From 18ba895f40a57cd9c8fbf530778ff5a39b27bdae Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Wed, 17 Jun 2026 16:08:13 +0200 Subject: [PATCH 01/28] Adding extensions for generating md version of docs --- doc/Makefile | 6 +- doc/conf.py | 36 +- pixi.lock | 50996 ++++++++++++++++++++++++----------------------- pyproject.toml | 2 + 4 files changed, 25662 insertions(+), 25378 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index 695de599b..435d02ed9 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -33,17 +33,17 @@ html: @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." html-noplot: - SKB_TABLE_REPORT_VERBOSITY=0 $(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D markdown_uri_doc_suffix="html.md" -D llms_txt_enabled=1 -D llms_txt_full_build=0 -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + SKB_TABLE_REPORT_VERBOSITY=0 $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Linkcheck finished. Results are in $(BUILDDIR)/linkcheck." linkcheck-noplot: - $(SPHINXBUILD) -D plot_gallery=0 -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck-noplot + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D plot_gallery=0 -D llms_txt_enabled=0 -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck-noplot @echo @echo "Linkcheck (no plot) finished. Results are in $(BUILDDIR)/linkcheck-noplot." diff --git a/doc/conf.py b/doc/conf.py index aa18871ad..a0f1a8319 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -24,6 +24,13 @@ import jinja2 +# Allow skipping jupyterlite to speed up builds (e.g. html-noplot) +_SKIP_JUPYTERLITE = os.environ.get("SKIP_JUPYTERLITE", "").strip() in ( + "1", + "true", + "yes", +) + # Generate the table report html file for the homepage sys.path.append(os.path.relpath(".")) from data_ops_report import create_data_ops_report @@ -76,6 +83,8 @@ "sphinx_copybutton", "sphinx_gallery.gen_gallery", "autoshortsummary", + "sphinx_llm.txt", + "sphinx_markdown_builder", ] try: @@ -85,18 +94,21 @@ except ImportError: print("ERROR: sphinxext.opengraph import failed") -try: - import jupyterlite_sphinx # noqa: F401 - - extensions.append("jupyterlite_sphinx") - with_jupyterlite = True -except ImportError: - # In some cases we don't want to require jupyterlite_sphinx to be installed, - # e.g. the doc-min-dependencies build - warnings.warn( - "jupyterlite_sphinx is not installed, you need to install it " - "if you want JupyterLite links to appear in each example" - ) +if not _SKIP_JUPYTERLITE: + try: + import jupyterlite_sphinx # noqa: F401 + + extensions.append("jupyterlite_sphinx") + with_jupyterlite = True + except ImportError: + # In some cases we don't want to require jupyterlite_sphinx to be installed, + # e.g. the doc-min-dependencies build + warnings.warn( + "jupyterlite_sphinx is not installed, you need to install it " + "if you want JupyterLite links to appear in each example" + ) + with_jupyterlite = False +else: with_jupyterlite = False import sphinx_autosummary_accessors diff --git a/pixi.lock b/pixi.lock index efd60a25b..3bd6d040d 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,9 +1,4 @@ -version: 7 -platforms: -- name: linux-64 -- name: osx-64 -- name: osx-arm64 -- name: win-64 +version: 6 environments: check-pyi-diff: channels: @@ -11,24 +6,39 @@ environments: - url: https://conda.anaconda.org/pytorch/ indexes: - https://pypi.org/simple + options: + pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.16.1-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.6.0-py314h680f03e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - 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- matplotlib ; extra == 'all' - requires_python: '>=3.12' + size: 388453 + timestamp: 1764777142545 diff --git a/pyproject.toml b/pyproject.toml index 38991146f..6073ad155 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -124,6 +124,8 @@ sphinx-gallery = "*" sphinxext-opengraph = "*" sphinx-autosummary-accessors = ">=2025.3.1,<2026" sphinx-sitemap = "*" +sphinx-llm = "*" +sphinx-markdown-builder = "*" statsmodels = "*" optuna = "*" skorch = "*" From d022a01c0b4649dc5ad58c2a942d5a387190166d Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 11:02:35 +0200 Subject: [PATCH 02/28] changing llms library --- doc/Makefile | 18 +- doc/conf.py | 6 +- pixi.lock | 1325 +++++++++++++++++++----------------------------- pyproject.toml | 4 +- 4 files changed, 550 insertions(+), 803 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index 435d02ed9..a24f9ec45 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -29,11 +29,17 @@ html: rm -rf $(BUILDDIR)/html/_images #rm -rf _build/doctrees/ SKB_TABLE_REPORT_VERBOSITY=0 $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + # Build markdown sources so llms.txt links point to .md files + SKB_TABLE_REPORT_VERBOSITY=0 $(SPHINXBUILD) -b markdown $(ALLSPHINXOPTS) $(BUILDDIR)/markdown + cp -r $(BUILDDIR)/markdown/. $(BUILDDIR)/html/_sources/ @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." html-noplot: - SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D markdown_uri_doc_suffix="html.md" -D llms_txt_enabled=1 -D llms_txt_full_build=0 -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D markdown_uri_doc_suffix="html.md" -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + # Build markdown sources so llms.txt links point to .md files + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D plot_gallery=0 -b markdown $(ALLSPHINXOPTS) $(BUILDDIR)/markdown + cp -r $(BUILDDIR)/markdown/. $(BUILDDIR)/html/_sources/ @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." @@ -47,6 +53,16 @@ linkcheck-noplot: @echo @echo "Linkcheck (no plot) finished. Results are in $(BUILDDIR)/linkcheck-noplot." +markdown: + SKB_TABLE_REPORT_VERBOSITY=0 $(SPHINXBUILD) -b markdown $(ALLSPHINXOPTS) $(BUILDDIR)/markdown + @echo + @echo "Markdown build finished. The markdown files are in $(BUILDDIR)/markdown." + +markdown-noplot: + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D plot_gallery=0 -b markdown $(ALLSPHINXOPTS) $(BUILDDIR)/markdown + @echo + @echo "Markdown build (no plot) finished. The markdown files are in $(BUILDDIR)/markdown." + # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile diff --git a/doc/conf.py b/doc/conf.py index a0f1a8319..5c1b7cba9 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -83,10 +83,14 @@ "sphinx_copybutton", "sphinx_gallery.gen_gallery", "autoshortsummary", - "sphinx_llm.txt", + "sphinx_llms_txt", "sphinx_markdown_builder", ] +# -- sphinx-llms-txt configuration ------------------------------------------- +# Link to Markdown sources in _sources/ (generated by the markdown builder). +llms_txt_uri_template = "{base_url}_sources/{docname}.md" + try: import sphinxext.opengraph # noqa diff --git a/pixi.lock b/pixi.lock index 3bd6d040d..90153a9df 100644 --- a/pixi.lock +++ b/pixi.lock @@ -21,9 +21,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - 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pygments >=2.7.2 - python >=3.10 @@ -30280,11 +30004,10 @@ packages: constrains: - pytest-faulthandler >=2 license: MIT - license_family: MIT purls: - pkg:pypi/pytest?source=compressed-mapping - size: 306602 - timestamp: 1781624895494 + size: 306619 + timestamp: 1781699581695 - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-7.1.0-pyhcf101f3_0.conda sha256: 44e42919397bd00bfaa47358a6ca93d4c21493a8c18600176212ec21a8d25ca5 md5: 67d1790eefa81ed305b89d8e314c7923 @@ -31172,6 +30895,10 @@ packages: - pkg:pypi/torch?source=hash-mapping size: 23594763 timestamp: 1781371137288 +- pypi: https://files.pythonhosted.org/packages/ec/dd/96da98f892250475bdf2328112d7468abdd4acc7b902b6af23f4ed958ea0/pytz-2026.2-py2.py3-none-any.whl + name: pytz + version: '2026.2' + sha256: 04156e608bee23d3792fd45c94ae47fae1036688e75032eea2e3bf0323d1f126 - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2026.2-pyhcf101f3_0.conda sha256: 5020863d629f584b5c057333a67a7aed43e3ed013ba15dd70f353501ccb5aff6 md5: 03cb60f505ad3ada0a95277af5faeb1a @@ -33483,7 +33210,7 @@ packages: - pypi: ./ name: skrub version: 0.10.dev0 - sha256: e1c6cefa7029bd64fb9efeb34dab3aade49c485c9116996107eef0b039d3669e + sha256: 64185a1837b3161ace519feec04b9924231dbb9665081202652d8181b1c8a267 requires_dist: - numpy>=1.23.5 - pandas>=1.5.3 @@ -33813,20 +33540,18 @@ packages: - pkg:pypi/sphinx-last-updated-by-git?source=hash-mapping size: 17546 timestamp: 1750694360605 -- conda: https://conda.anaconda.org/conda-forge/noarch/sphinx-llm-0.4.1-pyhcf101f3_0.conda - sha256: 1335afc012ae55a2205814c86c67f84a5fdaa8bfcff96e3de923c6910df22796 - md5: 47b4654f4d7dabd83341ca3ef7915c9c +- conda: https://conda.anaconda.org/conda-forge/noarch/sphinx-llms-txt-0.7.1-pyhd8ed1ab_0.conda + sha256: d57d93accf0fd40769eff17b84b30b5980b877240a393e3e83495f33eb282784 + md5: 6b170f1a7d5c1729073c354b2d0ac32d depends: - python >=3.10 - - sphinx-markdown-builder >=0.6.8 - - sphinx >=5 - - python - license: BSD-3-Clause - license_family: BSD + - sphinx + license: MIT + license_family: MIT purls: - - pkg:pypi/sphinx-llm?source=hash-mapping - size: 34771 - timestamp: 1775124355749 + - pkg:pypi/sphinx-llms-txt?source=hash-mapping + size: 25685 + timestamp: 1765935234507 - conda: https://conda.anaconda.org/conda-forge/noarch/sphinx-markdown-builder-0.6.10-pyhd8ed1ab_0.conda sha256: 57079789716b56cc198c1f8518d9422c62380ebc7cb77b3170ece04b1d914f17 md5: e804fed0abd0c8df4ff40e3084d724a0 @@ -34703,9 +34428,9 @@ packages: - pkg:pypi/tornado?source=hash-mapping size: 919275 timestamp: 1781006902968 -- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.68.2-pyh8f84b5b_1.conda - sha256: f3ac3dcc43f011835efe2718f5d78981935e8aa1e1d9741b63499dfdd8fa802c - md5: 99ee58c51aae7ee9ab947a0c6ce5a4c7 +- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.68.3-pyh8f84b5b_0.conda + sha256: 613d5cc47571c0b66e31265ff1e0fa5bef0e7b7670f33c67f5a2c587faf6e5d1 + md5: 65094960cb7ed216b6049aab57205902 depends: - python >=3.10 - __unix @@ -34716,11 +34441,11 @@ packages: license: MPL-2.0 and MIT purls: - pkg:pypi/tqdm?source=compressed-mapping - size: 94725 - timestamp: 1781094943144 -- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.68.2-pyha7b4d00_1.conda - sha256: f25ec3f44a3a0243c35baba3dceb1dc0e4a127e5f168ca9fa34708cee821f6b7 - md5: f73d419741d981f9a22939d0cb68bd4a + size: 94965 + timestamp: 1781741268338 +- conda: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.68.3-pyha7b4d00_0.conda + sha256: 41768105c1b4d2cfa3877b902f653275de26bb57d8877904791113329acc1fd4 + md5: 0b814124391b7e176bcc0cce445a3a36 depends: - python >=3.10 - colorama @@ -34732,8 +34457,8 @@ packages: license: MPL-2.0 and MIT purls: - pkg:pypi/tqdm?source=compressed-mapping - size: 94422 - timestamp: 1781095005329 + size: 94630 + timestamp: 1781741323148 - conda: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.15.1-pyhcf101f3_0.conda sha256: b89a823edf524956b94a2a4db974866e4501f05c68976eff458c5dcf07f88431 md5: 37e3be7b6e2977d37b8fa5da229f5dc0 diff --git a/pyproject.toml b/pyproject.toml index 6073ad155..1e8b98a0b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,6 +63,8 @@ dev = [ "sphinx-copybutton", "sphinx-gallery", "sphinxext-opengraph", + "sphinx-llms-txt", + "sphinx-markdown-builder", "sphinx-autosummary-accessors", "statsmodels", @@ -124,7 +126,7 @@ sphinx-gallery = "*" sphinxext-opengraph = "*" sphinx-autosummary-accessors = ">=2025.3.1,<2026" sphinx-sitemap = "*" -sphinx-llm = "*" +sphinx-llms-txt = "*" sphinx-markdown-builder = "*" statsmodels = "*" optuna = "*" From 209a68ca474c3202d4f82b52a86c75f6bd149d2e Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 11:57:39 +0200 Subject: [PATCH 03/28] updating build to ship documentation --- .gitignore | 1 + README.rst | 8 ++++++++ doc/Makefile | 9 +++++++++ pyproject.toml | 3 +++ skrub/__init__.py | 9 ++++++++- 5 files changed, 29 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index ddd8cd7d2..40fbde578 100644 --- a/.gitignore +++ b/.gitignore @@ -71,6 +71,7 @@ doc/sg_execution_times.rst doc/_templates/demo_table_report_generated.html doc/reference/*.rst doc/benchmark_indications.rst +skrub/data/* # Pkl files for benchmarks benchmarks/*.pkl diff --git a/README.rst b/README.rst index f8322a91e..f7d925080 100644 --- a/README.rst +++ b/README.rst @@ -28,6 +28,14 @@ Website: https://skrub-data.org/ See our `examples `_, or check out the `learning materials `_. +The documentation (in Markdown format) is also bundled with the package itself. +After installing, you can find it at: + +.. code-block:: python + + import skrub + print(skrub.__docs_dir__) + Installation ------------ diff --git a/doc/Makefile b/doc/Makefile index a24f9ec45..29dba40d7 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -63,6 +63,15 @@ markdown-noplot: @echo @echo "Markdown build (no plot) finished. The markdown files are in $(BUILDDIR)/markdown." +# Copy the generated markdown docs into the skrub/ package tree so they +# get bundled with the wheel. Run after html, html-noplot, markdown, or +# markdown-noplot. +install-docs: + rm -rf ../skrub/data/docs + mkdir -p ../skrub/data/docs + cp -r $(BUILDDIR)/markdown/. ../skrub/data/docs/ + find ../skrub/data/docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" + # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile diff --git a/pyproject.toml b/pyproject.toml index 1e8b98a0b..2d34d186c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -93,6 +93,9 @@ Issues = "https://github.com/skrub-data/skrub/issues" [tool.setuptools] packages = ["skrub"] +[tool.setuptools.package-data] +skrub = ["data/docs/**/*.md"] + [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] platforms = ["linux-64", "osx-arm64", "osx-64", "win-64"] diff --git a/skrub/__init__.py b/skrub/__init__.py index 451cd4eab..92aad1b1c 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -1,9 +1,16 @@ """ skrub: Prepping tables for machine learning. + +The Markdown documentation is bundled with the package and can be accessed +via ``skrub.__docs_dir__``. """ from pathlib import Path as _Path +#: Path to the Markdown documentation bundled with the package. +#: Use ``skrub.__docs_dir__`` to access it programmatically. +__docs_dir__ = _Path(__file__).parent / "data" / "docs" + from . import core, selectors from ._agg_joiner import AggJoiner, AggTarget from ._apply_to_cols import ApplyToCols @@ -80,7 +87,6 @@ "DropSimilar", "DropUninformative", "deduplicate", - "deduplicate", "ToCategorical", "to_datetime", "AggJoiner", @@ -108,4 +114,5 @@ "ApplyToCols", "ToFloat", "core", + "__docs_dir__", ] From b435e398cb318e38579f344f6d661269d84e2ff4 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 13:06:27 +0200 Subject: [PATCH 04/28] changelog --- CHANGES.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGES.rst b/CHANGES.rst index f6c5e1f50..e2bac7f8b 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -62,6 +62,9 @@ Changes :pr:`2048` by :user:`Riccardo Cappuzzo `. - The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1. :pr:`2159` by :user:`Riccardo Cappuzzo `. +- The package wheel has been updated so that it includes the User Guide and examples + in Markdown format. + :pr:`2173` by :user:`Riccardo Cappuzzo `. Bugfixes -------- From 017233416349cd35adf4e1bc69cbff0760d168a8 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 13:14:10 +0200 Subject: [PATCH 05/28] avoiding duplication, renaming folder --- .gitignore | 2 +- doc/Makefile | 12 ++++++++---- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index 40fbde578..0cd6d2414 100644 --- a/.gitignore +++ b/.gitignore @@ -71,7 +71,7 @@ doc/sg_execution_times.rst doc/_templates/demo_table_report_generated.html doc/reference/*.rst doc/benchmark_indications.rst -skrub/data/* +skrub/_docs/* # Pkl files for benchmarks benchmarks/*.pkl diff --git a/doc/Makefile b/doc/Makefile index 29dba40d7..8503631d6 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -67,10 +67,14 @@ markdown-noplot: # get bundled with the wheel. Run after html, html-noplot, markdown, or # markdown-noplot. install-docs: - rm -rf ../skrub/data/docs - mkdir -p ../skrub/data/docs - cp -r $(BUILDDIR)/markdown/. ../skrub/data/docs/ - find ../skrub/data/docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" + rm -rf ../skrub/_docs + mkdir -p ../skrub/_docs + cp -r $(BUILDDIR)/markdown/auto_examples/. ../skrub/_docs/ + cp -r $(BUILDDIR)/markdown/auto_tutorials/. ../skrub/_docs/ + cp -r $(BUILDDIR)/markdown/guides/. ../skrub/_docs/ + cp -r $(BUILDDIR)/markdown/modules/. ../skrub/_docs/ + cp -r $(BUILDDIR)/markdown/*.md ../skrub/_docs/ + find ../skrub/_docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). From b24ce6a09f710ad12d31cab296bb7b7aa26ae447 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 13:14:30 +0200 Subject: [PATCH 06/28] [doc-build] --- skrub/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skrub/__init__.py b/skrub/__init__.py index 92aad1b1c..ba931f029 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -9,7 +9,7 @@ #: Path to the Markdown documentation bundled with the package. #: Use ``skrub.__docs_dir__`` to access it programmatically. -__docs_dir__ = _Path(__file__).parent / "data" / "docs" +__docs_dir__ = _Path(__file__).parent / "_docs" from . import core, selectors from ._agg_joiner import AggJoiner, AggTarget From 1b90ce7a1e4bc4c2ab8551a1cb4ed890bf6e39b4 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 13:16:09 +0200 Subject: [PATCH 07/28] pyproject --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2d34d186c..f297108e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["data/docs/**/*.md"] +skrub = ["data/_docs/**/*.md"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] From 15dcc08817fb622e82bec9f8ff1193e8d7a355ba Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 14:17:43 +0200 Subject: [PATCH 08/28] cleanup of doc install --- doc/Makefile | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index 8503631d6..e92f037f2 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -69,11 +69,15 @@ markdown-noplot: install-docs: rm -rf ../skrub/_docs mkdir -p ../skrub/_docs - cp -r $(BUILDDIR)/markdown/auto_examples/. ../skrub/_docs/ - cp -r $(BUILDDIR)/markdown/auto_tutorials/. ../skrub/_docs/ - cp -r $(BUILDDIR)/markdown/guides/. ../skrub/_docs/ - cp -r $(BUILDDIR)/markdown/modules/. ../skrub/_docs/ + mkdir -p ../skrub/_docs/examples + mkdir -p ../skrub/_docs/tutorials + mkdir -p ../skrub/_docs/guides + cp -r ../examples/* ../skrub/_docs/examples/ + cp -r ../doc/tutorials/* ../skrub/_docs/tutorials/ + cp -r $(BUILDDIR)/markdown/guides/* ../skrub/_docs/guides + cp -r $(BUILDDIR)/markdown/modules/* ../skrub/_docs/guides cp -r $(BUILDDIR)/markdown/*.md ../skrub/_docs/ + cp -r $(BUILDDIR)/html/llms.txt ../skrub/_docs/ find ../skrub/_docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" # Catch-all target: route all unknown targets to Sphinx using the new From 8d15e9d1d0b65ec4436eda027b0b84ead5a55b46 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 14:34:31 +0200 Subject: [PATCH 09/28] adding manifest file to clean up wheel --- MANIFEST.in | 6 ++++++ pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000..15494e100 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,6 @@ +prune doc/ +prune examples/ +prune .github/ +prune .binder/ +prune build_tools/ +prune htmlcov/ diff --git a/pyproject.toml b/pyproject.toml index f297108e9..ce5315e8d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["data/_docs/**/*.md"] +skrub = ["_docs/**/*.md"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] From 6ab36e88ae01ba5c503143b7cdd8f340ea11de1a Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 16:40:15 +0200 Subject: [PATCH 10/28] improvements --- doc/Makefile | 2 +- pyproject.toml | 4 ++-- skrub/__init__.py | 8 ++------ 3 files changed, 5 insertions(+), 9 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index e92f037f2..62ab9faa2 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -49,7 +49,7 @@ linkcheck: @echo "Linkcheck finished. Results are in $(BUILDDIR)/linkcheck." linkcheck-noplot: - SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D plot_gallery=0 -D llms_txt_enabled=0 -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck-noplot + SKB_TABLE_REPORT_VERBOSITY=0 SKIP_JUPYTERLITE=1 $(SPHINXBUILD) -D plot_gallery=0 -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck-noplot @echo @echo "Linkcheck (no plot) finished. Results are in $(BUILDDIR)/linkcheck-noplot." diff --git a/pyproject.toml b/pyproject.toml index ce5315e8d..7c36c3d5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -227,8 +227,8 @@ python = "~=3.11.0" python = "~=3.14.0" [tool.pixi.feature.doc.tasks] -build-doc = { cmd = "make html", cwd = "doc" } -build-doc-quick = { cmd = "make html-noplot", cwd = "doc" } +build-doc = { cmd = "make html && make install-docs", cwd = "doc" } +build-doc-quick = { cmd = "make html-noplot && make install-docs", cwd = "doc" } clean-doc = { cmd = "make clean", cwd = "doc" } linkcheck = { cmd = "make linkcheck", cwd = "doc" } linkcheck-quick = { cmd = "make linkcheck-noplot", cwd = "doc" } diff --git a/skrub/__init__.py b/skrub/__init__.py index f88b782fb..f02906e7c 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -6,13 +6,9 @@ data. It helps clean, encode, and transform dataframes into features ready for scikit-learn or other ML frameworks. -Docs: https://skrub-data.org/stable/reference/index.html -User Guide: https://skrub-data.org/stable/documentation.html +Bundled docs: ``skrub.__docs_dir__`` +Online docs: https://skrub-data.org/stable/reference/index.html Source: https://github.com/skrub-data/skrub/ -Examples: https://skrub-data.org/stable/auto_examples/index.html - -The Markdown documentation is bundled with the package and can be accessed -via ``skrub.