index fbb26e8b..e31bccb2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -25,7 +25,7 @@ # The full version, including alpha/beta/rc tags -release = '1.0.3' +release = '1.1.0' # -- General configuration ---------------------------------------------------

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