__docs_dir__``. """ from pathlib import Path as _Path From 76dfc6196688f1a9ab5f5f8427008cf10be8f2bf Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 18 Jun 2026 16:40:27 +0200 Subject: [PATCH 11/28] more references to applytocols/selectors --- .../default_wrangling/apply_to_cols.rst | 29 ++++++++++--------- .../multi_column_operations/selectors.rst | 14 +++++---- skrub/_table_vectorizer.py | 7 +++-- 3 files changed, 27 insertions(+), 23 deletions(-) diff --git a/doc/modules/default_wrangling/apply_to_cols.rst b/doc/modules/default_wrangling/apply_to_cols.rst index c25eeb418..fc9b6e07c 100644 --- a/doc/modules/default_wrangling/apply_to_cols.rst +++ b/doc/modules/default_wrangling/apply_to_cols.rst @@ -2,6 +2,7 @@ .. |ApplyToCols| replace:: :class:`ApplyToCols` .. |TableVectorizer| replace:: :class:`TableVectorizer` +.. |selectors| replace:: :mod:`skrub.selectors` .. |s.string| replace:: :meth:`~skrub.selectors.string` .. |s.numeric| replace:: :meth:`~skrub.selectors.numeric` .. |RejectColumn| replace:: :class:`core.RejectColumn` @@ -14,7 +15,7 @@ .. _user_guide_multiple_columns: -Transforming selected columns with |ApplyToCols| +Transforming only some columns with |ApplyToCols| =========================================================== Very often and for various reasons, transformers must be applied only to some of the @@ -22,22 +23,22 @@ columns in a dataframe. For example, all numeric columns in a dataframe may need to be scaled at the same time, while string columns should be left alone. While the heuristics used by the :class:`TableVectorizer` are usually good enough to apply the proper transformers to different datatypes, using it may not be an -option in all cases. In scikit-learn pipelines, the column selection operation can -be done with the :class:`~sklearn.compose.ColumnTransformer`. +option in all cases. -Skrub provides the |ApplyToCols| transformer to achieve the same results with -a larger degree of control over which columns are being transformed. -|ApplyToCols| maps a transformer to columns in a dataframe, so that all -columns that satisfy a certain condition are transformed, while the others are -left untouched. +|ApplyToCols| (optionally paired with the |selectors|) allows to transform specific +columns with a large degree of control: |ApplyToCols| maps a transformer to columns +in a dataframe, so that all columns that satisfy a certain condition are transformed, +while the others are left untouched. |ApplyToCols| and the |selectors| are similar +to scikit-learn's :class:`~sklearn.compose.ColumnTransformer`. -.. tip:: - If a skrub transformer has a ``cols`` parameter to specify a column list, - that can be a selector as well. Selectors give more control over which columns - are being transformed: they are discussed at length in the - :ref:`selectors user guide`. +Using selectors to choose or exclude columns +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If a skrub transformer has a ``cols`` parameter to specify a column list, +that can be a selector as well. Selectors give more control over which columns +are being transformed: they are discussed at length in the +:ref:`selectors user guide`. |ApplyToCols| can be used to transform a subset of columns in a dataframe, while leaving the non-selected columns unchanged. In this example, we want to apply @@ -110,7 +111,7 @@ id city_Madrid city_Paris city_Rome date_year date_month date_day date_to Note that the column "id" was not encoded and was instead left as-is. -Dealing with columns that cannot be handled by a transformer +Rejecting columns that cannot be handled by a transformer ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |ApplyToCols| can allow the underlying encoder to decide which columns it can be applied to. diff --git a/doc/modules/multi_column_operations/selectors.rst b/doc/modules/multi_column_operations/selectors.rst index 73c0ab987..1e5004d8d 100644 --- a/doc/modules/multi_column_operations/selectors.rst +++ b/doc/modules/multi_column_operations/selectors.rst @@ -108,8 +108,8 @@ name, data type, contents, or according to arbitrary user-provided rules:: * :ref:`user_guide_advanced_selectors` -Combining selectors -------------------- +Selectors can be combined with the set operators +------------------------------------------------ The available operators are ``|``, ``&``, ``-``, ``^`` with the meaning of usual python sets, and ``~`` to invert a selection: @@ -146,8 +146,8 @@ following selector won't compute the cardinality of non-categorical columns: (categorical() & cardinality_below(10)) .. _user_guide_selectors_expand: -Visualizing a selector ----------------------- +Using selectors with dataframe libraries +---------------------------------------- All selectors have the :meth:`expand` method, which allows dataframe manipulation outside of a skrub workflow: applying it to any dataframe will return the list @@ -180,8 +180,10 @@ The :meth:`expand_index` method also exists: rather than returning a list of col Using selectors with other skrub transformers ------------------------------------------------- -Skrub transformers are designed to be used in conjunction with other transformers -that operate on columns to improve their versatility. +Skrub selectors are designed to be used in conjunction with :class:`~skrub.ApplyToCols`, +:class:`skrub.SelectCols`, and :class:`skrub.DropCols`, as well as +:func:`~skrub.DataOp.skb.apply` to improve their versatility in how they modify +columns. For example, it is possible to drop columns that have more unique values than a certain amount by combining :func:`~skrub.selectors.cardinality_below` with diff --git a/skrub/_table_vectorizer.py b/skrub/_table_vectorizer.py index 3a096a385..7bfbf72bf 100644 --- a/skrub/_table_vectorizer.py +++ b/skrub/_table_vectorizer.py @@ -590,9 +590,10 @@ class TableVectorizer(TransformerMixin, SkrubBaseEstimator): specified transformer. This disables any preprocessing usually done by the TableVectorizer; the columns are passed to the transformer without any modification. A column is not allowed to appear twice in - ``specific_transformers``. Using ``specific_transformers`` provides - similar functionality to what is offered by scikit-learn's - :class:`~sklearn.compose.ColumnTransformer`. + ``specific_transformers``. + Consider using :class:`~skrub.ApplyToCols` to apply a transformer to multiple + columns at once, or the :ref:`skrub Data Ops ` for + more complex pre-processing. drop_null_fraction : float or None, default=1.0 Fraction of null above which the column is dropped. If `drop_null_fraction` is From 8f4e62d1d3053de4dffbd58aab685d1dc1709dcf Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Fri, 19 Jun 2026 16:43:28 +0200 Subject: [PATCH 12/28] clean up build --- MANIFEST.in | 6 ------ pyproject.toml | 2 +- 2 files changed, 1 insertion(+), 7 deletions(-) delete mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 15494e100..000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,6 +0,0 @@ -prune doc/ -prune examples/ -prune .github/ -prune .binder/ -prune build_tools/ -prune htmlcov/ diff --git a/pyproject.toml b/pyproject.toml index 7c36c3d5a..06b9e2c1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["_docs/**/*.md"] +skrub = ["_docs/**/*.md", "_docs/**/*.py"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] From 4e577f9e266df8b30aa5744cad5f5f200b839e05 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 13:07:06 +0200 Subject: [PATCH 13/28] updating build process --- MANIFEST.in | 12 ++++++++++++ README.rst | 3 +-- pyproject.toml | 8 +++++++- setup.py | 27 +++++++++++++++++++++++++++ 4 files changed, 47 insertions(+), 3 deletions(-) create mode 100644 MANIFEST.in create mode 100644 setup.py diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000..a2a10af36 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,12 @@ +prune .binder +prune .circleci +prune .github +prune build_tools +prune doc +prune examples + +exclude .coveragerc +exclude .git-blame-ignore-revs +exclude .pre-commit-config.yaml +exclude codecov.yml +exclude pixi.lock diff --git a/README.rst b/README.rst index f7d925080..794b32baf 100644 --- a/README.rst +++ b/README.rst @@ -17,8 +17,7 @@ skrub .. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg -**skrub** (formerly *dirty_cat*) is a Python -library that facilitates machine learning with dataframes. +**skrub** is a Python library that facilitates machine learning with dataframes. If you like the package, spread the word and ⭐ this repository! You can also join the `Discord server `_. diff --git a/pyproject.toml b/pyproject.toml index 06b9e2c1f..116414403 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,13 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["_docs/**/*.md", "_docs/**/*.py"] +skrub = [ + "_docs/**/*.md", + "_docs/**/*.py", + "_docs/**/*.css", + "_docs/**/*.js", + "_docs/**/*.txt", +] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] diff --git a/setup.py b/setup.py new file mode 100644 index 000000000..e1bc27c5e --- /dev/null +++ b/setup.py @@ -0,0 +1,27 @@ +"""Custom build step: copy doc/_build/markdown → skrub/_docs before packaging.""" + +import shutil +from pathlib import Path + +from setuptools import setup +from setuptools.command.build_py import build_py + + +class BuildPyWithDocs(build_py): + """Extend build_py to bundle the pre-built markdown documentation.""" + + def run(self): + self._copy_markdown_docs() + super().run() + + def _copy_markdown_docs(self): + source = Path("doc/_build/markdown") + dest = Path("skrub/_docs") + if not source.is_dir(): + return + if dest.is_dir(): + shutil.rmtree(dest) + shutil.copytree(source, dest) + + +setup(cmdclass={"build_py": BuildPyWithDocs}) From 95e922f56b7c7c8ed0aae3bf113219455d72c769 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 13:17:28 +0200 Subject: [PATCH 14/28] removing setup --- setup.py | 27 --------------------------- 1 file changed, 27 deletions(-) delete mode 100644 setup.py diff --git a/setup.py b/setup.py deleted file mode 100644 index e1bc27c5e..000000000 --- a/setup.py +++ /dev/null @@ -1,27 +0,0 @@ -"""Custom build step: copy doc/_build/markdown → skrub/_docs before packaging.""" - -import shutil -from pathlib import Path - -from setuptools import setup -from setuptools.command.build_py import build_py - - -class BuildPyWithDocs(build_py): - """Extend build_py to bundle the pre-built markdown documentation.""" - - def run(self): - self._copy_markdown_docs() - super().run() - - def _copy_markdown_docs(self): - source = Path("doc/_build/markdown") - dest = Path("skrub/_docs") - if not source.is_dir(): - return - if dest.is_dir(): - shutil.rmtree(dest) - shutil.copytree(source, dest) - - -setup(cmdclass={"build_py": BuildPyWithDocs}) From 1f5d2a55f421aa42bb442b2294323b9fa3a05087 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 13:20:35 +0200 Subject: [PATCH 15/28] changelog --- CHANGES.rst | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index e2bac7f8b..0ba11a7bf 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -62,8 +62,9 @@ Changes :pr:`2048` by :user:`Riccardo Cappuzzo `. - The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1. :pr:`2159` by :user:`Riccardo Cappuzzo `. -- The package wheel has been updated so that it includes the User Guide and examples - in Markdown format. +- The package build has been updated and improved to reduce its size and include the + user guide and examples with the package, so that it is now possible to access + it directly from the wheel rather than having to rely on the online docs. :pr:`2173` by :user:`Riccardo Cappuzzo `. Bugfixes From 4f732fc98666304303f146df17231780b016b339 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 13:27:49 +0200 Subject: [PATCH 16/28] removing manifest --- MANIFEST.in | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 MANIFEST.in diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index a2a10af36..000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,12 +0,0 @@ -prune .binder -prune .circleci -prune .github -prune build_tools -prune doc -prune examples - -exclude .coveragerc -exclude .git-blame-ignore-revs -exclude .pre-commit-config.yaml -exclude codecov.yml -exclude pixi.lock From f78dac8d64af67b09ce9aeda7d022559a88e5acc Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 15:11:45 +0200 Subject: [PATCH 17/28] avoiding exec --- doc/Makefile | 4 ++-- pyproject.toml | 12 +++++------- skrub/__init__.py | 4 ++++ 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index 62ab9faa2..2b8dfbcd6 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -72,10 +72,10 @@ install-docs: mkdir -p ../skrub/_docs/examples mkdir -p ../skrub/_docs/tutorials mkdir -p ../skrub/_docs/guides - cp -r ../examples/* ../skrub/_docs/examples/ - cp -r ../doc/tutorials/* ../skrub/_docs/tutorials/ cp -r $(BUILDDIR)/markdown/guides/* ../skrub/_docs/guides cp -r $(BUILDDIR)/markdown/modules/* ../skrub/_docs/guides + cp -r $(BUILDDIR)/markdown/auto_examples/* ../skrub/_docs/examples + cp -r $(BUILDDIR)/markdown/auto_tutorials/* ../skrub/_docs/tutorials cp -r $(BUILDDIR)/markdown/*.md ../skrub/_docs/ cp -r $(BUILDDIR)/html/llms.txt ../skrub/_docs/ find ../skrub/_docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" diff --git a/pyproject.toml b/pyproject.toml index 116414403..1b3e75ac9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,13 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = [ - "_docs/**/*.md", - "_docs/**/*.py", - "_docs/**/*.css", - "_docs/**/*.js", - "_docs/**/*.txt", -] +skrub = ["_docs/**/*.md", "_docs/**/*.py", "_docs/**/*.txt"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] @@ -372,6 +366,10 @@ log_cli_level = "INFO" xfail_strict = true addopts = ["--doctest-modules", "--strict-config", "--strict-markers"] doctest_optionflags = "NORMALIZE_WHITESPACE ELLIPSIS" +# Docs include examples in py format, this prevents them from running when pytest +# the tests +norecursedirs = ["skrub/_docs"] + [tool.codespell] # Ref: https://github.com/codespell-project/codespell#using-a-config-file diff --git a/skrub/__init__.py b/skrub/__init__.py index f02906e7c..074101faf 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -7,6 +7,10 @@ ready for scikit-learn or other ML frameworks. Bundled docs: ``skrub.__docs_dir__`` +Bundled getting started: ``skrub.__docs_dir__ / "tutorials"`` +Bundled user guide: ``skrub.__docs_dir__ / "guides"`` +Bundled examples: ``skrub.__docs_dir__ / "examples"`` + Online docs: https://skrub-data.org/stable/reference/index.html Source: https://github.com/skrub-data/skrub/ """ From 3c748c223bf6cb3ca0521731fbad037a586905da Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 15:11:56 +0200 Subject: [PATCH 18/28] improving tv/applytocol docs --- skrub/_apply_to_cols.py | 19 +++++++++++++++++++ skrub/_table_vectorizer.py | 7 ++++--- 2 files changed, 23 insertions(+), 3 deletions(-) diff --git a/skrub/_apply_to_cols.py b/skrub/_apply_to_cols.py index 7168cf70b..64d34e9cf 100644 --- a/skrub/_apply_to_cols.py +++ b/skrub/_apply_to_cols.py @@ -216,6 +216,25 @@ class ApplyToCols(TransformerMixin, SkrubBaseEstimator): skrub.core.RejectColumn: Column 'A' does not have Date or Datetime dtype. Transformer DatetimeEncoder.fit_transform failed on column 'A'. See above for the full traceback. + It is also possible to wrap a :class:`TableVectorizer` or :class:`Cleaner` in + ``ApplyToCols`` to select or exclude columns based on patterns. For example, + to apply a :class:`TableVectorizer` to all columns except those ending with "_id", + we can do: + + >>> import skrub.selectors as s + >>> from skrub import ApplyToCols, TableVectorizer + + >>> df = pd.DataFrame(dict( + ... user_id=["A001", "A002"], + ... age=[25, 30], + ... department=["Engineering", "Sales"], + ... )) + >>> tv = ApplyToCols(TableVectorizer(), cols=~s.glob("*_id")) + >>> tv.fit_transform(df) + user_id age department_Sales + 0 A001 25.0 0.0 + 1 A002 30.0 1.0 + **Accessing fitted transformers** Depending on the transformer, the fitted transformers diff --git a/skrub/_table_vectorizer.py b/skrub/_table_vectorizer.py index 7bfbf72bf..47a5b3553 100644 --- a/skrub/_table_vectorizer.py +++ b/skrub/_table_vectorizer.py @@ -591,9 +591,10 @@ class TableVectorizer(TransformerMixin, SkrubBaseEstimator): the TableVectorizer; the columns are passed to the transformer without any modification. A column is not allowed to appear twice in ``specific_transformers``. - Consider using :class:`~skrub.ApplyToCols` to apply a transformer to multiple - columns at once, or the :ref:`skrub Data Ops ` for - more complex pre-processing. + Consider wrapping the ``TableVectorizer`` in :class:`~skrub.ApplyToCols` + to select or exclude specific columns from the processing. Alternatively, + the :ref:`skrub Data Ops ` allow for more complex + pre-processing. drop_null_fraction : float or None, default=1.0 Fraction of null above which the column is dropped. If `drop_null_fraction` is From d05cb0d855a449b0101e3b5969b3632beecc2676 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Mon, 22 Jun 2026 15:17:16 +0200 Subject: [PATCH 19/28] commenting out a test --- skrub/tests/test_sklearn.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/skrub/tests/test_sklearn.py b/skrub/tests/test_sklearn.py index 257257744..00203a0db 100644 --- a/skrub/tests/test_sklearn.py +++ b/skrub/tests/test_sklearn.py @@ -1,8 +1,7 @@ import numpy as np -import pytest import sklearn from sklearn.metrics.pairwise import linear_kernel, pairwise_distances -from sklearn.utils.estimator_checks import _is_pairwise_metric, parametrize_with_checks +from sklearn.utils.estimator_checks import _is_pairwise_metric from skrub import ( # isort:skip DatetimeEncoder, @@ -100,9 +99,12 @@ def _tested_estimators(): # TODO: remove the skip when the scikit-learn common test will be more lenient towards # the string categorical data: # xref: https://github.com/scikit-learn/scikit-learn/pull/26860 -@pytest.mark.skip( - "Common tests in scikit-learn are not allowing for categorical string data." -) -@parametrize_with_checks(list(_tested_estimators())) -def test_estimators_compatibility_sklearn(estimator, check, request): - check(estimator) +# TODO: this test fails because pytest v10 deprecates generators in parametrize +# and this is a sklearn built-in, so it has to be fixed in scikit-learn before +# we can remove the skip and run this test again. +# @pytest.mark.skip( +# "Common tests in scikit-learn are not allowing for categorical string data." +# ) +# @parametrize_with_checks(list(_tested_estimators())) +# def test_estimators_compatibility_sklearn(estimator, check, request): +# check(estimator) From b8ec156c8d284879cbecb883f9150591c06f482d Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 23 Jun 2026 09:49:58 +0200 Subject: [PATCH 20/28] cleanup --- pyproject.toml | 3 --- 1 file changed, 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 1b3e75ac9..cb1e574ab 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -366,9 +366,6 @@ log_cli_level = "INFO" xfail_strict = true addopts = ["--doctest-modules", "--strict-config", "--strict-markers"] doctest_optionflags = "NORMALIZE_WHITESPACE ELLIPSIS" -# Docs include examples in py format, this prevents them from running when pytest -# the tests -norecursedirs = ["skrub/_docs"] [tool.codespell] From aa749a9f1b4e0e283647f79ee0b788db127902f4 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 23 Jun 2026 11:40:55 +0200 Subject: [PATCH 21/28] lock file --- pixi.lock | 25245 +++++++++------------------------------------------- 1 file changed, 4393 insertions(+), 20852 deletions(-) diff --git a/pixi.lock b/pixi.lock index 57ea17d1f..0c6b7e4e8 100644 --- a/pixi.lock +++ b/pixi.lock @@ -1,4 +1,9 @@ -version: 6 +version: 7 +platforms: +- name: linux-64 +- name: osx-64 +- name: osx-arm64 +- name: win-64 environments: check-pyi-diff: channels: @@ -6,39 +11,24 @@ environments: - url: https://conda.anaconda.org/pytorch/ indexes: - https://pypi.org/simple - options: - pypi-prerelease-mode: if-necessary-or-explicit packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.16.1-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.6.0-py314h680f03e_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda - 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version: 25.0.0.dev169 + version: 25.0.0.dev171 index: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple requires_python: '>=3.10' -- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pyarrow/25.0.0.dev169/pyarrow-25.0.0.dev169-cp314-cp314-manylinux_2_28_x86_64.whl +- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pyarrow/25.0.0.dev171/pyarrow-25.0.0.dev171-cp314-cp314-manylinux_2_28_x86_64.whl name: pyarrow - version: 25.0.0.dev169 + version: 25.0.0.dev171 index: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple requires_python: '>=3.10' -- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pyarrow/25.0.0.dev169/pyarrow-25.0.0.dev169-cp314-cp314-win_amd64.whl +- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pyarrow/25.0.0.dev171/pyarrow-25.0.0.dev171-cp314-cp314-win_amd64.whl name: pyarrow - version: 25.0.0.dev169 + version: 25.0.0.dev171 index: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple requires_python: '>=3.10' - pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scikit-learn/1.10.dev0/scikit_learn-1.10.dev0-cp314-cp314-macosx_10_15_x86_64.whl From 918906d404dcf39826681659492be99426a5d9a0 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 23 Jun 2026 13:54:17 +0200 Subject: [PATCH 22/28] testing build --- pyproject.toml | 2 +- skrub/__init__.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index cb1e574ab..7c6bd1f3a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["_docs/**/*.md", "_docs/**/*.py", "_docs/**/*.txt"] +skrub = ["_docs/**/*.rst", "_docs/**/*.txt"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] diff --git a/skrub/__init__.py b/skrub/__init__.py index 074101faf..1576bcf52 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -7,9 +7,8 @@ ready for scikit-learn or other ML frameworks. Bundled docs: ``skrub.__docs_dir__`` -Bundled getting started: ``skrub.__docs_dir__ / "tutorials"`` -Bundled user guide: ``skrub.__docs_dir__ / "guides"`` -Bundled examples: ``skrub.__docs_dir__ / "examples"`` +Bundled getting started: ``skrub.__docs_dir__ / "auto_tutorials"`` +Bundled examples: ``skrub.__docs_dir__ / "auto_examples"`` Online docs: https://skrub-data.org/stable/reference/index.html Source: https://github.com/skrub-data/skrub/ From ef17076414d122f1731aebfecb90cb88a961a230 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 23 Jun 2026 16:35:11 +0200 Subject: [PATCH 23/28] excluding py files from test collection --- skrub/conftest.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/skrub/conftest.py b/skrub/conftest.py index 4bbfed789..01ff21d64 100644 --- a/skrub/conftest.py +++ b/skrub/conftest.py @@ -34,6 +34,9 @@ def _example_data_dict(): } +collect_ignore_glob = ["_docs/**/*.py"] + + _DATAFRAME_MODULES_INFO = {} _DATAFRAME_MODULES_INFO["pandas-numpy-dtypes"] = SimpleNamespace( **{ From 651965953e0d948f31521ce7a691eea66231a02a Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Tue, 23 Jun 2026 18:18:39 +0200 Subject: [PATCH 24/28] moving doc files to _docs --- .gitignore | 1 - pyproject.toml | 3 +- skrub/_docs/CHANGES.rst | 1632 +++++++++++++++++ skrub/_docs/CONTRIBUTING.rst | 498 +++++ skrub/_docs/RELEASE_PROCESS.rst | 157 ++ skrub/_docs/_templates/base.rst | 37 + skrub/_docs/_templates/data_op_class.rst | 6 + skrub/_docs/_templates/numpydoc_docstring.rst | 16 + skrub/_docs/about.rst | 18 + skrub/_docs/column_level_featurizing.rst | 19 + skrub/_docs/data_ops.rst | 97 + skrub/_docs/default_wrangling.rst | 17 + skrub/_docs/development.rst | 18 + skrub/_docs/documentation.rst | 27 + skrub/_docs/examples/0010_apply_to_cols.py | 174 ++ skrub/_docs/examples/0050_deduplication.py | 164 ++ skrub/_docs/examples/0100_squashing_scaler.py | 204 +++ .../examples/01_encoding/0010_encodings.py | 377 ++++ .../0020_text_with_string_encoders.py | 346 ++++ .../01_encoding/0030_datetime_encoder.py | 355 ++++ .../examples/01_encoding/GALLERY_HEADER.rst | 2 + .../02_data_ops/1120_multiple_tables.py | 246 +++ .../examples/02_data_ops/1130_choices.py | 284 +++ .../02_data_ops/1131_optuna_choices.py | 188 ++ .../examples/02_data_ops/1140_subsampling.py | 108 ++ .../examples/02_data_ops/1150_use_case.py | 170 ++ .../examples/02_data_ops/1160_pytorch.py | 218 +++ .../examples/02_data_ops/GALLERY_HEADER.rst | 4 + .../examples/03_joining/0040_fuzzy_joining.py | 408 +++++ .../03_joining/0060_multiple_key_join.py | 184 ++ .../03_joining/0070_join_aggregation.py | 352 ++++ .../03_joining/0080_interpolation_join.py | 214 +++ .../examples/03_joining/GALLERY_HEADER.rst | 2 + skrub/_docs/examples/GALLERY_HEADER.rst | 2 + skrub/_docs/exploring_a_dataframe.rst | 13 + .../table_report/01_alter_appearance.rst | 25 + .../guides/table_report/02_exporting.rst | 61 + .../03_finding_correlated_columns.rst | 38 + .../guides/table_report/04_custom_filters.rst | 32 + .../utilities/customizing_configuration.rst | 93 + .../deduplicate_categorical_data.rst | 112 ++ .../guides/utilities/fetching_datasets.rst | 46 + skrub/_docs/howto.rst | 26 + skrub/_docs/includes/big_toc_css.rst | 160 ++ skrub/_docs/index.rst | 18 + skrub/_docs/install.rst | 238 +++ skrub/_docs/joining_dataframes.rst | 11 + skrub/_docs/learning_materials.rst | 11 + .../advanced_columnwise_operations.rst | 133 ++ .../feature_engineering_categorical.rst | 132 ++ .../feature_engineering_datetimes.rst | 276 +++ .../feature_engineering_numerical.rst | 106 ++ .../basics/building_data_ops_plan.rst | 94 + .../modules/data_ops/basics/control_flow.rst | 176 ++ .../basics/data_ops_vs_alternatives.rst | 67 + .../data_ops/basics/direct_access_methods.rst | 81 + .../data_ops/basics/using_previews.rst | 96 + .../data_ops/basics/what_are_data_ops.rst | 34 + .../applying_different_transformers.rst | 153 ++ .../ml_pipeline/applying_ml_estimators.rst | 66 + .../ml_pipeline/documenting_data_ops_plan.rst | 40 + .../evaluating_debugging_data_ops.rst | 32 + .../data_ops/ml_pipeline/subsampling_data.rst | 29 + .../using_part_of_data_ops_plan.rst | 80 + .../validation/exporting_data_ops.rst | 66 + .../validation/hyperparameter_tuning.rst | 227 +++ .../validation/nested_cross_validation.rst | 45 + .../nesting_choices_choosing_pipelines.rst | 110 ++ .../validation/tuning_validating_data_ops.rst | 266 +++ .../validation/tuning_with_optuna.rst | 219 +++ .../default_wrangling/apply_to_cols.rst | 160 ++ .../default_wrangling/cleaning_dataframes.rst | 121 ++ .../default_wrangling/table_vectorizer.rst | 152 ++ .../default_wrangling/tabular_pipeline.rst | 146 ++ .../modules/joining_tables/assembling.rst | 61 + .../advanced_selectors.rst | 126 ++ .../drop_uninformative.rst | 107 ++ .../multi_column_operations/selectors.rst | 237 +++ .../type_of_selectors.rst | 98 + .../exploring_dataframes_interactively.rst | 62 + skrub/_docs/multi_column_operations.rst | 17 + skrub/_docs/sg_execution_times.rst | 88 + skrub/_docs/tutorial_example.rst | 239 +++ skrub/_docs/tutorials/0000_getting_started.py | 221 +++ skrub/_docs/tutorials/1110_data_ops_intro.py | 210 +++ skrub/_docs/tutorials/GALLERY_HEADER.txt | 1 + skrub/_docs/vision.rst | 64 + 87 files changed, 12068 insertions(+), 2 deletions(-) create mode 100644 skrub/_docs/CHANGES.rst create mode 100644 skrub/_docs/CONTRIBUTING.rst create mode 100644 skrub/_docs/RELEASE_PROCESS.rst create mode 100644 skrub/_docs/_templates/base.rst create mode 100644 skrub/_docs/_templates/data_op_class.rst create mode 100644 skrub/_docs/_templates/numpydoc_docstring.rst create mode 100644 skrub/_docs/about.rst create mode 100644 skrub/_docs/column_level_featurizing.rst create mode 100644 skrub/_docs/data_ops.rst create mode 100644 skrub/_docs/default_wrangling.rst create mode 100644 skrub/_docs/development.rst create mode 100644 skrub/_docs/documentation.rst create mode 100644 skrub/_docs/examples/0010_apply_to_cols.py create mode 100644 skrub/_docs/examples/0050_deduplication.py create mode 100644 skrub/_docs/examples/0100_squashing_scaler.py create mode 100644 skrub/_docs/examples/01_encoding/0010_encodings.py create mode 100644 skrub/_docs/examples/01_encoding/0020_text_with_string_encoders.py create mode 100644 skrub/_docs/examples/01_encoding/0030_datetime_encoder.py create mode 100644 skrub/_docs/examples/01_encoding/GALLERY_HEADER.rst create mode 100644 skrub/_docs/examples/02_data_ops/1120_multiple_tables.py create mode 100644 skrub/_docs/examples/02_data_ops/1130_choices.py create mode 100644 skrub/_docs/examples/02_data_ops/1131_optuna_choices.py create mode 100644 skrub/_docs/examples/02_data_ops/1140_subsampling.py create mode 100644 skrub/_docs/examples/02_data_ops/1150_use_case.py create mode 100644 skrub/_docs/examples/02_data_ops/1160_pytorch.py create mode 100644 skrub/_docs/examples/02_data_ops/GALLERY_HEADER.rst create mode 100644 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skrub/_docs/modules/multi_column_operations/type_of_selectors.rst create mode 100644 skrub/_docs/modules/tablereport/exploring_dataframes_interactively.rst create mode 100644 skrub/_docs/multi_column_operations.rst create mode 100644 skrub/_docs/sg_execution_times.rst create mode 100644 skrub/_docs/tutorial_example.rst create mode 100644 skrub/_docs/tutorials/0000_getting_started.py create mode 100644 skrub/_docs/tutorials/1110_data_ops_intro.py create mode 100644 skrub/_docs/tutorials/GALLERY_HEADER.txt create mode 100644 skrub/_docs/vision.rst diff --git a/.gitignore b/.gitignore index 0cd6d2414..ddd8cd7d2 100644 --- a/.gitignore +++ b/.gitignore @@ -71,7 +71,6 @@ doc/sg_execution_times.rst doc/_templates/demo_table_report_generated.html doc/reference/*.rst doc/benchmark_indications.rst -skrub/_docs/* # Pkl files for benchmarks benchmarks/*.pkl diff --git a/pyproject.toml b/pyproject.toml index 7c6bd1f3a..903efa466 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,7 +94,7 @@ Issues = "https://github.com/skrub-data/skrub/issues" packages = ["skrub"] [tool.setuptools.package-data] -skrub = ["_docs/**/*.rst", "_docs/**/*.txt"] +skrub = ["_docs/**/*.rst", "_docs/**/*.py"] [tool.pixi.workspace] channels = ["conda-forge", "pytorch"] @@ -285,6 +285,7 @@ exclude = [ "dist", "doc/_build", "doc/auto_examples", + "skrub/_docs/*.py", "build", "pixi.lock", ] diff --git a/skrub/_docs/CHANGES.rst b/skrub/_docs/CHANGES.rst new file mode 100644 index 000000000..0ba11a7bf --- /dev/null +++ b/skrub/_docs/CHANGES.rst @@ -0,0 +1,1632 @@ +.. _changes: + +=============== +Release history +=============== + +.. currentmodule:: skrub + +Ongoing development +=================== + +New Features +------------ +- New methods :meth:`SkrubLearner.get_named_params` and + :meth:`SkrubLearner.set_named_params` allow getting and setting the outcomes for + choices contained in the DataOp, keyed by choice name. It provides a more + robust way of transferring selected hyperparameters from one DataOp to a + different one than :meth:`SkrubLearner.get_params` and + :meth:`SkrubLearner.set_params`. + :pr:`2090` by :user:`Jérôme Dockès `. +- A parameter ``becomes_default`` has been added to :func:`var`. It allows + indicating that the provided preview ``value`` should also be treated as a + default value for this variable in all contexts (for example in a + SkrubLearner's method like ``fit`` or ``predict``). + :pr:`2082` by :user:`Jérôme Dockès `. +- It is now possible to attach new preview values to the variables in a DataOp + with :meth:`DataOp.skb.set_data`. :pr:`2081` by + :user:`Jérôme Dockès `. +- :class:`DataOp` objects have a new attribute :attr:`DataOp.skb.id` which + provides an alternative for referring to a node, in the environment passed to + :meth:`DataOp.skb.eval`, :meth:`SkrubLearner.predict`, etc., or in + :meth:`DataOp.skb.find` or :meth:`SkrubLearner.truncated_after`. :pr:`2062` by + :user:`Jérôme Dockès `. +- The :class:`DropSimilar` transformer has been added, for removing columns in a + dataframe that present high correlation with other columns. :pr:`2023` by + :user:`Eloi Massoulié `. +- :class:`ToFloat32` now allows users to specify ``decimal`` and ``thousand`` + separators to parse numerical columns that use formatting different from the default + formatting used in Python, such as ``1'234,5``. + Additionally, negative numbers indicated with parentheses can be converted to the + regular numeric format (``(432)`` becomes ``-432``). :pr:`1772` by :user:`Gabriela + Gómez Jiménez `. +- :meth:`TableReport.json` now includes histogram data for numeric and datetime + columns (the bin count and edges, and numbers of low and high outliers). Now + ``json()`` contains all the information shown in the report html rendering, + including the plots. :pr:`2164` by :user:`Jérôme Dockès `. + +Changes +------- +- Grouped Examples into subject-specific sections. :pr:`2102` by + :user:`Maureen Githaiga `. +- :meth:`choose_from` now transparently converts `outcomes` to a list when it is + another type of sequence. :pr:`2100` by :user:`aidbar `. +- An unnecessary warning that was raised when passing a numpy array to the + TableVectorizer has been removed. :pr:`1908` by + :user:`Sandrine Henry `. +- Improving the association tab error message when only one column is present + :pr:`2094` by :user:`Alicja Kosak `. +- Added support for numpy arrays in :meth:`DataOp.skb.concat`. + :pr:`2096` by :user:`Ayesha Siddiqua `. +- The :class:`TableReport` can now be exported in markdown format with ``.markdown``. + :pr:`2048` by :user:`Riccardo Cappuzzo `. +- The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1. + :pr:`2159` by :user:`Riccardo Cappuzzo `. +- The package build has been updated and improved to reduce its size and include the + user guide and examples with the package, so that it is now possible to access + it directly from the wheel rather than having to rely on the online docs. + :pr:`2173` by :user:`Riccardo Cappuzzo `. + +Bugfixes +-------- +- A bug in how the :class:`TableVectorizer` and :class:`Cleaner` treated columns + duration columns in pandas and polars has been fixed. Now, both classes convert + durations to the total number of seconds (with fractional part). This is done + by the new transformer :class:`DurationToFloat`. :pr:`2069` by + :user:`Riccardo Cappuzzo `. +- An error that could arise when running ``TableReport`` on dataframes containing + double dollar (``$$``) signs has been fixed. + :pr:`2154` by :user:`Katerina Michenina `, + :user:`CecilyTS `, :user:`Eve Rabin `. + +Deprecations +------------ + +- The parameter ``order_by`` of :class:`TableReport` is deprecated. Passing + ``order_by`` now emits a :class:`DeprecationWarning` + :pr:`2101` by :user:`Heidi Koivisto `. + + +Release 0.9.0 +============= + +New Features +------------ +- It is now possible to pass additional (dynamically computed) arguments to the + scorers used by :class:`DataOp` objects for validation, hyperparameter search + etc. For example, sample weights. This is achieved by passing the scorers and + their arguments to :meth:`DataOp.skb.with_scoring`. :pr:`1995` by + :user:`Jérôme Dockès `. +- The diagrams displayed in notebooks for :class:`SkrubLearner`, + :class:`ParamSearch` and :class:`OptunaParamSearch` have been improved and now + display the :class:`DataOp` they contain. :pr:`2024` by :user:`Jérôme Dockès + `. +- The method :meth:`DataOp.skb.find` can find a node by name (or by a callable + predicate) in a DataOp. The method :meth:`DataOp.skb.find_X_y` finds the nodes + marked with :meth:`DataOp.skb.mark_as_X` and :meth:`DataOp.skb.mark_as_y`, and + the ``cv`` splitter and ``split_kwargs`` passed to + :meth:`DataOp.skb.mark_as_X`, if they exist. :pr:`2041` + by :user:`Jérôme Dockès `. +- :func:`selectors.has_dtype` has been added, allowing users to select columns + by passing the dtype objects they want to match. :pr:`2027` by + :user:`kudos07 `. +- A new dataframe generator, :func:`datasets.toy_cities`, has been added for + use cases on dataframes with variable sizes and variable correlation between + columns. :pr:`2042` by :user:`Eloi Massoulié `. +- A new selector function, :func:`selectors.drop`, has been added to drop columns + from a dataframe using a selector. It mirrors the behavior of :func:`selectors.select`. + :pr:`2108` by :user:`Mary Njoroge `. + +Changes +------- +- :class:`TableReport` now accepts ``plot_distributions`` and + ``compute_associations`` parameters (``True``, ``False``, or ``"auto"``) + to explicitly control whether distribution plots and pairwise associations + are computed. The threshold parameters controlling the maximum number of + columns for which these are computed have been renamed to + ``plots_threshold`` and ``associations_threshold`` for clarity. + :pr:`1907` by :user:`JulietteBgl `. +- The row indices of training and testing samples are now also included in the + dictionaries produced by :meth:`DataOp.skb.iter_cv_splits`. :pr:`2012` by + :user:`Jérôme Dockès `. +- The :class:`Cleaner` now exposes a ``parse_numbers`` boolean parameter to + control whether numeric-looking strings (e.g., ``["1", "2", "3"]``) are parsed + to ``float32``, and a ``cast_to_float`` parameter to downcast numeric + columns to ``float32``. + :pr:`1910` by :user:`Varshith-yadaV `. +- :func:`~datasets.fetch_toxicity` now returns a shuffled version of the dataset by default. + :pr:`1892` by :user:`Riccardo Cappuzzo `. +- Added a ``metric`` parameter to :func:`fuzzy_join` and :class:`Joiner` to configure + the nearest-neighbor distance used for matching. The metric can be any value + supported by :class:`~sklearn.neighbors.NearestNeighbors` (see its docstring). + :pr:`1861` by :user:`Saba Siddique `. +- :class:`ApplyToCols` now accepts an ``exclude_cols`` parameter, making it + possible to transform the columns selected by ``cols`` except for an + explicit subset, mirroring :meth:`DataOp.skb.apply`. + :pr:`2039` by :user:`Saba Siddique `. +- In python versions >= 3.11, :class:`ApplyToCols` now produces better error + tracebacks when the wrapped transformer fails, . :pr:`1979` by :user:`Jérôme + Dockès `. +- The parameter ``how`` of :meth:`DataOp.skb.apply` is replaced by a simpler + Boolean parameter ``no_wrap``. :pr:`2049` by :user:`Jérôme Dockès + `. +- The ``exclude_cols`` of :meth:`DataOp.skb.apply` can now be a DataOp. + :pr:`2050` by :user:`Jérôme Dockès `. +- Skrub estimators now correctly show links to the documentation in the HTML + representation that is generated for notebooks. :pr:`2036` by :user:`Riccardo + Cappuzzo `. + +Bugfixes +-------- +- An error that could arise when calling ``score`` on a ``SkrubLearner`` that + contains an inner transformer that has a ``score`` method has been fixed. + :pr:`2052` by :user:`Jérôme Dockès `. + +Deprecations +------------ +- The parameter ``numeric_dtype`` in the :class:`Cleaner` has been deprecated in + favor of ``cast_to_float`` in :pr:`1910`. +- The parameter ``drop_if_unique`` of :class:`Cleaner` and :class:`DropUninformative` + has been deprecated. :pr:`2040` by :user:`Riccardo Cappuzzo `. +- The parameters ``max_plot_columns`` and ``max_association_columns`` of the + :class:`TableReport` have been deprecated in favor of ``plot_distributions`` + and ``compute_associations``. :pr:`1907`. + +Release 0.8.0 +============= + +New Features +------------ +- The ``eager_data_ops`` :ref:`configuration + ` option has been added. When set to + False, no previews are computed and validation is deferred until the DataOp is + actually used (e.g. with ``.skb.eval()``) rather than as soon as it is + defined. This can make the definition of complex DataOps with many nodes + faster (the overhead it removes typically becomes noticeable only in DataOps + with 50-100 nodes or more). Moreover, the evaluation of large DataOps has also + become faster. :pr:`1890` by :user:`Jérôme Dockès `. +- The reports produced by :meth:`DataOp.skb.full_report` and + :meth:`SkrubLearner.report` now also display the values provided in the + environment. :pr:`1920` by :user:`Jérôme Dockès `. +- :class:`SkrubLearner`, :class:`ParamSearch` and :class:`OptunaParamSearch` expose + some more attributes for inspection by scikit-learn: ``__sklearn_tags__``, + ``classes_``, ``_estimator_type``. :pr:`1931` by :user:`Jérôme Dockès + `. +- It is now possible to pass additional (dynamically computed) arguments to the + cross-validation splitter used by :class:`DataOp` objects for validation, + hyperparameter search etc. For example, the groups for a + :class:`sklearn.model_selection.GroupKFold` can be computed as part of the + DataOp evaluation and used for splitting. This is achieved by passing the + splitter and its arguments to :meth:`DataOp.skb.mark_as_X`. :pr:`1943` by + :user:`Jérôme Dockès `. +- :func:`selectors.has_nulls` now takes a ``proportion`` parameter, which allows + selecting columns that have a fraction of null values above the given threshold. + :pr:`1881` by :user:`Gabriela Gómez Jiménez `. + + +Changes +------- +- Increased the minimum version of polars from 0.20 to 1.5.0. + :pr:`1897` by :user:`Riccardo Cappuzzo `. +- ``ApplyToCols`` and ``ApplyToFrame`` have been merged into a single class, + :class:`ApplyToCols`,that covers the functionality of both the old classes by + detecting automatically whether the provided transformer should be applied + independently on each column, or on all selected columns as a single dataframe. + As a result, ``ApplyToCols`` and ``ApplyToFrame`` have been removed. + :pr:`1913`, :pr:`1919` and :pr:`1962` by :user:`Riccardo Cappuzzo `. +- The dataset fetcher functions now include a "path" field for each table in the dataset. + For example, the dataset "employee_salaries" now has the field ``employee_salaries_path``. + Additionally, datasets that include a single table have the field ``path``. These + fields contain the paths to the datasets stored in the ``skrub_data`` folder. + The default ``skrub_data`` folder can now be set in the skrub configuration and by setting + the ``SKB_DATA_DIRECTORY`` environment variable. The environment variable ``SKRUB_DATA_DIRECTORY`` + is deprecated and will be removed in a future version of skrub. + :pr:`1852` by :user:`Riccardo Cappuzzo`. Examples in the gallery have + been updated accordingly in :pr:`1940` and :pr:`1964` by :user:`MuditAtrey `. +- :class:`~skrub.core.SingleColumnTransformer` and associated exception + :class:`~skrub.core.RejectColumn` (used internally by many skrub estimators) have + been added to the public API, in the newly-created ``skrub.core`` module. + :pr:`1851` by :user:`Eloi Massoulié `. +- Added the strings ``"None"`` and ``"none"`` to the list of null string values in + :class:`Cleaner`. Also, exposed the list of null string values that will be set + to null by the :class:`Cleaner` as the parameter ``null_strings``. + :pr:`1952` and :pr:`1954` by :user:`Lisa McBride `. +- The configuration parameter "use_table_report" has been removed from the skrub + configuration. Use :meth:`patch_display` instead. + :pr:`1973` by :user:`Riccardo Cappuzzo`. +- Updated how the ``column_filters`` parameter of :class:`TableReport` works. + It now accepts a dictionary where the key is the display name for the + dropdown menu, and the value is a filter of the columns that will be displayed. + Accepts either a list of column indices, a list of column names + or an instance of the :class:`Selector`. + :pr:`1976` by :user:`Lisa McBride `. +- The overplotting of the counts atop the vertical histogram bars in the + :class:`TableReport` has been removed due to formatting issues. + :pr:`1984` by :user:`Lisa McBride`. +- The maximum number of associations that can be displayed in the + :class:`TableReport` has been increased to N=1000, and the associations + are now displayed in a scrollable table. + :pr:`1992` by :user:`Lisa McBride`. + +Bug Fixes +-------- +- The :class:`TableVectorizer` now correctly handles the case where one of the + provided encoders is a scikit-learn Pipeline that starts with a skrub + single-column transformer. :pr:`1899` by :user:`Jérôme Dockès ` + and :pr:`1900` by :user:`Jérôme Dockès `. +- Errors raised when a polars LazyFrame is passed where an eager DataFrame is + expected are now clearer. :pr:`1916` by :user:`Jérôme Dockès `. +- :meth:`DataOp.skb.cross_validate` would raise an error when passed + ``return_indices=True``. Now it returns the train and test indices of each + fold in the ``train_indices`` and ``test_indices`` columns of the result + dataframe. :pr:`1953` by :user:`Jérôme Dockès `. +- Polars LazyFrames are no longer collected automatically anywhere in the library; + a ``TypeError`` is now raised instead. + :pr:`1941` by :user:`Mudit Atrey `. + + +Release 0.7.2 +============= + +Changes +------- +- The :class:`StringEncoder` now exposes the ``vocabulary`` parameter from the parent + :class:`TfidfVectorizer`. + :pr:`1819` by :user:`Eloi Massoulié ` +- :func:`compute_ngram_distance` has been renamed to :func:`_compute_ngram_distance` and is now a private function. + :pr:`1838` by :user:`Siddharth Baleja `. + +Bugfixes +-------- +- Fixed some issues related to the release of Pandas 3.0. :pr:`1855` by :user:`Riccardo Cappuzzo `. + +Release 0.7.1 +============= + +New features +------------ +- A new dataset, :func:`fetch_california_housing`, has been added to the + :mod:`skrub.datasets` module. It allows to get a redundancy copy of the scikit-learn + :func:`fetch_california_housing` function. + :pr:`1830` by :user:`Guillaume Lemaitre `. + +Bugfixes +-------- +- :class:`DropCols` and :class:`SelectCols:` attributes were renamed to end + with an underscore, in order to follow a scikit-learn convention which is + used to determine if an estimator is fitted. :pr:`1813` by :user:`Auguste + Baum `. + +Release 0.7.0 +============= + +New features +------------ +- It is now possible to tune the choices in a :class:`DataOp` with `Optuna + `_. See + :ref:`example_optuna_choices` for an example. + :pr:`1661` by :user:`Jérôme Dockès `. +- :meth:`DataOp.skb.apply` now allows passing extra named arguments to the + estimator's methods through the parameters ``fit_kwargs``, ``predict_kwargs`` + etc. :pr:`1642` by :user:`Jérôme Dockès `. +- TableReport now displays the mean statistic for boolean columns. + :pr:`1647` by :user:`Abdelhakim Benechehab `. +- :meth:`DataOp.skb.get_vars` allows inspecting all the variables, or all the + named dataops, in a :class:`DataOp`. This lets us easily know what keys should + be present in the ``environment`` dictionary we pass to + :meth:`DataOp.skb.eval` or to :meth:`SkrubLearner.fit`, + :meth:`SkrubLearner.predict`, etc. + :pr:`1646` by :user:`Jérôme Dockès `. +- :meth:`DataOp.skb.iter_cv_splits` iterates over the training and testing + environments produced by a CV splitter -- similar to + :meth:`DataOp.skb.train_test_split` but for multiple cross-validation splits. + :pr:`1653` by :user:`Jérôme Dockès `. +- :class:`TableReport` now supports ``np.array``. :pr:`1676` by :user:`Nisma Amjad `. +- :meth:`DataOp.skb.full_report` now accepts a new parameter, ``title``, that is displayed + in the html report. + :pr:`1654` by :user:`Marie Sacksick `. +- :class:`TableReport` now includes the ``open_tab`` parameter, which lets the + user select which tab should be opened when the ``TableReport`` is + rendered. :pr:`1737` by :user:`Riccardo Cappuzzo`. +- :class:`selectors.Selector` now has documentation for its :meth:`selectors.Selector.expand` + and :meth:`selectors.Selector.expand_index` methods, with added information and examples + in the user guide, as well as mentions in the corresponding constructor functions. + :pr:`1841` by :user:`Eloi Massoulié`. + +Changes +------- +- The minimum supported version of Python has been increased to 3.10. Additionally, + the minimum supported versions of scikit-learn and requests are 1.4.2 and 2.27.1 + respectively. Support for python 3.14 has been added. + :pr:`1572` by :user:`Riccardo Cappuzzo`. +- The :meth:`DataOp.skb.full_report` method now deletes reports created with + ``output_dir=None`` after 7 days. :pr:`1657` by :user:`Simon Dierickx `. +- The :func:`tabular_pipeline` uses a :class:`SquashingScaler` instead of a + :class:`StandardScaler` for centering and scaling numerical features + when linear models are used. + :pr:`1644` by :user:`Simon Dierickx ` +- The transformer :class:`ToFloat`, previously called ``ToFloat32``, is now public. + :pr:`1687` by :user:`Marie Sacksick `. +- Improved the error message raised when a Polars lazyframe is passed to + :class:`TableReport`, clarifying that ``.collect()`` must be called first. + :pr:`1767` by :user:`Fatima Ben Kadour `. +- Computing the associations in :class:`TableReport` is now deterministic and can + be controlled by the new parameter ``subsampling_seed`` of the global configuration. + :pr:`1775` by :user:`Thomas S. `. +- Added ``cast_to_str`` parameter to :class:`Cleaner` to prevent unintended + conversion of list/object-like columns to strings unless explicitly enabled. + :pr:`1789` by :user:`PilliSiddharth`. + +Bugfixes +-------- +- The :meth:`skrub.cross_validate` function now raises a specific exception if the wrong variable + type is passed. + :pr:`1799` by :user:`Eloi Massoulié` +- Fixed various issues with some transformers by adding ``get_feature_names_out`` + to all single column transformers. + :pr:`1666` by :user:`Riccardo Cappuzzo`. +- Issues occurring when :meth:`DataOp.skb.apply` was passed a DataOp as the + estimator have been fixed in :pr:`1671` by :user:`Jérôme Dockès + `. +- :class:`TableReport` could raise an error while trying to check if Polars + columns with some dtypes (lists, structs) are sorted. It would not indicate + Polars columns sorted in descending order. Fixed in :pr:`1673` by + :user:`Jérôme Dockès `. +- Fixed nightly checks and added support for upcoming library versions, including Pandas + v3.0. :pr:`1664` by :user:`Auguste Baum ` and + :user:`Riccardo Cappuzzo `. +- Fixed the use of :class:`TableReport` and :class:`Cleaner` with Polars dataframes + containing a column with empty string as name. + :pr:`1722` by :user:`Marie Sacksick `. +- Fixed an issue where :class:`TableReport` would fail when computing associations + for Polars dataframes if PyArrow was not installed. + :pr:`1742` by :user:`Riccardo Cappuzzo `. +- Fixed an issue in the Data Ops report generation in cases where the DataOp + contained escape characters or were spanning multiple lines. + :pr:`1764` by :user:`Riccardo Cappuzzo `. +- Added :meth:`get_feature_names_out` to :class:`Cleaner` for consistency with the + :class:`TableVectorizer` and other transformers. :pr:`1762` by + :user:`Riccardo Cappuzzo `. +- Improve error message when :class:`TextEncoder` is used without the optional + transformers dependencies. :pr:`1769` by :user:`Fangxuan Zhou `. +- Accessing ``.skb.applied_estimator`` on a :class:`DataOp` after calling + ``.skb.set_name()``, ``.skb.set_description()``, ``.skb.mark_as_X()`` or + ``.skb.mark_as_y()`` used to raise an error, this has been fixed in :pr:`1782` + by :user:`Jérôme Dockès `. +- Fixed potential issues that could arise in :meth:`ParamSearch.plot_results` + when NaN values were present in the cross-validation results. + :pr:`1800` by :user:`Riccardo Cappuzzo `. + +Release 0.6.2 +============= + +New features +------------ +- The :meth:`DataOp.skb.full_report` now displays the time each node took to + evaluate. :pr:`1596` by :user:`Jérôme Dockès `. + +Changes +------- +- Ken embeddings are now deprecated, the functions :func:`datasets.get_ken_embeddings`, + :func:`datasets.get_ken_table_aliases`, and :func:`datasets.get_ken_types` will be + removed in the next release of skrub. + :pr:`1546` by :user:`Vincent Maladiere `. +- Improved error messages when a DataOp is being sent to dispatched functions. + :pr:`1607` by :user:`Riccardo Cappuzzo`. +- The accepted values for the parameter ``how`` of :meth:`DataOp.skb.apply` have + changed. The new values are ``"auto"`` (unchanged), ``"cols"`` to wrap the + transformer in :class:`ApplyToCols`, ``"frame"`` to wrap the transformer in + :class:`ApplyToFrame`, or ``"no_wrap"`` for no wrapping. The old values are + deprecated and will result in an error in a future release. + :pr:`1628` by :user:`Jérôme Dockès `. +- The parameter ``splitter`` of :meth:`DataOp.skb.train_test_split` has been + renamed ``split_func``. :pr:`1630` by :user:`Jérôme Dockès `. +- KEN embeddings and all the relevant functions have been removed from skrub. + :pr:`1567` by :user:`Riccardo Cappuzzo`. +- The objects ``tabular_learner`` and ``DropIfTooManyNulls`` were removed. Use + :func:`tabular_pipeline` and :class:`DropUninformative` instead. + :pr:`1567` by :user:`Riccardo Cappuzzo`. +- The skrub global configuration now includes a parameter for setting the default + verbosity of the :class:`TableReport`. + :pr:`1567` by :user:`Riccardo Cappuzzo`. + +Bugfixes +-------- + +- Fixed a compatibility bug with Polars 1.32.3 that may cause `ToFloat32` to fail + when applied to categorical columns. :pr:`1570` by :user:`Riccardo Cappuzzo`. +- Fixed the display of DataOp objects in google colab cell outputs (no output + was displayed). :pr:`1590` by :user:`Jérôme Dockès `. +- Fixed an error that occurred when using ``.skb.concat`` with a pandas dataframe + with column names that aren't strings. :pr:`1594` by :user:`Riccardo Cappuzzo`. +- Fixed the range from which :func:`choose_float` and :func:`choose_int` sample + values when ``log=False`` and ``n_steps`` is ``None``. It was between ``low`` + and ``low + high``, now it is between ``low`` and ``high``. :pr:`1603` by + :user:`Jérôme Dockès `. +- DataOp hyperparameter search would raise an error when doing classification + and using the ``scoring`` parameter, when the dataop contained no variables. + Fixed in :pr:`1601` by :user:`Jérôme Dockès `. +- :class:`SkrubLearner` used to do a prediction on the train set during + ``fit()``, this has been fixed. + :pr:`1610` by :user:`Jérôme Dockès `. +- :class:`DataOp` would raise errors when containing subclasses of list, tuple + or dict that cannot be initialized with an instance of the builtin type (such + as classes created by ``collections.namedtuple``), this has been fixed. + DataOps now only recurse into the builtin collections to evaluate their items + (not into their subclasses). If you need the items evaluated (ie if they + contain DataOps or Choices), store them in one of the builtin collections. + :pr:`1612` by :user:`Jérôme Dockès `. +- :meth:`SkrubLearner.report` with ``mode="fit"`` used to display the dataops + themselves, rather than their outputs, in the report. This has been fixed in + :pr:`1623` by :user:`Jérôme Dockès `. +- Fixed a bug that happened when ``get_feature_names_out`` was called on instances + of the :class:`DatetimeEncoder`. :pr:`1622` by :user:`Riccardo Cappuzzo`. + +Release 0.6.1 +=================== + +Bugfixes +-------- + +- ``get_feature_names_out`` now works correctly when used by :class:`GapEncoder`, + :class:`DropCols`, :class:`SelectCols:` from within a scikit-learn ``Pipeline``. In + addition, :class:`DropCols`'s ``get_feature_names_out`` method now returns the + names of the columns that are not dropped, rather than the names of the columns + that are dropped. :pr:`1543` by :user:`Riccardo Cappuzzo`. + + +Release 0.6.0 +============= + +Highlights +---------- +- Major feature! Skrub DataOps are a powerful new way of + combining dataframe transformations over multiple tables, and machine learning + pipelines. DataOps can be combined to form compled data plans, that can be used + to train and tune machine learning models. Then, the DataOps plans can be exported + as ``Learners`` (:class:`skrub.SkrubLearner`), standalone objects that can be + used on new data. More detail about the DataOps can be found in the + :ref:`User guide ` and in the + :ref:`examples `. + +- The :class:`TableReport` has been improved with many new features. Series are + now supported directly. It is now + possible to skip computing column associations and generating plots when the + number of columns in the dataframe exceeds a user-defined threshold. Columns with + high cardinality and sorted columns are now highlighted in the report. + +- :mod:`selectors`, :class:`ApplyToCols` and :class:`ApplyToFrame` are now available, + providing utilities for selecting columns to which a transformer should be applied + in a flexible way. For more details, see the :ref:`User guide ` + and the :ref:`example `. + +- The :class:`SquashingScaler` has been added: it robustly rescales and smoothly + clips numeric columns, enabling more robust handling of numeric columns + with neural networks. See the :ref:`example ` + +New features +------------ + +- The skrub DataOps are new mechanism for building machine-learning + pipelines that handle multiple tables and easily describing their + hyperparameter spaces. Main PR: :pr:`1233` by :user:`Jérôme Dockès `. + Additional work from other contributors can be found + `here `_: + :user:`Vincent Maladiere ` provided very important help by + trying the DataOps on many use-cases and datasets, providing feedback and + suggesting improvements, improving the examples (including creating all the + figures in the examples) and adding jitter to the parallel coordinate plots, + :user:`Riccardo Cappuzzo` experimented with the DataOps, + suggested improvements and improved the examples, :user:`Gaël Varoquaux + ` , :user:`Guillaume Lemaitre `, :user:`Adrin Jalali + `, :user:`Olivier Grisel ` and others participated + through many discussions in defining the requirements and the public API. + See :ref:`the examples ` for + an introduction. + +- The :mod:`selectors` module provides utilities for selecting columns to which + a transformer should be applied in a flexible way. The module was created in + :pr:`895` by :user:`Jérôme Dockès ` and added to the public API + in :pr:`1341` by :user:`Jérôme Dockès `. + +- The :class:`DropUninformative` transformer is now available. This transformer + employs different heuristics to detect columns that are not likely to bring + useful information for training a model. + The current implementation includes detection of columns that contain only a + single value (constant columns), only missing values, or all unique values (such + as IDs). :pr:`1313` by :user:`Riccardo Cappuzzo`. + +- :func:`get_config`, :func:`set_config` and :func:`config_context` are now available + to configure settings for dataframes display and expressions. :func:`patch_display` + and :func:`unpatch_display` are deprecated and will be removed in the next release + of skrub. :pr:`1427` by :user:`Vincent Maladiere `. + The global configuration includes the parameter ``cardinality_threshold`` that + controls the threshold value used to warn user if they have high cardinality + columns in their dataset. :pr:`1498` by :user:`rouk1 `. + Additionally, the parameter ``float_precision`` + controls the number of significant digits displayed for floating-point values + in reports. :pr:`1470` by :user:`George S `. + +- Added the :class:`SquashingScaler`, a transformer that + robustly rescales and smoothly clips numeric columns, + enabling more robust handling of numeric columns + with neural networks. :pr:`1310` by :user:`Vincent Maladiere ` and + :user:`David Holzmüller `. + +- :func:`datasets.toy_order` is now available to create a toy dataframe and + corresponding targets for examples. + :pr:`1485` by :user:`Antoine Canaguier-Durand `. + +- :class:`ApplyToCols` and :class:`ApplyToFrame` are now available to apply transformers + on a set of columns independently and jointly respectively. + :pr:`1478` by :user:`Vincent Maladiere`. + + +Changes +------- +.. warning:: + The default high cardinality encoder for both :class:`TableVectorizer` and + :meth:`tabular_learner` (now :meth:`tabular_pipeline`) has been changed from + :class:`GapEncoder` to :class:`StringEncoder`. :pr:`1354` by + :user:`Riccardo Cappuzzo`. + +- The ``tabular_learner`` function has been deprecated in favor of :func:`tabular_pipeline` to honor + its scikit-learn pipeline cultural heritage, and remove the ambiguity with the data + ops Learner. :pr:`1493` by :user:`Vincent Maladiere `. + +- :class:`StringEncoder` now exposes the ``stop_words`` argument, which is passed to the + underlying vectorizer (:class:`~sklearn.feature_extraction.text.TfidfVectorizer`, + or :class:`~sklearn.feature_extraction.text.HashingVectorizer`). :pr:`1415` by + :user:`Vincent Maladiere `. + +- A new parameter ``max_association_columns`` has been added to the + :class:`TableReport` to skip association computation when the number of columns + exceeds the specified value. :pr:`1304` by :user:`Victoria Shevchenko `. + +- The `packaging` dependency was removed. + :pr:`1307` by :user:`Jovan Stojanovic ` + +- :class:`TextEncoder`, :class:`StringEncoder` and :class:`GapEncoder` now compute the + total standard deviation norm during training, which is a global constant, and + normalize the vector outputs by performing element-wise division on all entries. + :pr:`1274` by :user:`Vincent Maladiere `. + +- The :class:`DropIfTooManyNulls` transformer has been replaced by the + :class:`DropUninformative` transformer and will be removed in a future release. + :pr:`1313` by :user:`Riccardo Cappuzzo` + +- The :func:`concat_horizontal` function was replaced with :func:`concat`. Horizontal or vertical concatenation + is now controlled by the `axis` parameter. :pr:`1334` by :user:`Parasa V Prajwal `. + +- The :class:`TableVectorizer` and :class:`Cleaner` now accept a `datetime_format` + parameter for specifying the format to use when parsing datetime columns. + :pr:`1358` by :user:`Riccardo Cappuzzo`. + +- The :class:`SimpleCleaner` has been removed. use :class:`Cleaner` instead. :pr:`1370` by :user:`Riccardo Cappuzzo`. + +- The periodic encoding for the ``day_in_year`` has been removed from the :class:`DatetimeEncoder` as it was + redundant. The feature itself is still added if the flag is set to ``True``. :pr:`1396` by :user:`Riccardo Cappuzzo`. + +- The naming scheme used for the features generated by :class:`TextEncoder`, :class:`StringEncoder`, :class:`MinHashEncoder`, + :class:`DatetimeEncoder` has been standardized. Now features generated by all encoders have indices in the range + ``[0, n_components-1]``, rather than ``[1, n_components]``. Additionally, columns with empty name are assigned a default + name that depends on the encoder used. :pr:`1405` by :user:`Riccardo Cappuzzo`. + +- The optional dependencies 'dev', 'doc', 'lint' and 'test' have been coalesced into + 'dev'. :pr:`1404` by :user:`Vincent Maladiere `. + +- The :class:`TableReport` now supports Series in addition to Dataframes. :pr:`1420` by :user:`Vitor Pohlenz`. + +- The :class:`Cleaner` now exposes a parameter to convert numeric values to float32. :pr:`1440` by + :user:`Riccardo Cappuzzo`. + +- The :class:`TableReport` now shows if columns are sorted. :pr:`1512` by :user:`Dea María Léon`. + + +Bugfixes +-------- +- Fixed a bug that caused the :class:`StringEncoder` and :class:`TextEncoder` to raise an exception if the + input column was a Categorical datatype. :pr:`1401` by :user:`Riccardo Cappuzzo`. + +Documentation +------------- +A large number of improvements to the examples, docstrings, and the documentation +website have been made. Contributors include :user:`Vincent Maladiere `, +:user:`Riccardo Cappuzzo`, :user:`Jérôme Dockès `, +:user:`Gael Varoquaux `, :user:`Gabriela Gómez Jiménez `, +:user:`Sylvain Combettes `, :user:`Frits Hermans `, +:user:`Vitor Pohlenz `, :user:`Arturo Amor Quiroz `, +:user:`Marie Sacksick `, :user:`Emilien Battel `, +:user:`George El Haber `, :user:`Antoine Canaguier-Durand `, and +:user:`Lionel Kusch `. + + +Release 0.5.4 +============= + +Maintenance +----------- +* Make ``skrub`` compatible with scikit-learn 1.7. + :pr:`1434` by :user:`Vincent Maladiere `. + + +Release 0.5.3 +============= + +Changes +------- + +- The :class:`SimpleCleaner` has been renamed to :class:`Cleaner`. Use of the + name :class:`SimpleCleaner` is deprecated and will result in an error in some + future release of skrub. :pr:`1275` by :user:`Riccardo Cappuzzo`. + +- A new parameter ``max_plot_columns`` has been added to the + :class:`TableReport` and :func:`patch_display` to skip column plots when the + number of columns exceeds the specified value. :pr:`1255` by :user:`Priscilla + Baah`. + + +Release 0.5.2 +============= + +New features +------------ + +- The :class:`TableReport` now switches its visual theme between light and dark according to the user preferences. + :pr:`1201` by :user:`rouk1 `. + +- Adding a new way to control the location of the data directory, using envar ``SKRUB_DATA_DIRECTORY``. + :pr:`1215` by :user:`Thomas S. ` + +- The :class:`DatetimeEncoder` now supports periodic encoding of datetime features + with trigonometric functions and B-splines transformers. + :pr:`1235` by :user:`Riccardo Cappuzzo`. + +- The :class:`TableReport` now also compute Pearson's correlation for numeric values. + :pr:`1203` by :user:`Reshama Shaikh ` and + :user:`Vincent Maladiere `. + +- The :class:`SimpleCleaner` is now available (⚠️ it was renamed to + :class:`Cleaner` in skrub ``0.5.3``.). This transformer is a lightweight + pre-processor that applies some of the transformations applied by the + :class:`TableVectorizer`, with a simpler interface. :pr:`1266` by + :user:`Riccardo Cappuzzo` and :user:`Jerome Dockes ` . + +Changes +------- + +- The estimator returned by :func:`tabular_learner` now uses spline encoding of + datetime features when the supervised learner is not a model based on decision + trees such as random forests or gradient boosting. :pr:`1264` by + :user:`Guillaume Lemaitre `. + +- The "distribution" tab of the ``TableReport`` now stacks cards horizontally to avoid adding + vertical space. + :pr:`1259` by :user:`Gaël Varoquaux ` + +- Progress messages when generating a ``TableReport`` are now written to stderr instead of stdout. + :pr:`1236` by :user:`Priscilla Baah` + +- Optimize the :class:`StringEncoder`: lower memory footprint and faster execution in some cases. + :pr:`1248` by :user:`Gaël Varoquaux ` + +Bug fixes +--------- +- :class:`StringEncoder` now works correctly in presence of null values. + :pr:`1224` by :user:`Jérôme Dockès `. + +- The :meth:`TableVectorizer.get_feature_names_out` method now works when used in a + scikit-learn pipeline by exposing the `input_features` parameter. + :pr:`1258` by :user:`Guillaume Lemaitre `. + + +Release 0.5.1 +============= + +New features +------------ +* The :class:`StringEncoder` encodes strings using tf-idf and truncated SVD + decomposition and provides a cheaper alternative to :class:`GapEncoder`. + :pr:`1159` by :user:`Riccardo Cappuzzo`. + +Changes +------- +* New dataset fetching methods have been added: :func:`fetch_videogame_sales`, + :func:`fetch_bike_sharing`, :func:`fetch_flight_delays`, + :func:`fetch_country_happiness`, and removed :func:`fetch_road_safety`. + :pr:`1218` by :user:`Vincent Maladiere ` + +Bug fixes +--------- + +Maintenance +----------- + +Release 0.4.1 +============= + +Changes +------- + +* :class:`TableReport` has `write_html` method. :pr:`1190` by :user:`Mojdeh Rastgoo`. + +* A new parameter ``verbose`` has been added to the :class:`TableReport` to toggle on or off the + printing of progress information when a report is being generated. + :pr:`1182` by :user:`Priscilla Baah`. + +* A parameter ``verbose`` has been added to the :func:`patch_display` to toggle on or off the + printing of progress information when a table report is being generated. + :pr:`1188` by :user:`Priscilla Baah`. + +* :func:`tabular_learner` accepts the alias ``"regression"`` for the option + ``"regressor"`` and ``"classification"`` for ``"classifier"``. + :pr:`1180` by :user:`Mojdeh Rastgoo `. + +Bug fixes +--------- +* Generating a ``TableReport`` could have an effect on the matplotib + configuration which could cause plots not to display inline in jupyter + notebooks any more. This has been fixed in skrub in :pr:`1172` by + :user:`Jérôme Dockès ` and the matplotlib issue can be tracked + `here `_. + +* The labels on bar plots in the ``TableReport`` for columns of object dtypes + that have a repr spanning multiple lines could be unreadable. This has been + fixed in :pr:`1196` by :user:`Jérôme Dockès `. + +* Improve the performance of :func:`deduplicate` by removing some unnecessary + computations. :pr:`1193` by :user:`Jérôme Dockès `. + +Maintenance +----------- +* Make ``skrub`` compatible with scikit-learn 1.6. + :pr:`1169` by :user:`Guillaume Lemaitre `. + +Release 0.4.0 +============= + +Highlights +---------- +* The :class:`TextEncoder` can extract embeddings from a string column with a deep + learning language model (possibly downloaded from the HuggingFace Hub). + +* Several improvements to the :class:`TableReport` such as better support for + other scripts than the latin alphabet in the bar plot labels, smaller report + sizes, clipping the outliers to better see the details of distributions in + histograms. See the full changelog for details. + +* The :class:`TableVectorizer` can now drop columns that contain a fraction of + null values above a user-chosen threshold. + +New features +------------ +* The :class:`TextEncoder` is now available to encode string columns with + diverse entries. + It allows the representation of table entries as embeddings computed by a deep + learning language model. The weights of this model can be fetched locally + or from the HuggingFace Hub. + :pr:`1077` by :user:`Vincent Maladiere `. + +* The :func:`column_associations` function has been added. It computes a + pairwise measure of statistical dependence between all columns in a dataframe + (the same as shown in the :class:`TableReport`). :pr:`1109` by :user:`Jérôme + Dockès `. + +* The :func:`patch_display` function has been added. It changes the display of + pandas and polars dataframes in jupyter notebooks to replace them with a + :class:`TableReport`. This can be undone with :func:`unpatch_display`. + :pr:`1108` by :user:`Jérôme Dockès ` + +Major changes +------------- +* :class:`AggJoiner`, :class:`AggTarget` and :class:`MultiAggJoiner` now require + the `operations` argument. They do not split columns by type anymore, but + apply `operations` on all selected cols. "median" is now supported, "hist" and + "value_counts" are no longer supported. :pr:`1116` by :user:`Théo Jolivet `. + +* The :class:`AggTarget` no longer supports `y` inputs of type list. :pr:`1116` + by :user:`Théo Jolivet `. + +Minor changes +------------- + +* The column filter selection dropdown in the tablereport is smaller and its + label has been removed to save space. :pr:`1107` by :user:`Jérôme Dockès + `. + +* The TableReport now uses the font size of its parent element when inserted + into another page. This makes it smaller in pages that use a smaller font size + than the browser default such as VSCode in some configurations. It also makes + it easier to control its size when inserting it in a web page by setting the + font size of its parent element. A few other small adjustments have also been + made to make it a bit more compact. :pr:`1098` by :user:`Jérôme Dockès + `. + +* Display of labels in the plots of the TableReport, especially for other + scripts than the latin alphabet, has improved. + + - before, some characters could be missing and replaced by empty boxes. + - before, when the text is truncated, the ellipsis "..." could appear on the + wrong side for right-to-left scripts. + + Moreover, when the text contains line breaks it now appears all on one line. + Note this only affects the labels in the plots; the rest of the report did not + have these problems. + :pr:`1097` by :user:`Jérôme Dockès ` + and :pr:`1138` by :user:`Jérôme Dockès `. + +* In the TableReport it is now possible, before clicking any of the cells, to + reach the dataframe sample table and activate a cell with tab key navigation. + :pr:`1101` by :user:`Jérôme Dockès `. + +* The "Column name" column of the "summary statistics" table in the TableReport + is now always visible when scrolling the table. :pr:`1102` by :user:`Jérôme + Dockès `. + +* Added parameter `drop_null_fraction` to `TableVectorizer` to drop columns based + on whether they contain a fraction of nulls larger than the given threshold. + :pr:`1115` and :pr:`1149` by :user:`Riccardo Cappuzzo `. + +* The :class:`TableReport` now provides more helpful output for columns of dtype + TimeDelta / Duration. :pr:`1152` by :user:`Jérôme Dockès `. + +* The :class:`TableReport` now also reports the number of unique values for + numeric columns. :pr:`1154` by :user:`Jérôme Dockès `. + +* The :class:`TableReport`, when plotting histograms, now detects outliers and + clips the range of data shown in the histogram. This allows seeing more detail + in the shown distribution. :pr:`1157` by :user:`Jérôme Dockès `. + +Bug fixes +--------- + +* The :class:`TableReport` could raise an exception when one of the columns + contained datetimes with time zones and missing values; this has been fixed in + :pr:`1114` by :user:`Jérôme Dockès `. + +* In scikit-learn versions older than 1.4 the :class:`TableVectorizer` could + fail on polars dataframes when used with the default parameters. This has been + fixed in :pr:`1122` by :user:`Jérôme Dockès `. + +* The :class:`TableReport` would raise an exception when the input (pandas) + dataframe contained several columns with the same name. This has been fixed in + :pr:`1125` by :user:`Jérôme Dockès `. + +* The :class:`TableReport` would raise an exception when a column contained + infinite values. This has been fixed in :pr:`1150` by :user:`Jérôme Dockès + ` and :pr:`1151` by Jérôme Dockès. + +Release 0.3.1 +============= + +Minor changes +------------- + +* For tree-based models, :func:`tabular_learner` now adds + `handle_unknown='use_encoded_value'` to the `OrdinalEncoder`, to avoid + errors with new categories in the test set. This is consistent with the + setting of `OneHotEncoder` used by default in the + :class:`TableVectorizer`. :pr:`1078` by :user:`Gaël Varoquaux ` + +* The reports created by :class:`TableReport`, when inserted in an html page (or + displayed in a notebook), now use the same font as the surrounding page. + :pr:`1038` by :user:`Jérôme Dockès `. + +* The content of the dataframe corresponding to the currently selected table + cell in the TableReport can be copied without actually selecting the text (as + in a spreadsheet). + :pr:`1048` by :user:`Jérôme Dockès `. + +* The selection of content displayed in the TableReport's copy-paste boxes has + been removed. Now they always display the value of the selected item. When + copied, the repr of the selected item is copied to the clipboard. + :pr:`1058` by :user:`Jérôme Dockès `. + +* A "stats" panel has been added to the TableReport, showing summary statistics + for all columns (number of missing values, mean, etc. -- similar to + ``pandas.info()`` ) in a table. It can be sorted by each column. + :pr:`1056` and :pr:`1068` by :user:`Jérôme Dockès `. + +* The credit fraud dataset is now available with the + :func:`fetch_credit_fraud function`. + :pr:`1053` by :user:`Vincent Maladiere `. + +* Added zero padding for column names in :class:`MinHashEncoder` to improve column ordering consistency. + :pr:`1069` by :user:`Shreekant Nandiyawar `. + +* The selection in the TableReport's sample table can now be manipulated with + the keyboard. :pr:`1065` by :user:`Jérôme Dockès `. + +* The ``TableReport`` now displays the pandas (multi-)index, and has a better + display & interaction of pandas columns when the columns are a MultiIndex. + :pr:`1083` by :user:`Jérôme Dockès `. + +* It is possible to control the number of rows displayed by the TableReport in + the "sample" tab panel by specifying ``n_rows``. + :pr:`1083` by :user:`Jérôme Dockès `. + +* the `TableReport` used to raise an exception when the dataframe contained + unhashable types such as python lists. This has been fixed in :pr:`1087` by + :user:`Jérôme Dockès `. + +* Display's columns name with the HTML representation of the fitted TableVectorizer. + This has been fixed in :pr:`1093` by :user:`Shreekant Nandiyawar `. + +* AggTarget will now work even when y is a Series and not raise any error. + This has been fixed in :pr:`1094` by :user:`Shreekant Nandiyawar `. + +Release 0.3.0 +============= + +Highlights +---------- +* Polars dataframes are now supported across all ``skrub`` estimators. +* :class:`TableReport` generates an interactive report for a dataframe. This + `page `_ regroups some + precomputed examples. + +Major changes +------------- +* The :class:`InterpolationJoiner` now supports polars dataframes. :pr:`1016` + by :user:`Théo Jolivet `. +* The :class:`TableReport` provides an interactive report on a dataframe's + contents: an overview, summary statistics and plots, statistical associations + between columns. It can be displayed in a jupyter notebook, a browser tab or + saved as a static HTML page. :pr:`984` by :user:`Jérôme Dockès `. + +Minor changes +------------- +* :class:`Joiner` and :func:`fuzzy_join` used to raise an error when columns + with the same name appeared in the main and auxiliary table (after adding the + suffix). This is now allowed and a random string is inserted in the duplicate + column to ensure all names are unique. + :pr:`1014` by :user:`Jérôme Dockès `. + +* :class:`AggJoiner` and :class:`AggTarget` could produce outputs whose column + names varied across calls to `transform` in some cases in the presence of + duplicate column names, now the output names are always the same. + :pr:`1013` by :user:`Jérôme Dockès `. + +* In some cases :class:`AggJoiner` and :class:`AggTarget` inserted a column in + the output named "index" containing the pandas index of the auxiliary table. + This has been corrected. + :pr:`1020` by :user:`Jérôme Dockès `. + +Release 0.2.0 +============= + +Major changes +------------- +* The :class:`Joiner` has been adapted to support polars dataframes. :pr:`945` by :user:`Théo Jolivet `. + +* The :class:`TableVectorizer` now consistently applies the same transformation + across different calls to `transform`. There also have been some breaking + changes to its functionality: (i) all transformations are now applied + independently to each column, i.e. it does not perform multivariate + transformations (ii) in ``specific_transformers`` the same column may not be + used twice (go through 2 different transformers). + :pr:`902` by :user:`Jérôme Dockès `. + +* Some parameters of :class:`TableVectorizer` have been renamed: + `high_cardinality_transformer` → `high_cardinality`, + `low_cardinality_transformer` → `low_cardinality`, + `datetime_transformer` → `datetime`, `numeric_transformer` → `numeric`. + :pr:`947` by :user:`Jérôme Dockès `. + +* The :class:`GapEncoder` and :class:`MinHashEncoder` are now a single-column + transformers: their ``fit``, ``fit_transform`` and ``transform`` methods + accept a single column (a pandas or polars Series). Dataframes and numpy + arrays are not accepted. + :pr:`920` and :pr:`923` by :user:`Jérôme Dockès `. + +* Added the :class:`MultiAggJoiner` that allows to augment a main table with + multiple auxiliary tables. :pr:`876` by :user:`Théo Jolivet `. + +* :class:`AggJoiner` now only accepts a single table as an input, and some of its + parameters were renamed to be consistent with the :class:`MultiAggJoiner`. + It now has a ``key``` parameter that allows to join main and auxiliary tables that share + the same column names. :pr:`876` by :user:`Théo Jolivet `. + +* :func:`tabular_learner` has been added to easily create a supervised + learner that works well on tabular data. :pr:`926` by :user:`Jérôme Dockès + `. + +Minor changes +------------- + +* :class:`GapEncoder` and :class:`MinHashEncoder` used to modify their input + in-place, replacing missing values with a string. They no longer do so. Their + parameter `handle_missing` has been removed; now missing values are always + treated as the empty string. + :pr:`930` by :user:`Jérôme Dockès `. + +* The minimum supported python version is now 3.9 + :pr:`939` by :user:`Jérôme Dockès `. + +* Skrub supports numpy 2. :pr:`946` by :user:`Jérôme Dockès `. + +* :func:`~datasets.fetch_ken_embeddings` now add suffix even with the default + value for the parameter `pca_components`. + :pr:`956` by :user:`Guillaume Lemaitre `. + +* :class:`Joiner` now performs some preprocessing (the same as done by the + :class:`TableVectorizer`, eg trying to parse dates, converting pandas object + columns with mixed types to a single type) on the joining columns before + vectorizing them. :pr:`972` by :user:`Jérôme Dockès `. + +skrub release 0.1.1 +=================== + +This is a bugfix release to adapt to the most recent versions of pandas (2.2) and +scikit-learn (1.5). There are no major changes to the functionality of skrub. + + +skrub release 0.1.0 +=================== + + +Major changes +------------- +* :class:`TargetEncoder` has been removed in favor of + :class:`sklearn.preprocessing.TargetEncoder`, available since scikit-learn 1.3. + +* :class:`Joiner` and :func:`fuzzy_join` support several ways of rescaling + distances; ``match_score`` has been replaced by ``max_dist``; bugs which + prevented the Joiner to consistently vectorize inputs and accept or reject + matches across calls to transform have been fixed. :pr:`821` by :user:`Jérôme + Dockès `. + +* :class:`InterpolationJoiner` was added to join two tables by using + machine-learning to infer the matching rows from the second table. + :pr:`742` by :user:`Jérôme Dockès `. + +* Pipelines including :class:`TableVectorizer` can now be grid-searched, since + we can now call `set_params` on the default transformers of :class:`TableVectorizer`. + :pr:`814` by :user:`Vincent Maladiere ` + +* :func:`to_datetime` is now available to support pandas.to_datetime + over dataframes and 2d arrays. + :pr:`784` by :user:`Vincent Maladiere ` + +* Some parameters of :class:`Joiner` have changed. The goal is to harmonize + parameters across all estimator that perform join(-like) operations, as + discussed in `#751 `_. + :pr:`757` by :user:`Jérôme Dockès `. + +* :func:`dataframe.pd_join`, :func:`dataframe.pd_aggregate`, + :func:`dataframe.pl_join` and :func:`dataframe.pl_aggregate` + are now available in the dataframe submodule. + :pr:`733` by :user:`Vincent Maladiere ` + +* :class:`FeatureAugmenter` is renamed to :class:`Joiner`. + :pr:`674` by :user:`Jovan Stojanovic ` + +* :func:`fuzzy_join` and :class:`FeatureAugmenter` can now join on datetime columns. + :pr:`552` by :user:`Jovan Stojanovic ` + +* :class:`Joiner` now supports joining on multiple column keys. + :pr:`674` by :user:`Jovan Stojanovic ` + +* The signatures of all encoders and functions have been revised to enforce + cleaner calls. This means that some arguments that could previously be passed + positionally now have to be passed as keywords. + :pr:`514` by :user:`Lilian Boulard `. + +* Parallelized the :class:`GapEncoder` column-wise. Parameters `n_jobs` and `verbose` + added to the signature. :pr:`582` by :user:`Lilian Boulard ` + +* Introducing :class:`AggJoiner`, a transformer performing + aggregation on auxiliary tables followed by left-joining on a base table. + :pr:`600` by :user:`Vincent Maladiere `. + +* Introducing :class:`AggTarget`, a transformer performing + aggregation on the target y, followed by left-joining on a base table. + :pr:`600` by :user:`Vincent Maladiere `. + +* Added the :class:`SelectCols` and :class:`DropCols` transformers that allow + selecting a subset of a dataframe's columns inside of a pipeline. :pr:`804` by + :user:`Jérôme Dockès `. + + +Minor changes +------------- +* :class:`DatetimeEncoder` doesn't remove constant features anymore. + It also supports an 'errors' argument to raise or coerce errors during + transform, and a 'add_total_seconds' argument to include the number of + seconds since Epoch. + :pr:`784` by :user:`Vincent Maladiere ` + +* Scaling of ``matching_score`` in :func:`fuzzy_join` is now between 0 and 1; it used to be between 0.5 and 1. Moreover, the division by 0 error that occurred when all rows had a perfect match has been fixed. :pr:`802` by :user:`Jérôme Dockès `. + +* :class:`TableVectorizer` is now able to apply parallelism at the column level rather than the transformer level. This is the default for univariate transformers, like :class:`MinHashEncoder`, and :class:`GapEncoder`. + :pr:`592` by :user:`Leo Grinsztajn ` + +* ``inverse_transform`` in :class:`SimilarityEncoder` now works as expected; it used to raise an exception. :pr:`801` by :user:`Jérôme Dockès `. + +* :class:`TableVectorizer` propagate the `n_jobs` parameter to the underlying + transformers except if the underlying transformer already set explicitly `n_jobs`. + :pr:`761` by :user:`Leo Grinsztajn `, :user:`Guillaume Lemaitre `, + and :user:`Jerome Dockes `. + + +* Parallelized the :func:`deduplicate` function. Parameter `n_jobs` + added to the signature. :pr:`618` by :user:`Jovan Stojanovic ` + and :user:`Lilian Boulard ` + +* Functions :func:`datasets.fetch_ken_embeddings`, :func:`datasets.fetch_ken_table_aliases` + and :func:`datasets.fetch_ken_types` have been renamed. + :pr:`602` by :user:`Jovan Stojanovic ` + +* Make `pyarrow` an optional dependencies to facilitate the integration + with `pyodide`. + :pr:`639` by :user:`Guillaume Lemaitre `. + +* Bumped minimal required Python version to 3.10. :pr:`606` by + :user:`Gael Varoquaux ` + +* Bumped minimal required versions for the dependencies: + - numpy >= 1.23.5 + - scipy >= 1.9.3 + - scikit-learn >= 1.2.1 + - pandas >= 1.5.3 :pr:`613` by :user:`Lilian Boulard ` + +* You can now pass column-specific transformers to :class:`TableVectorizer` + using the `specific_transformers` argument. + :pr:`583` by :user:`Lilian Boulard `. + +* Do not support 1-D array (and pandas Series) in :class:`TableVectorizer`. Pass a + 2-D array (or a pandas DataFrame) with a single column instead. This change is for + compliance with the scikit-learn API. + :pr:`647` by :user:`Guillaume Lemaitre ` + +* Fixes a bug in :class:`TableVectorizer` with `remainder`: it is now cloned if it's + a transformer so that the same instance is not shared between different + transformers. + :pr:`678` by :user:`Guillaume Lemaitre ` + +* :class:`GapEncoder` speedup :pr:`680` by :user:`Leo Grinsztajn ` + + - Improved :class:`GapEncoder`'s early stopping logic. The parameters `tol` and `min_iter` + have been removed. The parameter `max_no_improvement` can now be used to control the + early stopping. + :pr:`663` by :user:`Simona Maggio ` + :pr:`593` by :user:`Lilian Boulard ` + :pr:`681` by :user:`Leo Grinsztajn ` + + - Implementation improvement leading to a ~x5 speedup for each iteration. + + - Better default hyperparameters: `batch_size` now defaults to 1024, and `max_iter_e_steps` + to 1. + +* Removed the `most_frequent` and `k-means` strategies from the :class:`SimilarityEncoder`. + These strategy were used for scalability reasons, but we recommend using the :class:`MinHashEncoder` + or the :class:`GapEncoder` instead. :pr:`596` by :user:`Leo Grinsztajn ` + +* Removed the `similarity` argument from the :class:`SimilarityEncoder` constructor, + as we only support the ngram similarity. :pr:`596` by :user:`Leo Grinsztajn ` + +* Added the `analyzer` parameter to the :class:`SimilarityEncoder` to allow word counts + for similarity measures. :pr:`619` by :user:`Jovan Stojanovic ` + +* skrub now uses modern type hints introduced in PEP 585. + :pr:`609` by :user:`Lilian Boulard ` + +* Some bug fixes for :class:`TableVectorizer` ( :pr:`579`): + + - `check_is_fitted` now looks at `"transformers_"` rather than `"columns_"` + - the default of the `remainder` parameter in the docstring is now `"passthrough"` + instead of `"drop"` to match the implementation. + - uint8 and int8 dtypes are now considered as numeric columns. + +* Removed the leading "<" and trailing ">" symbols from KEN entities + and types. + :pr:`601` by :user:`Jovan Stojanovic ` + +* Add `get_feature_names_out` method to :class:`MinHashEncoder`. + :pr:`616` by :user:`Leo Grinsztajn ` + +* Removed `requests` from the requirements. :pr:`613` by :user:`Lilian Boulard ` + +* :class:`TableVectorizer` now handles mixed types columns without failing + by converting them to string before type inference. + :pr:`623`by :user:`Leo Grinsztajn ` + +* Moved the default storage location of data to the user's home folder. + :pr:`652` by :user:`Felix Lefebvre ` and + :user:`Gael Varoquaux ` + +* Fixed bug when using :class:`TableVectorizer`'s `transform` method on + categorical columns with missing values. + :pr:`644` by :user:`Leo Grinsztajn ` + +* :class:`TableVectorizer` never output a sparse matrix by default. This can be changed by + increasing the `sparse_threshold` parameter. :pr:`646` by :user:`Leo Grinsztajn ` + +* :class:`TableVectorizer` doesn't fail anymore if an inferred type doesn't work during transform. + The new entries not matching the type are replaced by missing values. :pr:`666` by :user:`Leo Grinsztajn ` + +- Dataset fetcher :func:`datasets.fetch_employee_salaries` now has a parameter + `overload_job_titles` to allow overloading the job titles + (`employee_position_title`) with the column `underfilled_job_title`, + which provides some more information about the job title. + :pr:`581` by :user:`Lilian Boulard ` + +* Fix bugs which was triggered when `extract_until` was "year", "month", "microseconds" + or "nanoseconds", and add the option to set it to `None` to only extract `total_time`, + the time from epoch. :class:`DatetimeEncoder`. :pr:`743` by :user:`Leo Grinsztajn ` + +Before skrub: dirty_cat +======================== + +Skrub was born from the `dirty_cat `__ +package. + +Dirty-cat release 0.4.1 +========================== + +Major changes +------------- +* :func:`fuzzy_join` and :class:`FeatureAugmenter` can now join on numeric columns based on the euclidean distance. + :pr:`530` by :user:`Jovan Stojanovic ` + +* :func:`fuzzy_join` and :class:`FeatureAugmenter` can perform many-to-many joins on lists of numeric or string key columns. + :pr:`530` by :user:`Jovan Stojanovic ` + +* :func:`GapEncoder.transform` will not continue fitting of the instance anymore. + It makes functions that depend on it (:func:`~GapEncoder.get_feature_names_out`, + :func:`~GapEncoder.score`, etc.) deterministic once fitted. + :pr:`548` by :user:`Lilian Boulard ` + +* :func:`fuzzy_join` and :class:`FeatureAugmenter` now perform joins on missing values as in `pandas.merge` + but raises a warning. :pr:`522` and :pr:`529` by :user:`Jovan Stojanovic ` + +* Added :func:`get_ken_table_aliases` and :func:`get_ken_types` for exploring + KEN embeddings. :pr:`539` by :user:`Lilian Boulard `. + + +Minor changes +------------- +* Improvement of date column detection and date format inference in :class:`TableVectorizer`. The + format inference now tries to find a format which works for all non-missing values of the column, and only + tries pandas default inference if it fails. + :pr:`543` by :user:`Leo Grinsztajn ` + :pr:`587` by :user:`Leo Grinsztajn ` + + + +Dirty-cat Release 0.4.0 +========================= + +Major changes +------------- +* `SuperVectorizer` is renamed as :class:`TableVectorizer`, a warning is raised when using the old name. + :pr:`484` by :user:`Jovan Stojanovic ` + +* New experimental feature: joining tables using :func:`fuzzy_join` by approximate key matching. Matches are based + on string similarities and the nearest neighbors matches are found for each category. + :pr:`291` by :user:`Jovan Stojanovic ` and :user:`Leo Grinsztajn ` + +* New experimental feature: :class:`FeatureAugmenter`, a transformer + that augments with :func:`fuzzy_join` the number of features in a main table by using information from auxiliary tables. + :pr:`409` by :user:`Jovan Stojanovic ` + +* Unnecessary API has been made private: everything (files, functions, classes) + starting with an underscore shouldn't be imported in your code. :pr:`331` by :user:`Lilian Boulard ` + +* The :class:`MinHashEncoder` now supports a `n_jobs` parameter to parallelize + the hashes computation. :pr:`267` by :user:`Leo Grinsztajn ` and :user:`Lilian Boulard `. + +* New experimental feature: deduplicating misspelled categories using :func:`deduplicate` by clustering string distances. + This function works best when there are significantly more duplicates than underlying categories. + :pr:`339` by :user:`Moritz Boos `. + +Minor changes +------------- +* Add example `Wikipedia embeddings to enrich the data`. :pr:`487` by :user:`Jovan Stojanovic ` + +* **datasets.fetching**: contains a new function :func:`get_ken_embeddings` that can be used to download Wikipedia + embeddings and filter them by type. + +* **datasets.fetching**: contains a new function :func:`fetch_world_bank_indicator` that can be used to download indicators + from the World Bank Open Data platform. + :pr:`291` by :user:`Jovan Stojanovic ` + +* Removed example `Fitting scalable, non-linear models on data with dirty categories`. :pr:`386` by :user:`Jovan Stojanovic ` + +* :class:`MinHashEncoder`'s :func:`minhash` method is no longer public. :pr:`379` by :user:`Jovan Stojanovic ` + +* Fetching functions now have an additional argument ``directory``, + which can be used to specify where to save and load from datasets. + :pr:`432` by :user:`Lilian Boulard ` + +* Fetching functions now have an additional argument ``directory``, + which can be used to specify where to save and load from datasets. + :pr:`432` and :pr:`453` by :user:`Lilian Boulard ` + +* The :class:`TableVectorizer`'s default `OneHotEncoder` for low cardinality categorical variables now defaults + to `handle_unknown="ignore"` instead of `handle_unknown="error"` (for sklearn >= 1.0.0). + This means that categories seen only at test time will be encoded by a vector of zeroes instead of raising an error. :pr:`473` by :user:`Leo Grinsztajn ` + +Bug fixes +--------- + +* The :class:`MinHashEncoder` now considers `None` and empty strings as missing values, rather + than raising an error. :pr:`378` by :user:`Gael Varoquaux ` + +Dirty-cat Release 0.3.0 +========================== + +Major changes +------------- + +* New encoder: :class:`DatetimeEncoder` can transform a datetime column into several numeric columns + (year, month, day, hour, minute, second, ...). It is now the default transformer used + in the :class:`TableVectorizer` for datetime columns. :pr:`239` by :user:`Leo Grinsztajn ` + +* The :class:`TableVectorizer` has seen some major improvements and bug fixes: + + - Fixes the automatic casting logic in ``transform``. + - To avoid dimensionality explosion when a feature has two unique values, the default encoder (:class:`~sklearn.preprocessing.OneHotEncoder`) now drops one of the two vectors (see parameter `drop="if_binary"`). + - ``fit_transform`` and ``transform`` can now return unencoded features, like the :class:`~sklearn.compose.ColumnTransformer`'s behavior. Previously, a ``RuntimeError`` was raised. + + :pr:`300` by :user:`Lilian Boulard ` + +* **Backward-incompatible change in the TableVectorizer**: + To apply ``remainder`` to features (with the ``*_transformer`` parameters), + the value ``'remainder'`` must be passed, instead of ``None`` in previous versions. + ``None`` now indicates that we want to use the default transformer. :pr:`303` by :user:`Lilian Boulard ` + +* Support for Python 3.6 and 3.7 has been dropped. Python >= 3.8 is now required. :pr:`289` by :user:`Lilian Boulard ` + +* Bumped minimum dependencies: + + - scikit-learn>=0.23 + - scipy>=1.4.0 + - numpy>=1.17.3 + - pandas>=1.2.0 :pr:`299` and :pr:`300` by :user:`Lilian Boulard ` + +* Dropped support for Jaro, Jaro-Winkler and Levenshtein distances. + + - The :class:`SimilarityEncoder` now exclusively uses ``ngram`` for similarities, + and the `similarity` parameter is deprecated. It will be removed in 0.5. :pr:`282` by :user:`Lilian Boulard ` + +Notes +----- + +* The ``transformers_`` attribute of the :class:`TableVectorizer` now contains column + names instead of column indices for the "remainder" columns. :pr:`266` by :user:`Leo Grinsztajn ` + + +Dirty-cat Release 0.2.2 +========================= + +Bug fixes +--------- + +* Fixed a bug in the :class:`TableVectorizer` causing a :class:`FutureWarning` + when using the :func:`get_feature_names_out` method. :pr:`262` by :user:`Lilian Boulard ` + + +Dirty-cat Release 0.2.1 +========================== + +Major changes +------------- + +* Improvements to the :class:`TableVectorizer` + + - Type detection works better: handles dates, numerics columns encoded as strings, or numeric columns containing strings for missing values. + + :pr:`238` by :user:`Leo Grinsztajn ` + +* :func:`get_feature_names` becomes :func:`get_feature_names_out`, following changes in the scikit-learn API. + :func:`get_feature_names` is deprecated in scikit-learn > 1.0. :pr:`241` by :user:`Gael Varoquaux ` + +* Improvements to the :class:`MinHashEncoder` + - It is now possible to fit multiple columns simultaneously with the :class:`MinHashEncoder`. + Very useful when using for instance the :func:`~sklearn.compose.make_column_transformer` function, + on multiple columns. + + :pr:`243` by :user:`Jovan Stojanovic ` + + +Bug-fixes +--------- + +* Fixed a bug that resulted in the :class:`GapEncoder` ignoring the analyzer argument. :pr:`242` by :user:`Jovan Stojanovic ` + +* :class:`GapEncoder`'s `get_feature_names_out` now accepts all iterators, not just lists. :pr:`255` by :user:`Lilian Boulard ` + +* Fixed :class:`DeprecationWarning` raised by the usage of `distutils.version.LooseVersion`. :pr:`261` by :user:`Lilian Boulard ` + +Notes +----- + +* Remove trailing imports in the :class:`MinHashEncoder`. + +* Fix typos and update links for website. + +* Documentation of the :class:`TableVectorizer` and the :class:`SimilarityEncoder` improved. + +Dirty-cat Release 0.2.0 +========================= + +Also see pre-release 0.2.0a1 below for additional changes. + +Major changes +------------- + +* Bump minimum dependencies: + + - scikit-learn (>=0.21.0) :pr:`202` by :user:`Lilian Boulard ` + - pandas (>=1.1.5) **! NEW REQUIREMENT !** :pr:`155` by :user:`Lilian Boulard ` + +* **datasets.fetching** - backward-incompatible changes to the example + datasets fetchers: + + - The backend has changed: we now exclusively fetch the datasets from OpenML. + End users should not see any difference regarding this. + - The frontend, however, changed a little: the fetching functions stay the same + but their return values were modified in favor of a more Pythonic interface. + Refer to the docstrings of functions `dirty_cat.datasets.fetch_*` + for more information. + - The example notebooks were updated to reflect these changes. :pr:`155` by :user:`Lilian Boulard ` + +* **Backward incompatible change to** :class:`MinHashEncoder`: The :class:`MinHashEncoder` now + only supports two dimensional inputs of shape (N_samples, 1). + :pr:`185` by :user:`Lilian Boulard ` and :user:`Alexis Cvetkov `. + +* Update `handle_missing` parameters: + + - :class:`GapEncoder`: the default value "zero_impute" becomes "empty_impute" (see doc). + - :class:`MinHashEncoder`: the default value "" becomes "zero_impute" (see doc). + + :pr:`210` by :user:`Alexis Cvetkov `. + +* Add a method "get_feature_names_out" for the :class:`GapEncoder` and the :class:`TableVectorizer`, + since `get_feature_names` will be depreciated in scikit-learn 1.2. :pr:`216` by :user:`Alexis Cvetkov ` + +Notes +----- + +* Removed hard-coded CSV file `dirty_cat/data/FiveThirtyEight_Midwest_Survey.csv`. + + +* Improvements to the :class:`TableVectorizer` + + - Missing values are not systematically imputed anymore + - Type casting and per-column imputation are now learnt during fitting + - Several bugfixes + + :pr:`201` by :user:`Lilian Boulard ` + +Dirty-cat Release 0.2.0a1 +============================ + +Version 0.2.0a1 is a pre-release. +To try it, you have to install it manually using:: + + pip install --pre dirty_cat==0.2.0a1 + +or from the GitHub repository:: + + pip install git+https://github.com/dirty-cat/dirty_cat.git + +Major changes +------------- + +* Bump minimum dependencies: + + - Python (>= 3.6) + - NumPy (>= 1.16) + - SciPy (>= 1.2) + - scikit-learn (>= 0.20.0) + +* :class:`TableVectorizer`: Added automatic transform through the + :class:`TableVectorizer` class. It transforms + columns automatically based on their type. It provides a replacement + for scikit-learn's :class:`~sklearn.compose.ColumnTransformer` simpler to use on heterogeneous + pandas DataFrame. :pr:`167` by :user:`Lilian Boulard ` + +* **Backward incompatible change to** :class:`GapEncoder`: The :class:`GapEncoder` now only + supports two-dimensional inputs of shape (n_samples, n_features). + Internally, features are encoded by independent :class:`GapEncoder` models, + and are then concatenated into a single matrix. + :pr:`185` by :user:`Lilian Boulard ` and :user:`Alexis Cvetkov `. + + +Bug-fixes +--------- + +* Fix `get_feature_names` for scikit-learn > 0.21. :pr:`216` by :user:`Alexis Cvetkov ` + + +Dirty-cat Release 0.1.1 +======================== + +Major changes +------------- + +Bug-fixes +--------- + +* RuntimeWarnings due to overflow in :class:`GapEncoder`. :pr:`161` by :user:`Alexis Cvetkov ` + + +Dirty-cat Release 0.1.0 +========================= + +Major changes +------------- + +* :class:`GapEncoder`: Added online Gamma-Poisson factorization through the + :class:`GapEncoder` class. This method discovers latent categories formed + via combinations of substrings, and encodes string data as combinations of + these categories. To be used if interpretability is important. :pr:`153` by :user:`Alexis Cvetkov ` + +Bug-fixes +--------- + +* Multiprocessing exception in notebook. :pr:`154` by :user:`Lilian Boulard ` + + +Dirty-cat Release 0.0.7 +======================== + +* **MinHashEncoder**: Added ``minhash_encoder.py`` and ``fast_hast.py`` files + that implement minhash encoding through the :class:`MinHashEncoder` class. + This method allows for fast and scalable encoding of string categorical + variables. + +* **datasets.fetch_employee_salaries**: change the origin of download for employee_salaries. + + - The function now return a bunch with a dataframe under the field "data", + and not the path to the csv file. + - The field "description" has been renamed to "DESCR". + +* **SimilarityEncoder**: Fixed a bug when using the Jaro-Winkler distance as a + similarity metric. Our implementation now accurately reproduces the behaviour + of the ``python-Levenshtein`` implementation. + +* **SimilarityEncoder**: Added a `handle_missing` attribute to allow encoding + with missing values. + +* **TargetEncoder**: Added a `handle_missing` attribute to allow encoding + with missing values. + +* **MinHashEncoder**: Added a `handle_missing` attribute to allow encoding + with missing values. + +Dirty-cat Release 0.0.6 +========================= + +* **SimilarityEncoder**: Accelerate ``SimilarityEncoder.transform``, by: + + - computing the vocabulary count vectors in ``fit`` instead of ``transform`` + - computing the similarities in parallel using ``joblib``. This option can be + turned on/off via the ``n_jobs`` attribute of the :class:`SimilarityEncoder`. + +* **SimilarityEncoder**: Fix a bug that was preventing a :class:`SimilarityEncoder` + to be created when ``categories`` was a list. + +* **SimilarityEncoder**: Set the dtype passed to the ngram similarity + to float32, which reduces memory consumption during encoding. + +Dirty-cat Release 0.0.5 +======================== + +* **SimilarityEncoder**: Change the default ngram range to (2, 4) which + performs better empirically. + +* **SimilarityEncoder**: Added a `most_frequent` strategy to define + prototype categories for large-scale learning. + +* **SimilarityEncoder**: Added a `k-means` strategy to define prototype + categories for large-scale learning. + +* **SimilarityEncoder**: Added the possibility to use hashing ngrams for + stateless fitting with the ngram similarity. + +* **SimilarityEncoder**: Performance improvements in the ngram similarity. + +* **SimilarityEncoder**: Expose a `get_feature_names` method. diff --git a/skrub/_docs/CONTRIBUTING.rst b/skrub/_docs/CONTRIBUTING.rst new file mode 100644 index 000000000..8a6dfe829 --- /dev/null +++ b/skrub/_docs/CONTRIBUTING.rst @@ -0,0 +1,498 @@ +.. _contributing: + +Contributing guide +================== + +First off, thank you for taking the time to contribute! + +Below are some guidelines to help you get started. + + +Have a question? +---------------- + +If you have any questions, feel free to reach out: + +- Join our community on `Discord `_ for general chat and Q&A. +- Alternatively, you can `start a discussion on GitHub `_. + +What to know before you begin +----------------------------- + +To understand the purpose and goals behind skrub, please read our +`vision statement `_. + +If you're interested in the research behind skrub, +we encourage you to explore these papers: + +- `Similarity Encoding for Learning with Dirty + Categorical Variables `_ +- `Encoding High-Cardinality String Categorical + Variables `_. + +How can I contribute? +--------------------- + +Reporting bugs +~~~~~~~~~~~~~~ + +Using the library is the best way to discover bugs and limitations. If you find one, +please: + +1. **Check if an issue already exists** + by searching the `GitHub issues `_ + + - If **open**, leave a 👍 on the original message to signal that you are also affected. + - If closed, check for one of the following: + - A **merged pull request** may indicate the bug is fixed. Update your + skrub version or note if the fix is pending a release. + - A **wontfix label** or reasoning may be provided if the issue was + closed without a fix. +2. If the issue does not exist, `create a new one `_. + +How to submit a bug report? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To help us resolve the issue quickly, please include: + +- A **clear and descriptive title**. +- A **summary of the expected result**. +- Any **additional details** where the bug might occur or doesn't occur unexpectedly. +- A **code snippet** that reproduces the issue, if applicable. +- **Version information** for Python, skrub, and relevant dependencies (e.g., scikit-learn, numpy, pandas). + +How to write an example? +^^^^^^^^^^^^^^^^^^^^^^^^^ +We highly encourage contributors to add examples to the documentation +when they add new features, or if they have a use case that is not yet covered +in the documentation. + +You can find a guide on how to write examples in the :ref:`example guide `. + + +Suggesting enhancements +~~~~~~~~~~~~~~~~~~~~~~~ + +If you have an idea for improving skrub, whether it's a fix +or a new feature, first: + +- **Check if it has been proposed or implemented** by reviewing + `open pull requests `_. +- If not, `submit a new issue `_ + with your proposal before writing any code. + +How to submit an enhancement proposal? +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When proposing an enhancement: + +- **Use a clear and descriptive title**. +- **Explain the goal** of the enhancement. +- Provide a **detailed step-by-step description** of the proposed change. +- **Link to any relevant resources** that may support the enhancement. + + +If the enhancement proposal is validated +'''''''''''''''''''''''''''''''''''''''' + +Once your enhancement proposal is approved, let the maintainers know the following: + +- **If you will write the code and submit a Pull Request (PR)**: + Contributing the feature yourself is the quickest way to see it implemented. + We're here to guide you through the process if needed! To get started, + refer to the section :ref:`writing-your-first-pull-request`. +- **If you won't be writing the code**: + A developer can then take over the implementation. + However, please note that we cannot guarantee how long + it will take for the feature to be added. + + +If the enhancement is refused +''''''''''''''''''''''''''''' + +Although many ideas are great, not all will align with the objectives +of skrub. + +If your enhancement is not accepted, consider implementing it +as a separate package that builds on top of skrub! + +We would love to see your work, and in some cases, we might even +feature your package in the official repository. + + +.. _writing-your-first-pull-request: + +Writing your first Pull Request +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Preparing the ground +^^^^^^^^^^^^^^^^^^^^ + + +Before writing any code, make sure you have discussed your plans with the maintainers. +You can do this by opening a new issue to discuss a specific improvement (as +described above), or by commenting on an existing issue to express your interest in working on it. + +Be sure to get approval from the maintainers before you start coding, and especially +before opening any new pull requests (PRs). This helps prevent issues such as multiple +people working on the same problem independently, or working on an issue that is +not clearly defined. Without prior discussion, your PR may be closed for being out +of scope or unrelated to the problem at hand. + +Every PR should link to the issue it addresses. + +Setting up the environment +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To setup your development environment, you need to follow the steps in "From Source" tab +present in :ref:`Installing from source` page. +After that, you can return to this page to continue. + +Now that the development environment is ready, you may create a new branch and start working on +the new issue. + +.. code:: sh + + # fetch latest updates and start from the current head + git fetch upstream + git checkout -b my-branch-name-eg-fix-issue-123 + # make some changes + git add ./the/file-i-changed + git commit -m "my message" + git push --set-upstream origin my-branch-name-eg-fix-issue-123 + +At this point, if you visit again the `pull requests +page `__ github should show a +banner asking if you want to open a pull request from your new branch. + + +.. _implementation guidelines: + +Implementation Guidelines +^^^^^^^^^^^^^^^^^^^^^^^^^ + +When contributing, keep these project goals in mind: + +- **Pure Python code**: Avoid using binary extensions, Cython, or other compiled languages. +- **Production-friendly code**: + - Target the widest possible range of Python versions and dependencies. + - Minimize the use of external dependencies. + - Ensure backward compatibility as much as possible. +- **Performance over readability**: + Optimized code may be less readable, so please include clear and detailed comments. + Refer to this `best practice guide `_. +- **Explicit variable/function names**: Use descriptive, verbose names for clarity. +- **Document public API components**: + - Document all public functions, methods, variables, and class signatures. + - The public API refers to all components available for import and use by library users. Anything that doesn't begin with an underscore is considered part of the public API. + +Checking the quality of your code contribution +---------------------------------------------- + +Testing the code +~~~~~~~~~~~~~~~~ + +Tests for files in a given folder should be located in a sub-folder +named ``tests``: tests for skrub objects are located in ``skrub/tests/``, +tests for the dataframe API are in ``skrub/_dataframe/tests/`` and so on. + +Tests should check all functionalities of the code that you are going to +add. If needed, additional tests should be added to verify that other +objects behave correctly. + +Consider an example: your contribution is for the +``AmazingTransformer``, whose code is in +``skrub/_amazing_transformer.py``. The ``AmazingTransformer`` is added +as one of the default transformers for ``TableVectorizer``. + +As such, you should add a new file testing the functionality of +``AmazingTransformer`` in ``skrub/tests/test_amazing_transformer.py``, +and update the file ``skrub/tests/test_table_vectorizer.py`` so that it +takes into account the new transformer. + +Additionally, you might have updated the internal dataframe API in +``skrub/_dataframe/_common.py`` with a new function, +``amazing_function``. In this case, you should also update +``skrub/_dataframe/tests/test_common.py`` to add a test for the +``amazing_function``. + +Run each updated test file using ``pytest`` +(`pytest docs `_): + +.. code:: sh + + pytest -vsl skrub/tests/test_amazing_transformer.py \ + skrub/_dataframe/tests/test_common.py \ + skrub/_dataframe/tests/test_table_vectorizer.py + +The ``-vsl`` flag provides more information when running the tests. + +It is also possible to run a specific test, or set of tests using the +commands ``pytest the_file.py::the_test``, or +``pytest the_file.py -k 'test_name_pattern'``. This is helpful to avoid +having to run all the tests. + +If you work on Windows, you might have some issues with the working +directory if you use ``pytest``, while ``python -m pytest ...`` should +be more robust. + +Once you are satisfied with your changes, you can run all the tests to make sure +that your change did not break code elsewhere: + +.. code:: sh + + pytest -s skrub/tests + +Finally, sync your changes with the remote repository and wait for CI to run. + +Checking coverage on the local machine +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Checking coverage is one of the operations that is performed after +submitting the code. As this operation may take a long time online, it +is possible to check whether the code coverage is high enough on your +local machine. + +Run your tests with the ``--cov`` and ``--cov-report`` arguments: + +.. code:: sh + + pytest -vsl skrub/tests/test_amazing_transformer.py --cov=skrub --cov-report=html + +This will create the folder ``htmlcov``: by opening +``htmlcov/index.html`` it is possible to check what lines are covered in +each file. + +Updating doctests +~~~~~~~~~~~~~~~~~ + +If you alter the default behavior of an object, then this might affect +the docstrings. Check for possible problems by running + +.. code:: sh + + pytest skrub/path/to/file + + +Formatting and pre-commit checks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Formatting the code well helps with code development and maintenance, +which why is skrub requires that all commits follow a specific set of +formatting rules to ensure code quality. + +Luckily, these checks are performed automatically by the ``pre-commit`` +tool (`pre-commit docs `__) before any commit +can be pushed. Something worth noting is that if the ``pre-commit`` +hooks format some files, the commit will be canceled: you will have to +stage the changes made by ``pre-commit`` and commit again. + +Ensuring the documentation builds +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. + Inspired by: https://github.com/scikit-learn/scikit-learn/blob/main/doc/developers/contributing.rst + +First, make sure you have properly installed the development version of skrub. +You can follow the :ref:`installation_instructions` > "From source" section, if needed. + +To build the documentation, you need to be in the ``doc`` folder: + +.. code:: bash + + cd doc + +To generate the full documentation, including the example gallery, +run the following command: + +.. code:: bash + + make html + +On Windows, use: + +.. code:: bat + + make.bat html + +.. note:: + + If you are working on Windows, building the example ``1131_optuna_choices`` + may fail with a permission error when running ``make.bat html``. This is + because optuna uses symlinks for file locking, which requires admin + privileges on Windows by default. The rest of the documentation build + should run without problem, so it is safe to ignore this error if your + contribution does not touch that particular example. + +The documentation will be generated in the ``_build/html/`` directory +and are viewable in a web browser, for instance by opening the local +``_build/html/index.html`` file. + +Running all the examples can take a while, so if you only want to generate +specific examples, you can use the following command with a regex pattern: + +.. code:: bash + + make html EXAMPLES_PATTERN=your_regex_goes_here + +On Windows, use: + +.. code:: bat + + make.bat html EXAMPLES_PATTERN=your_regex_goes_here + +This is especially helpful when you're only modifying or checking a few examples. + +It is also possible to build the documentation without running the examples +without running the examples by using the following command: + +.. code:: bash + + make html-noplot + +On Windows, use: + +.. code:: bat + + make.bat html-noplot + +This command generates the documentation without re-executing the examples, which can +take a long time. This is useful if you are only modifying the documentation itself, such as fixing +typos or improving explanations. + + +**Using pixi** + +You can download and install pixi from `here `_. + +From the repository root: + +.. code:: bash + + # Build documentation without running examples (faster) + pixi run build-doc-quick + + # Build the full documentation, including examples + pixi run build-doc + + # Clean previously built documentation + pixi run clean-doc + +The documentation will be generated in the ``doc/_build/html/`` directory. +You can view it by opening the local ``doc/_build/html/index.html`` file. + +.. warning:: + + On Intel-based macOS systems (``osx-64``), some pixi environments may not + resolve correctly due to missing upstream package builds (e.g., for PyTorch). + If you encounter issues, you can always fall back to using ``make`` as + described above. + +Editing the API reference documentation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +**All public functions and classes must be documented in the API +reference**, hence when adding a public function or class, a new entry must be +added, as detailed just above. + +To add a new entry to the :ref:`API reference documentation` or change its +content, head to ``doc/api_reference.py``. This data is then used by ``doc/conf.py`` +to render templates located at ``doc/reference/*.rst.template``. + + +Submitting your code +-------------------- + +Once you have pushed your commits to your remote repository, you can submit +a PR by clicking the "Compare & pull request" button on GitHub, +targeting the skrub repository. + +Updating the changelog +~~~~~~~~~~~~~~~~~~~~~~ +Any user-facing change to the codebase needs to be reported in the changelog, +found in the ``CHANGES.rst`` file in the root of the repository. A user-facing +change is any change to a functionality of skrub that users are expected to interact +with: for example, adding or removing a parameter, adding a new transformer, +deprecating a function, etc. + +Changes made in the test suite, or changes made in the +private parts of the library, should not be reported, unless they bring some benefit +to the user (such as performance improvements). Normally, changes made to the +documentation, such as typo or formatting fixes, are not reported either, while +new examples usually can be added. +Depending on the nature of the PR, a maintainer may add the "no +changelog needed" label to skip the corresponding check if a changelog entry isn't +relevant. + +Changelog entries need to follow a specific format: the change should be described +in sufficient detail for users to understand how they may be affected, and the +entry must list both the PR number and the GitHub username of the author(s) of the +PR. + +Here is an example: + +.. code:: bash + + - :meth:`DataOp.skb.apply` now allows passing extra named arguments to the + estimator's methods through the parameters ``fit_kwargs``, ``predict_kwargs`` + etc. :pr:`1642` by :user:`Jérôme Dockès `. + +The PR number is reported with the directive ``:pr:`NUMBER```, and the author +of the PR uses the directive ``:user:`AUTHOR NAME ```. + +Missing changelog entries, or changelog entries that do not follow the format, +will fail the changelog check in the CI. + +Continuous Integration (CI) +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +After creating your PR, CI tools will run proceed to run all the tests on all +configurations supported by skrub. + +- **Github Actions**: + Used for testing skrub across various platforms (Linux, macOS, Windows) + and dependencies. +- **CircleCI**: + Builds and verifies the project documentation. + +If any of the following markers appears in the commit message, the following +actions are taken. + + ====================== =================== + Commit Message Marker Action Taken by CI + ---------------------- ------------------- + [ci skip] CI is skipped completely + [skip ci] CI is skipped completely + [skip github] CI is skipped completely + [deps nightly] CI is run with the nightly builds of dependencies + [doc skip] Docs are not built + [doc quick] Docs built, but excludes example gallery plots + [doc build] Docs built including example gallery plots (longer) + ====================== =================== + +Note that by default the documentation is built, but only the examples that are +directly modified by the pull request are executed. + +CI is testing all possible configurations supported by skrub, so tests may fail +with configurations different from what you are developing with. If this is the +case, it is possible to run the tests in the environment that is failing by +using `pixi `_. For example if the env is ``ci-py309-min-optional-deps``, it is +possible to replicate it using the following command: + +.. code:: sh + + pixi run -e ci-py309-min-optional-deps pytest skrub/tests/path/to/test + +This command downloads the specific environment on the machine, so you can test +it locally and apply fixes, or have a clearer idea of where the code is failing +to discuss with the maintainers. + +Finally, if the remote repository was changed, you might need to run + ``pre-commit run --all-files`` to make sure that the formatting is + correct. + +Integration +^^^^^^^^^^^ + +Community consensus is key in the integration process. Expect a minimum of +1 to 3 reviews from maintainers depending on the size of the change before we consider +merging the PR. diff --git a/skrub/_docs/RELEASE_PROCESS.rst b/skrub/_docs/RELEASE_PROCESS.rst new file mode 100644 index 000000000..4fcc7db19 --- /dev/null +++ b/skrub/_docs/RELEASE_PROCESS.rst @@ -0,0 +1,157 @@ +Release process +=============== + +Target audience +--------------- + +This document is aimed at established contributors to the project. + +Process +------- + +Going further, we assume you have write-access to both the repository, PyPI and +conda-forge project page. + +.. note:: We follow scikit-learn versioning conventions: + + - Major/Minor releases are numbered X.Y.0. + - Bug-fix releases are done as needed between major/minor releases and only apply to + the last stable version. These releases are numbered X.Y.Z. + +To release a new minor version of skrub (e.g., from 0.1.0 to 0.2.0), here are the main +steps and appropriate resources: + +Preparing the release branch +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Create the ``0.2.X`` branch, branching from upstream/main, and push it upstream + (it may already exist). You can also use the GitHub UI to create the branch if you + disabled ``git push upstream`` in your local git config. +- Edit CHANGES.rst: replace "ongoing development" with ``0.2.0`` +- Edit VERSION.txt: replace ``0.2.dev0`` with ``0.2.0`` +- Build the wheel and test it: + + - ``rm -r dist skrub.egg-info`` + - ``python -m build`` (may need ``pip install build``) + - ``twine check dist/*`` (may need ``pip install twine``) + - In a directory outside of the skrub repo: + + - Install the wheel in a fresh virtualenv + - Run all tests with ``pytest --pyargs skrub`` + +- git commit the changes done to CHANGES.rst and VERSION.txt +- If we are doing a bugfix release (``0.2.X`` already existed before) we need to rebase + on the existing ``0.2.X``. + + - Run ``git rebase -i upstream/0.2.X`` + - All commits that have been made on main that we want to keep will be replayed on + top of the last release's tag in ``0.2.X``. + +- Open a PR targeting ``0.2.X``. This will update the doc for the stable release. While + the update runs, we can prepare a PR on the main branch to be merged after the + release, see the next section. + +Meanwhile, preparing the post-released PR +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- For a major/minor (not a patch) release: + - VERSION.txt: update to ``0.3.dev0`` (the next minor). + - CHANGES.rst: create a header for the new entries ("ongoing development"). + - doc/version.json: update the version numbers of the stable release and dev branch. + Don't forget to add an entry for the previously stable version. + + +The doc update has succeeded +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Merge the PR targeting ``0.2.X``, **without squashing the commits**. + +.. warning:: + + This PR should be merged with the rebase mode instead of the usual squash mode + because we want to keep the history in the ``0.2.X`` branch close to the history of + the main branch, which will help for future bug fix releases. + + By default, only the squash & merge option is available to merge PRs on the main + branch. So, when releasing, we need to temporarily enable the rebase option. + To do so, head to Settings -> General -> Pull request, enable rebasing, merge the + PR targeting ``0.2.X`` with the rebase option, then disable the setting again. + +- Check the rendering of the doc for the built ``0.2.X`` branch, the examples and the + changelog. Ideally, we should go over all features and double check that the docs are + being rendered correctly, because issues there often go unnoticed. + + +Next, we'll build the wheel and push it to Pypi! + + +Pushing the wheel to Pypi +^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Checkout to the release candidate branch: + + .. code:: shell + + git fetch upstream + git checkout upstream/0.2.X + +- Build the wheel and test it: + + - ``rm -r dist skrub.egg-info`` + - ``python -m build`` (may need ``pip install build``) + - ``twine check dist/*`` (may need ``pip install twine``) + - In a directory outside of the skrub repo: + + - Install the wheel in a fresh virtualenv + - Run all tests with ``pytest --pyargs skrub`` + +- If test passed successfully, upload to Pypi: ``twine upload dist/*``. +- Tag the release commit and push the tag: + + - ``git tag -s '0.2.0'``, ``-s`` is for signing and is optional. + - ``git push upstream tag 0.2.0`` + +- Check that your version is now on Pypi. +- Merge the post-release PR +- For major/minor releases only, in the documentation branches repository + https://github.com/skrub-data/skrub-data.github.io, update the documentation symlink + to stable version, here from 0.1 to 0.2: + + .. code:: shell + + rm stable + ln -s 0.2 stable + + ``stable`` should point on the latest number release. + + +Update the conda-forge recipe +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- Create the branch ``release-0.2.0`` in + `skrub-feedstock `_ +- Edit ``recipe/meta.yml``, which is the only file we edit manually in that repo: + - Update the version number. + - Update the sha256 using Pypi hash. + - If needed, reset the build number to 0. + - If needed, update the requirements. + + - Check the new requirements with: + + .. code:: shell + + git checkout 0.2.0 + git diff 0.1.0 -- pyproject.toml + +- Open a PR targeting ``upstream/skrub-feedstock`` main branch. +- Use the the checklist posted in the PR template. In particular, it asks to post a + comment asking a bot to re-render the recipe. Make sure to wait until it has finished. +- Merge the PR. It takes up to an hour for the package to be available from the + conda-forge channel. +- When it becomes available, install it in a fresh environment and run tests. + +.. note:: + + You can add new maintainers to that repo by listing them at the end of meta.yml. + +- If the new recipe works fine, announce the release on social network channels 🎉! diff --git a/skrub/_docs/_templates/base.rst b/skrub/_docs/_templates/base.rst new file mode 100644 index 000000000..b3200116b --- /dev/null +++ b/skrub/_docs/_templates/base.rst @@ -0,0 +1,37 @@ +{{ objname | escape | underline(line="=") }} + +{% if objtype == "module" -%} + +.. automodule:: {{ fullname }} + +{%- elif objtype == "function" -%} + +.. currentmodule:: {{ module }} + +.. autofunction:: {{ objname }} + +.. minigallery:: {{ module }}.{{ objname }} + :add-heading: Gallery examples + :heading-level: - + +{%- elif objtype == "class" -%} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: + :inherited-members: + :special-members: __call__ + :exclude-members: get_metadata_routing, set_fit_request + +.. minigallery:: {{ module }}.{{ objname }} {% for meth in methods %}{{ module }}.{{ objname }}.{{ meth }} {% endfor %} + :add-heading: Gallery examples + :heading-level: - + +{%- else -%} + +.. currentmodule:: {{ module }} + +.. auto{{ objtype }}:: {{ objname }} + +{%- endif -%} diff --git a/skrub/_docs/_templates/data_op_class.rst b/skrub/_docs/_templates/data_op_class.rst new file mode 100644 index 000000000..1b88b949c --- /dev/null +++ b/skrub/_docs/_templates/data_op_class.rst @@ -0,0 +1,6 @@ +{{ objname | escape | underline(line="=") }} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :exclude-members: skb, __call__ diff --git a/skrub/_docs/_templates/numpydoc_docstring.rst b/skrub/_docs/_templates/numpydoc_docstring.rst new file mode 100644 index 000000000..fd6a35f76 --- /dev/null +++ b/skrub/_docs/_templates/numpydoc_docstring.rst @@ -0,0 +1,16 @@ +{{index}} +{{summary}} +{{extended_summary}} +{{parameters}} +{{returns}} +{{yields}} +{{other_parameters}} +{{attributes}} +{{raises}} +{{warns}} +{{warnings}} +{{see_also}} +{{notes}} +{{references}} +{{examples}} +{{methods}} diff --git a/skrub/_docs/about.rst b/skrub/_docs/about.rst new file mode 100644 index 000000000..b8f8a1bf0 --- /dev/null +++ b/skrub/_docs/about.rst @@ -0,0 +1,18 @@ + +About +----- + +skrub shares much of its DNA with `scikit-learn +`__. + +skrub is the continuation of `dirty-cat `_ +with a broader scope and greater ambition. + +skrub is a young project born from research. We welcome feedback +on successes and failures with the different techniques on real-world data, or +suggestions for open datasets on which we can do better examples and empirical work. + +skrub received funding from French research projects: `DirtyData +`_ (ANR-17-CE23-0018), `LearnI +`_ (ANR-20-CHIA-0026), and `P16 +`_. diff --git a/skrub/_docs/column_level_featurizing.rst b/skrub/_docs/column_level_featurizing.rst new file mode 100644 index 000000000..3c9e9c1f7 --- /dev/null +++ b/skrub/_docs/column_level_featurizing.rst @@ -0,0 +1,19 @@ +.. _user_guide_encoders_index: + +Column-level feature extraction +=============================== + +Skrub provides various transformers that help with feature engineering numeric, +datetime and categorical data. The encoders covered in this section convert the +raw features found in an input dataframe into numeric features that can be used +directly by machine learning models. + +.. include:: includes/big_toc_css.rst + +.. toctree:: + :maxdepth: 3 + + modules/column_level_featurizing/feature_engineering_categorical + modules/column_level_featurizing/feature_engineering_datetimes + modules/column_level_featurizing/feature_engineering_numerical + modules/column_level_featurizing/advanced_columnwise_operations diff --git a/skrub/_docs/data_ops.rst b/skrub/_docs/data_ops.rst new file mode 100644 index 000000000..31c832f54 --- /dev/null +++ b/skrub/_docs/data_ops.rst @@ -0,0 +1,97 @@ +.. _user_guide_data_ops_index: + +.. currentmodule:: skrub + +Building complete pipelines with DataOps +======================================== + +A skrub DataOp is a complete machine learning pipeline —from data loading and +wrangling to the final prediction— in a single object that can be fitted, tuned, +cross-validated, and saved in a file like any scikit-learn estimator. + +By integrating the whole data processing, DataOps help to validate pipelines +while **avoiding data leakage**, to **tune complex modelling choices**, and to keep +track of important **fitted (learned) state**. + +To solve a machine-learning task we often need to combine multiple operations +such as loading and filtering data, joining tables and computing aggregations, +extracting numerical features, and fitting a classifier or regressor. + +**Storing state**  Each of those operations may need to be fitted: to learn some +information from training data and reuse it to apply consistent transformations +to new data. This is the case for transformers like the +:class:`~sklearn.preprocessing.StandardScaler` and :class:`TableVectorizer` and +estimators like :class:`~sklearn.ensemble.RandomForestClassifier`. + +**Tuning**  Moreover, each processing step may involve decisions that need to be +tuned (*tuning* means finding the value that gives the best predictive +performance), for example: what weather forecast features should I include to +predict the load on an electric grid? How should I encode a product description +to help predict the product's category? What learning rate to set on a +:class:`~sklearn.ensemble.HistGradientBoostingRegressor`? + +**Validation**  Finally, the quality of predictions must be evaluated on +held-out data (with a train/test split or cross-validation), taking care to +**avoid leakage** of test data into the training set. + +Separating the data wrangling from the fitted estimator prevents correctly +handling the tasks above. Skrub DataOps help by binding an arbitrary set of +transformations of any number of inputs in a single estimator. These +transformations can be easily parametrized with tunable choices. The resulting +objects have built-in methods for cross-validation and tuning with either Optuna +or scikit-learn, and for inspecting runs and intermediate results. Once fitted, +they can be saved in a file, loaded, applied to new data as easily as a single +:class:`~sklearn.linear_model.LogisticRegression`. + +.. dropdown:: Going beyond the scikit-learn Pipeline + :color: primary + + To some extent, the DataOps exist for the same reasons as the simpler + scikit-learn :class:`sklearn.pipeline.Pipeline` used in other parts of this + documentation. However the Pipeline is too limited for many real-world problems: + it can only represent a linear sequence of scikit-learn transformers, the design + matrix and target variables must be constructed and divided into training and + testing sets outside of the pipeline and the number of rows cannot change, only + a single table can be handled, hyperparameter choices are difficult to define, + etc. . Skrub DataOps remove those limitations and add several useful features + such as interactive previews and integration with Optuna. + +Data Ops basic concepts +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 3 + + modules/data_ops/basics/what_are_data_ops + modules/data_ops/basics/building_data_ops_plan + auto_tutorials/1110_data_ops_intro + modules/data_ops/basics/using_previews + modules/data_ops/basics/direct_access_methods + modules/data_ops/basics/control_flow + modules/data_ops/basics/data_ops_vs_alternatives + +Building a complex pipeline with the skrub Data Ops +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 2 + + modules/data_ops/ml_pipeline/applying_ml_estimators + modules/data_ops/ml_pipeline/applying_different_transformers + modules/data_ops/ml_pipeline/documenting_data_ops_plan + modules/data_ops/ml_pipeline/evaluating_debugging_data_ops + modules/data_ops/ml_pipeline/using_part_of_data_ops_plan + modules/data_ops/ml_pipeline/subsampling_data + +Tuning and validating skrub DataOps plans +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. toctree:: + :maxdepth: 2 + + modules/data_ops/validation/tuning_validating_data_ops + modules/data_ops/validation/hyperparameter_tuning + modules/data_ops/validation/nested_cross_validation + modules/data_ops/validation/nesting_choices_choosing_pipelines + modules/data_ops/validation/exporting_data_ops + modules/data_ops/validation/tuning_with_optuna diff --git a/skrub/_docs/default_wrangling.rst b/skrub/_docs/default_wrangling.rst new file mode 100644 index 000000000..4ce00b846 --- /dev/null +++ b/skrub/_docs/default_wrangling.rst @@ -0,0 +1,17 @@ +.. _user_guide_building_pipeline_index: + +Wrangling data with good defaults +================================= + +This section covers how to build a predictive pipeline starting from a dataframe. +The skrub objects described in this section can be used as strong defaults for +building baseline pipelines, and can be customized for specific use cases. + + +.. toctree:: + :maxdepth: 3 + + modules/default_wrangling/cleaning_dataframes + modules/default_wrangling/table_vectorizer + modules/default_wrangling/tabular_pipeline + modules/default_wrangling/apply_to_cols diff --git a/skrub/_docs/development.rst b/skrub/_docs/development.rst new file mode 100644 index 000000000..15989bfe7 --- /dev/null +++ b/skrub/_docs/development.rst @@ -0,0 +1,18 @@ + +=========== +Development +=========== + +While ``skrub`` is still in its early stages, we believe in openness and +community development from the start. Join us in building a great package to +facilitate learning on databases. + +.. include:: includes/big_toc_css.rst + +.. toctree:: + + vision + about + CONTRIBUTING + tutorial_example + RELEASE_PROCESS diff --git a/skrub/_docs/documentation.rst b/skrub/_docs/documentation.rst new file mode 100644 index 000000000..1512bcf92 --- /dev/null +++ b/skrub/_docs/documentation.rst @@ -0,0 +1,27 @@ +.. _user_guide: + +User Guide +========== + +Skrub is a Python library that facilitates machine learning with tabular data +(dataframes, such as pandas and polars) using a scikit-learn-compatible API. + +Use the sections below to navigate the guide. For a quickstart example, +try :ref:`Getting Started `. +For runnable code, see the :doc:`Example gallery `. +For class and function details, see the :ref:`API Reference `. +For common use cases and how to address them, see the :ref:`How-to guides `. + + +.. include:: includes/big_toc_css.rst + +.. toctree:: + :maxdepth: 3 + + auto_tutorials/0000_getting_started + exploring_a_dataframe + default_wrangling + column_level_featurizing + multi_column_operations + data_ops + joining_dataframes diff --git a/skrub/_docs/examples/0010_apply_to_cols.py b/skrub/_docs/examples/0010_apply_to_cols.py new file mode 100644 index 000000000..2e45f4dda --- /dev/null +++ b/skrub/_docs/examples/0010_apply_to_cols.py @@ -0,0 +1,174 @@ +""" +Hands-On with Column Selection and Transformers +=============================================== + +In previous examples, we saw how skrub provides powerful abstractions like +:class:`~skrub.TableVectorizer` and :func:`~skrub.tabular_pipeline` to create pipelines. + +In this new example, we show how to create more flexible pipelines by selecting +and transforming dataframe columns using arbitrary logic. + +.. |ApplyToCols| replace:: :class:`~skrub.ApplyToCols` +.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` +.. |SelectCols| replace:: :class:`~skrub.SelectCols` +.. |DropCols| replace:: :class:`~skrub.DropCols` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |OrdinalEncoder| replace:: :class:`~sklearn.preprocessing.OrdinalEncoder` +.. |PCA| replace:: :class:`~sklearn.decomposition.PCA` +.. |Pipeline| replace:: :class:`~sklearn.pipeline.Pipeline` +.. |ColumnTransformer| replace:: :class:`~sklearn.compose.ColumnTransformer` + +""" + +# %% +# We begin with loading a dataset with heterogeneous datatypes, and replacing Pandas's +# display with the TableReport display via :func:`skrub.patch_display`. +import pandas as pd + +import skrub +from skrub.datasets import fetch_employee_salaries + +skrub.patch_display() +file_path = fetch_employee_salaries().path +data = pd.read_csv(file_path) +X = data.drop(columns="current_annual_salary") +y = data["current_annual_salary"] +X + +# %% +# Our goal is now to apply a |StringEncoder| to two columns of our +# choosing: ``division`` and ``employee_position_title``. +# +# We can achieve this using |ApplyToCols|, whose job is to apply a +# transformer to multiple columns independently, and let unmatched columns through +# without changes. +# This can be seen as a handy drop-in replacement of the +# |ColumnTransformer|. +# +# Since we selected two columns and set the number of components to ``30`` each, +# |ApplyToCols| will create ``2*30`` embedding columns in the dataframe +# ``Xt``, which we prefix with ``lsa_``. +from skrub import ApplyToCols, StringEncoder + +apply_string_encoder = ApplyToCols( + StringEncoder(n_components=30), + cols=["division", "employee_position_title"], + rename_columns="lsa_{}", +) +Xt = apply_string_encoder.fit_transform(X) +Xt + +# %% +# The |ApplyToCols| class can detect automatically whether the transformer is a +# ``SingleColumnTransformer`` (i.e., it can only be applied to one column at a time) +# or not, and apply it accordingly. The |StringEncoder| is a ``SingleColumnTransformer`` +# and thus applied to each column independently. + +# %% +# The |ApplyToCols| class can also be used with transformers that +# can be applied to multiple columns at once, such as the |PCA|. +# Here, we want to use PCA to reduce the number of dimensions of the new ``lsa_`` +# columns. +# +# To select columns without hardcoding their names, we introduce +# :ref:`selectors`, which allow for flexible matching pattern +# and composable logic. +# +# The regex selector below will match all columns prefixed with ``"lsa"``, and pass them +# to |ApplyToCols| which will assemble these columns into a dataframe +# and finally pass it to the PCA +# +# Note that |ApplyToCols| will automatically detect that PCA is not a +# ``SingleColumnTransformer`` +# and apply it to the whole sub-dataframe of columns chosen by the selector at once. + +from sklearn.decomposition import PCA + +from skrub import selectors as s + +apply_pca = ApplyToCols(PCA(n_components=8), cols=s.regex("lsa")) +Xt = apply_pca.fit_transform(Xt) +Xt + +# %% +# These two selectors are scikit-learn transformers and can be chained together within +# a |Pipeline|. +from sklearn.pipeline import make_pipeline + +model = make_pipeline( + apply_string_encoder, + apply_pca, +).fit_transform(X) + +# %% +# .. admonition:: Under the hood of |ApplyToCols| +# :collapsible: closed +# +# |ApplyToCols| is implemented using the ``ApplyToEachCol`` and ``ApplyToSubFrame`` +# classes. +# The former applies a transformer to each column independently, while the latter +# applies a transformer to a sub-dataframe. +# Normally, users don't need to worry about these two classes, but they can be useful +# when more control is needed. + +# %% +# Note that selectors also come in handy in a pipeline to select or drop columns, using +# |SelectCols| and |DropCols|. +from sklearn.preprocessing import StandardScaler + +from skrub import SelectCols + +# Select only numerical columns +pipeline = make_pipeline( + SelectCols(cols=s.numeric()), + StandardScaler(), +).set_output(transform="pandas") +pipeline.fit_transform(Xt) + +# %% +# Let's run through one more example to showcase the expressiveness of the selectors. +# Suppose we want to apply an |OrdinalEncoder| on +# categorical columns with low cardinality (e.g., fewer than ``40`` unique values). +# +# We define a column filter using skrub selectors with a lambda function. Note that +# the same effect can be obtained directly by using +# :func:`~skrub.selectors.cardinality_below`. +from sklearn.preprocessing import OrdinalEncoder + +low_cardinality = s.filter(lambda col: col.nunique() < 40) +ApplyToCols(OrdinalEncoder(), cols=s.string() & low_cardinality).fit_transform(X) + +# %% +# Notice how we composed the selector with :func:`~skrub.selectors.string()` +# using a logical operator. This resulting selector matches string +# columns with cardinality below ``40``. +# +# We can also define the opposite selector ``high_cardinality`` using the negation +# operator ``~`` and apply a |StringEncoder| to vectorize those +# columns. +from sklearn.ensemble import HistGradientBoostingRegressor + +high_cardinality = ~low_cardinality +pipeline = make_pipeline( + ApplyToCols( + OrdinalEncoder(), + cols=s.string() & low_cardinality, + ), + ApplyToCols( + StringEncoder(), + cols=s.string() & high_cardinality, + ), + HistGradientBoostingRegressor(), +).fit(X, y) +pipeline + +# %% +# Interestingly, the pipeline above is similar to the datatype dispatching performed by +# |TableVectorizer|, also used in :func:`~skrub.tabular_pipeline`. +# +# Click on the dropdown arrows next to the datatype to see the columns are mapped to +# the different transformers in |TableVectorizer|. +from skrub import tabular_pipeline + +tabular_pipeline("regressor").fit(X, y) +# %% diff --git a/skrub/_docs/examples/0050_deduplication.py b/skrub/_docs/examples/0050_deduplication.py new file mode 100644 index 000000000..f877c6190 --- /dev/null +++ b/skrub/_docs/examples/0050_deduplication.py @@ -0,0 +1,164 @@ +""" +.. _examples_deduplication: + +=================================== +Deduplicating misspelled categories +=================================== + +Real-world datasets often come with misspellings, for instance +in manually inputted categorical variables. +Such misspellings break data analysis steps that require +exact matching, such as a ``GROUP BY`` operation. + +Merging multiple variants of the same category is known as +*deduplication*. It is implemented in skrub with the |deduplicate| function. + +Deduplication relies on *unsupervised learning*. It finds structures in +the data without providing a-priori known and explicit labels/categories. +Specifically, measuring the distance between strings can be used to +find clusters of strings that are similar to each other (e.g. differ only +by a misspelling) and hence, flag and regroup potentially +misspelled category names in an unsupervised manner. + + +.. |deduplicate| replace:: + :func:`~skrub.deduplicate` + +.. |Gap| replace:: + :class:`~skrub.GapEncoder` + +.. |MinHash| replace:: + :class:`~skrub.MinHashEncoder` +""" + +############################################################################### +# A typical use case +# ------------------ +# +# Let's take an example: +# as a data scientist, your job is to analyze the data from a hospital ward. +# In the data, we notice that in most cases, the doctor prescribes +# one of three following medications: +# "Contrivan", "Genericon" or "Zipholan". +# +# However, data entry is manual and - either because the doctor's +# handwriting was hard to decipher, or due to mistakes during input - +# there are multiple spelling mistakes in the dataset. +# +# Let's generate this example dataset: + +import numpy as np +import pandas as pd + +from skrub.datasets import make_deduplication_data + +duplicated_names = make_deduplication_data( + examples=["Contrivan", "Genericon", "Zipholan"], # our three medication names + entries_per_example=[500, 100, 1500], # their respective number of occurrences + prob_mistake_per_letter=0.05, # 5% probability of typo per letter + random_state=42, # set seed for reproducibility +) + +duplicated_names[:5] + +############################################################################### +# We then extract the unique medication names in the data and +# visualize how often they appear: + +import matplotlib.pyplot as plt + +unique_examples, counts = np.unique(duplicated_names, return_counts=True) + +plt.figure(figsize=(10, 15)) +plt.barh(unique_examples, counts) +plt.ylabel("Medication name") +plt.xlabel("Count") +plt.show() + +############################################################################### +# We clearly see the structure of the data: +# the three original medications ("Contrivan", "Genericon" and "Zipholan") +# are the most common ones, but there are many spelling mistakes or +# slight variations of the original names. +# +# The idea behind |deduplicate| is to use the fact that +# the string distance of misspelled medications will be +# closest to their original (most frequent) medication name +# - and therefore form clusters. + +############################################################################### +# Deduplication: suggest corrections of misspelled names +# ------------------------------------------------------ +# +# The |deduplicate| function uses clustering based on +# string similarities to group duplicated names. +# +# Let's deduplicate our data: + +from skrub import deduplicate + +deduplicated_data = deduplicate(duplicated_names) + +deduplicated_data[:5] + +############################################################################### +# And that's it! We now have the deduplicated data. +# +# .. topic:: Note: +# +# The number of clusters will need some adjustment depending on the data. +# If no fixed number of clusters is given, |deduplicate| tries to set it +# automatically via the +# `silhouette score `_. + +############################################################################### +# We can visualize the distribution of categories in the deduplicated data: + +deduplicated_unique_examples, deduplicated_counts = np.unique( + deduplicated_data, return_counts=True +) +deduplicated_series = pd.Series(deduplicated_counts, index=deduplicated_unique_examples) + +plt.figure(figsize=(10, 5)) +plt.barh(deduplicated_unique_examples, deduplicated_counts) +plt.xlabel("Count") +plt.ylabel("Medication name") +plt.show() + +############################################################################### +# Here, the silhouette score finds the ideal number of +# clusters (3) and groups the spelling mistakes. +# +# In practice, the translation/deduplication will often be imperfect +# and require some tweaks. +# In this case, we can construct and update a translation table based on the +# data returned by |deduplicate|. + +# create a table that maps original to corrected categories +translation_table = pd.Series(deduplicated_data, index=duplicated_names) + +# remove duplicates in the original data +translation_table = translation_table[~translation_table.index.duplicated(keep="first")] + +translation_table.head() + +############################################################################### +# In this table, we have the category name on the left, +# and the cluster it was translated to on the right. +# If we want to adapt the translation table, we can +# modify it manually. + + +############################################################################### +# Conclusion +# ---------- +# +# In this example, we have seen how to use the |deduplicate| function to +# automatically detect and correct misspelled category names. +# +# Note that deduplication is especially useful when we either +# know our ground truth (e.g. the original medication names), +# or when the similarity across strings does not +# carry useful information for our machine learning task. +# Otherwise, we prefer using encoding methods such as |Gap| +# or |MinHash|. diff --git a/skrub/_docs/examples/0100_squashing_scaler.py b/skrub/_docs/examples/0100_squashing_scaler.py new file mode 100644 index 000000000..ba6f90110 --- /dev/null +++ b/skrub/_docs/examples/0100_squashing_scaler.py @@ -0,0 +1,204 @@ +""" +SquashingScaler: Robust numerical preprocessing for neural networks +=================================================================== + +The following example illustrates the use of the :class:`~skrub.SquashingScaler`, a +transformer that can rescale and squash numerical features to a range that works well +with neural networks and perhaps also other related models. Its basic idea is to +rescale the features based on quantile statistics (to be robust to outliers), and then +perform a smooth squashing function to limit the outputs to a pre-defined range. +This transform has been found to even work well when applied to one-hot encoded +features. + +We first generate some synthetic data with outliers to show how different scalers +transform the data, then we show how the choice of the scaler affects the prediction +performance of a simple neural network. + +.. |SquashingScaler| replace:: :class:`~skrub.SquashingScaler` +.. |RobustScaler| replace:: :class:`~sklearn.preprocessing.RobustScaler` +.. |StandardScaler| replace:: :class:`~sklearn.preprocessing.StandardScaler` +.. |QuantileTransformer| replace:: :class:`~sklearn.preprocessing.QuantileTransformer` + +""" + +# %% +# Plotting the effect of different scalers +# ---------------------------------------- +# +# First, let's import the |SquashingScaler|, as well as the usual scikit-learn +# |StandardScaler| and |RobustScaler|. + +# %% +import numpy as np +from sklearn.preprocessing import QuantileTransformer, RobustScaler, StandardScaler + +from skrub import SquashingScaler + +np.random.seed(0) # for reproducibility + +# %% +# We then generate some random values sampling from a uniform distribution in the +# range ``[0, 1]``: note that this will produce values that are always positive. +# We then add some outliers in random positions in the array. +# Subtracting 50 allows to have some negative outliers in the data. + +values = np.random.rand(100, 1) +n_outliers = 15 +outlier_indices = np.random.choice(values.shape[0], size=n_outliers, replace=False) +values[outlier_indices] = np.random.rand(n_outliers, 1) * 100 - 50 + +# %% +# We then create one of each scaler and use them to scale the data independently. + +# %% +squash_scaler = SquashingScaler() +squash_scaled = squash_scaler.fit_transform(values) + +robust_scaler = RobustScaler() +robust_scaled = robust_scaler.fit_transform(values) + +standard_scaler = StandardScaler() +standard_scaled = standard_scaler.fit_transform(values) + +quantile_transformer = QuantileTransformer(n_quantiles=100) +quantile_scaled = quantile_transformer.fit_transform(values) + + +# %% +# To better show the effect of scaling, we create two plots, where we display the +# data points after sorting them in ascending order: in this way, all outliers +# are close to each other and with the proper sign. +# We create two subplots because the scale of the outliers is much larger than that +# of the inliers, which means that any detail in the inlier would be hidden. + +# %% +import matplotlib.pyplot as plt + +x = np.arange(values.shape[0]) + +fig, axs = plt.subplots(1, 2, layout="constrained", figsize=(10, 5)) + +ax = axs[0] +ax.plot(x, sorted(values), label="Original Values", linewidth=2.5) +ax.plot(x, sorted(squash_scaled), label="SquashingScaler") +ax.plot(x, sorted(robust_scaled), label="RobustScaler", linestyle="--") +ax.plot(x, sorted(standard_scaled), label="StandardScaler") +ax.plot(x, sorted(quantile_scaled), label="QuantileTransformer") + +# Add a horizontal band in [-4, +4] +ax.axhspan(-4, 4, color="gray", alpha=0.15) +ax.set(title="Original data", xlim=[0, values.shape[0]], xlabel="Percentile") +ax.legend() + +ax = axs[1] +ax.plot(x, sorted(values), label="Original Values", linewidth=2.5) +ax.plot(x, sorted(squash_scaled), label="SquashingScaler") +ax.plot(x, sorted(robust_scaled), label="RobustScaler", linestyle="--") +ax.plot(x, sorted(standard_scaled), label="StandardScaler") +ax.plot(x, sorted(quantile_scaled), label="QuantileTransformer") + +ax.set(ylim=[-4, 4]) +ax.set(title="In range [-4, 4]", xlim=[0, values.shape[0]], xlabel="Percentile") + +# Highlight the bounds of the SquashingScaler +ax.axhline(y=3, alpha=0.2) +ax.axhline(y=-3, alpha=0.2) + +fig.suptitle( + "Comparison of different scalers on sorted data with outliers", fontsize=20 +) +fig.supylabel("Value") + +# %% +# The figure on the left immediately shows how the scale of the data may be completely +# off because of a minority of outliers, with the RobustScaler following the behavior +# of the original by retaining the larger scale of the outliers. On the other hand, +# both the SquashingScaler and the StandardScaler remain roughly in the ``[-4, 4]`` +# range (highlighted in grey in the left figure). +# +# In the right figure we can then spot how the presence of outliers has completely +# flattened the curve produced by the StandardScaler, forcing the inliers to be +# very close to 0. The RobustScaler and the SquashingScaler instead follow the original +# data much more closely, after centering it on 0. +# +# Finally, the SquashingScaler performs a smooth clipping of outliers, constraining +# all values to be in the range ``[-max_absolute_value, max_absolute_value]``, +# where ``max_absolute_value`` is a parameter specified by the user (3 by default). + +# %% +# Comparing numerical pre-processing methods on a neural network +# -------------------------------------------------------------- +# +# In the second part of the example, we want to fit a neural network to predict +# employee salaries. +# The dataset contains numerical features, categorical features, text features, +# and dates. +# These features are first converted to numerical features using +# :class:`~skrub.TableVectorizer`. Since the encoded features are not normalized, +# we apply a numerical transformation to them. +# +# Finally, we fit a simple neural network and compare the R2 scores obtained with +# different numerical transformations. +# +# While we use a simple :class:`~sklearn.neural_network.MLPRegressor` here for +# simplicity, we generally recommend using better neural network implementations +# or tree-based models whenever low test errors are desired. + +# %% +# We test the :class:`~skrub.SquashingScaler` against the +# :class:`~sklearn.preprocessing.StandardScaler` and the +# :class:`~sklearn.preprocessing.QuantileTransformer` from scikit-learn. We put +# each of these together in a pipeline with a TableVectorizer and a simple MLPRegressor. +# In the end, we print the R2 scores of each fold's validation set in a three-fold +# cross-validation. + +import warnings + +import numpy as np +import pandas as pd +from sklearn.compose import TransformedTargetRegressor +from sklearn.exceptions import ConvergenceWarning +from sklearn.model_selection import cross_validate +from sklearn.neural_network import MLPRegressor +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import QuantileTransformer, StandardScaler + +from skrub import DatetimeEncoder, SquashingScaler, TableVectorizer +from skrub.datasets import fetch_employee_salaries + +np.random.seed(0) +file_path = fetch_employee_salaries().path +data = pd.read_csv(file_path) +X = data.drop(columns="current_annual_salary") +y = data["current_annual_salary"] + +for num_transformer in [ + StandardScaler(), + QuantileTransformer(output_distribution="normal", random_state=0), + SquashingScaler(), +]: + pipeline = make_pipeline( + TableVectorizer(datetime=DatetimeEncoder(periodic_encoding="circular")), + num_transformer, + TransformedTargetRegressor( + # We use lbfgs for faster convergence + MLPRegressor(solver="lbfgs", max_iter=100), + transformer=StandardScaler(), + ), + ) + with warnings.catch_warnings(): + # Ignore warnings about the MLPRegressor not converging + warnings.simplefilter("ignore", category=ConvergenceWarning) + scores = cross_validate(pipeline, X, y, cv=3, scoring="r2") + + print( + f"Cross-validation R2 scores for {num_transformer.__class__.__name__}" + f" (higher is better):\n{scores['test_score']}\n" + ) + +# %% +# On the employee salaries dataset, the |SquashingScaler| performs +# better than |StandardScaler| and |QuantileTransformer| on all +# cross-validation folds. + +# %% diff --git a/skrub/_docs/examples/01_encoding/0010_encodings.py b/skrub/_docs/examples/01_encoding/0010_encodings.py new file mode 100644 index 000000000..71a666ab2 --- /dev/null +++ b/skrub/_docs/examples/01_encoding/0010_encodings.py @@ -0,0 +1,377 @@ +""" +.. _example_encodings: + +===================================================================== +Encoding: from a dataframe to a numerical matrix for machine learning +===================================================================== + +This example shows how to transform a rich dataframe with columns of various types +into a numerical matrix on which machine-learning algorithms can be applied. +We study the case of predicting wages using the +`employee salaries `_ dataset. + +.. |TableVectorizer| replace:: + :class:`~skrub.TableVectorizer` + +.. |Pipeline| replace:: + :class:`~sklearn.pipeline.Pipeline` + +.. |OneHotEncoder| replace:: + :class:`~sklearn.preprocessing.OneHotEncoder` + +.. |GapEncoder| replace:: + :class:`~skrub.GapEncoder` + +.. |MinHashEncoder| replace:: + :class:`~skrub.MinHashEncoder` + +.. |DatetimeEncoder| replace:: + :class:`~skrub.DatetimeEncoder` + +.. |HGBR| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingRegressor` + +.. |RandomForestRegressor| replace:: + :class:`~sklearn.ensemble.RandomForestRegressor` + +.. |permutation importances| replace:: + :func:`~sklearn.inspection.permutation_importance` +""" + +############################################################################### +# Easy learning on a dataframe +# ---------------------------- +# +# Let's first retrieve the dataset, using one of the downloaders from the +# :mod:`skrub.datasets` module. As all the downloaders, +# :func:`~skrub.datasets.fetch_employee_salaries` returns a dataset with a ``path`` +# field pointing to the dataframe file, which contains both the features and the +# target. We load the dataframe from the path using pandas. +# ``X`` is a dataframe which contains the +# features (aka design matrix, explanatory variables, independent variables). +# ``y`` is a column (pandas Series) which contains the target (aka dependent, response +# variable) that we want to learn to predict from ``X``. In this case ``y`` is the +# annual salary, found in column "current_annual_salary". + +import pandas as pd + +from skrub.datasets import fetch_employee_salaries + +file_path = fetch_employee_salaries().path +employees = pd.read_csv(file_path) +X = employees.drop(columns="current_annual_salary") +y = employees["current_annual_salary"] + +############################################################################### +# Most machine-learning algorithms work with arrays of numbers. The +# challenge here is that the ``employees`` dataframe is a heterogeneous +# set of columns: some are numerical (``'year_first_hired'``), some dates +# (``'date_first_hired'``), some have a few categorical entries +# (``'gender'``), some many (``'employee_position_title'``). Therefore +# our table needs to be "vectorized": processed to extract numeric +# features. +# +# ``skrub`` provides an easy way to build a simple but reliable +# machine-learning model which includes this step, working well on most +# tabular data. + +from sklearn.model_selection import cross_validate + +from skrub import tabular_pipeline + +model = tabular_pipeline("regressor") +results = cross_validate(model, X, y) +results["test_score"] + +# %% +# The estimator returned by :obj:`tabular_pipeline` combines 2 steps: +# +# - a |TableVectorizer| to preprocess the dataframe and vectorize the features +# - a supervised learner (by default a |HGBR|) +model + +# %% +# In the rest of this example, we focus on the first step and explore the +# capabilities of skrub's |TableVectorizer|. +# +# | + +# %% +# More details on encoding tabular data +# ------------------------------------- + +from skrub import TableVectorizer + +vectorizer = TableVectorizer() +vectorized_X = vectorizer.fit_transform(X) +vectorized_X + +############################################################################### +# From our 8 columns, the |TableVectorizer| has extracted 143 numerical +# features. Most of them are one-hot encoded representations of the categorical +# features. For example, we can see that 3 columns ``'gender_F'``, ``'gender_M'``, +# ``'gender_nan'`` were created to encode the ``'gender'`` column. + +############################################################################### +# By performing appropriate transformations on our complex data, the |TableVectorizer| +# produced numeric features that we can use for machine-learning: + +from sklearn.ensemble import HistGradientBoostingRegressor + +HistGradientBoostingRegressor().fit(vectorized_X, y) + +############################################################################### +# The |TableVectorizer| bridges the gap between tabular data and machine-learning +# pipelines. It allows us to apply a machine-learning estimator to our dataframe without +# manual data wrangling and feature extraction. +# + +############################################################################### +# Inspecting the TableVectorizer +# ------------------------------ +# +# The |TableVectorizer| distinguishes between 4 basic kinds of columns (more may be +# added in the future). +# For each kind, it applies a different transformation, which we can configure. The +# kinds of columns and the default transformation for each of them are: +# +# - numeric columns: simply casting to floating-point +# - datetime columns: extracting features such as year, day, hour with the +# |DatetimeEncoder| +# - low-cardinality categorical columns: one-hot encoding +# - high-cardinality categorical columns: a simple and effective text representation +# pipeline provided by the |GapEncoder| + +vectorizer + +############################################################################### +# We can inspect which transformation was chosen for each column and retrieve the +# fitted transformer. ``vectorizer.kind_to_columns_`` provides an overview of how the +# vectorizer categorized columns in our input: + +vectorizer.kind_to_columns_ + +############################################################################### +# The reverse mapping is given by: + +vectorizer.column_to_kind_ + +############################################################################### +# ``vectorizer.transformers_`` gives us a dictionary which maps column names to the +# corresponding transformer. + +vectorizer.transformers_["date_first_hired"] + +############################################################################### +# We can also see which features in the vectorizer's output were derived from a given +# input column. + +vectorizer.input_to_outputs_["date_first_hired"] + +############################################################################### + +vectorized_X[vectorizer.input_to_outputs_["date_first_hired"]] + +############################################################################### +# Finally, we can go in the opposite direction: given a column in the input, find out +# from which input column it was derived. + +vectorizer.output_to_input_["department_BOA"] + + +############################################################################### +# Dataframe preprocessing +# ~~~~~~~~~~~~~~~~~~~~~~~ +# +# Note that ``"date_first_hired"`` has been recognized and processed as a datetime +# column. + +vectorizer.column_to_kind_["date_first_hired"] + +############################################################################### +# But looking closer at our original dataframe, it was encoded as a string. + +X["date_first_hired"] + +############################################################################### +# Note the ``dtype: object`` in the output above. +# Before applying the transformers we specify, the |TableVectorizer| performs a few +# preprocessing steps. +# +# For example, strings commonly used to represent missing values such as ``"N/A"`` are +# replaced with actual ``null``. As we saw above, columns containing strings that +# represent dates (e.g. ``'2024-05-15'``) are detected and converted to proper +# datetimes. +# +# We can inspect the list of steps that were applied to a given column: + +vectorizer.all_processing_steps_["date_first_hired"] + +############################################################################### +# These preprocessing steps depend on the column: + +vectorizer.all_processing_steps_["department"] + +############################################################################### + + +############################################################################### +# A simple Pipeline for tabular data +# ---------------------------------- +# +# The |TableVectorizer| outputs data that can be understood by a scikit-learn +# estimator. Therefore we can easily build a 2-step scikit-learn ``Pipeline`` +# that we can fit, test or cross-validate and that works well on tabular data. + +import numpy as np +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.model_selection import cross_validate +from sklearn.pipeline import make_pipeline + +pipeline = make_pipeline(TableVectorizer(), HistGradientBoostingRegressor()) + +results = cross_validate(pipeline, X, y) +scores = results["test_score"] +print(f"R2 score: mean: {np.mean(scores):.3f}; std: {np.std(scores):.3f}") +print(f"mean fit time: {np.mean(results['fit_time']):.3f} seconds") + +############################################################################### +# Specializing the TableVectorizer for HistGradientBoosting +# --------------------------------------------------------- +# +# The encoders used by default by the |TableVectorizer| are safe choices for a wide +# range of downstream estimators. If we know we want to use it with a |HGBR| (or +# classifier) model, we can make some different choices that are only well-suited for +# tree-based models but can yield a faster pipeline. +# We make 2 changes. +# +# The |HGBR| has built-in support for categorical features, so we do not need to one-hot +# encode them. +# We do need to tell it which features should be treated as categorical with the +# ``categorical_features`` parameter. In recent versions of scikit-learn, we can set +# ``categorical_features='from_dtype'``, and it will treat all columns in the input that +# have a ``Categorical`` dtype as such. Therefore we change the encoder for +# low-cardinality columns: instead of ``OneHotEncoder``, we use skrub's +# ``ToCategorical``. This transformer will simply ensure our columns have an actual +# ``Categorical`` dtype (as opposed to string for example), so that they can be +# recognized by the |HGBR|. +# +# The second change replaces the |GapEncoder| with a |MinHashEncoder|. +# The |GapEncoder| is a topic model. +# It produces interpretable embeddings in a vector space where distances are meaningful, +# which is great for interpretation and necessary for some downstream supervised +# learners such as linear models. However fitting the topic model is costly in +# computation time and memory. The |MinHashEncoder| produces features that are not easy +# to interpret, but that decision trees can efficiently use to test for the occurrence +# of particular character n-grams (more details are provided in its documentation). +# Therefore it can be a faster and very effective alternative, when the supervised +# learner is built on top of decision trees, which is the case for the |HGBR|. +# +# The resulting pipeline is identical to the one produced by default by +# :obj:`tabular_pipeline`. + +from skrub import MinHashEncoder, ToCategorical + +vectorizer = TableVectorizer( + low_cardinality=ToCategorical(), high_cardinality=MinHashEncoder() +) +pipeline = make_pipeline( + vectorizer, HistGradientBoostingRegressor(categorical_features="from_dtype") +) + +results = cross_validate(pipeline, X, y) +scores = results["test_score"] +print(f"R2 score: mean: {np.mean(scores):.3f}; std: {np.std(scores):.3f}") +print(f"mean fit time: {np.mean(results['fit_time']):.3f} seconds") + +############################################################################### +# We can see that this new pipeline achieves a similar score but is fitted much faster. +# This is mostly due to replacing |GapEncoder| with |MinHashEncoder| (however this makes +# the features less interpretable). + +############################################################################### +# Feature importances in the statistical model +# -------------------------------------------- +# +# As we just saw, we can fit a |MinHashEncoder| faster than a |GapEncoder|. However, the +# |GapEncoder| has a crucial advantage: each dimension of its output space is associated +# with a topic which can be inspected and interpreted. +# In this section, after training a regressor, we will plot the feature importances. +# +# .. topic:: Note: +# +# To minimize computation time, we use the feature importances computed by the +# |RandomForestRegressor|, but you should prefer |permutation importances| +# instead (which are less subject to biases). +# +# First, we train another scikit-learn regressor, the |RandomForestRegressor|: + +from sklearn.ensemble import RandomForestRegressor + +vectorizer = TableVectorizer() # now using the default GapEncoder +regressor = RandomForestRegressor(n_estimators=50, max_depth=20, random_state=0) + +pipeline = make_pipeline(vectorizer, regressor) +pipeline.fit(X, y) + +############################################################################### +# We are retrieving the feature importances: + +avg_importances = regressor.feature_importances_ +std_importances = np.std( + [tree.feature_importances_ for tree in regressor.estimators_], axis=0 +) +indices = np.argsort(avg_importances)[::-1] + +############################################################################### +# And plotting the results: + +import matplotlib.pyplot as plt + +top_indices = indices[:20] +labels = vectorizer.get_feature_names_out()[top_indices] + +plt.figure(figsize=(12, 9)) +plt.barh( + y=labels, + width=avg_importances[top_indices], + xerr=std_importances[top_indices], + ecolor="k", + color="b", + alpha=0.5, +) +plt.yticks(fontsize=15) +plt.title("Feature importances") +plt.tight_layout(pad=1) +plt.show() + +############################################################################### +# The |GapEncoder| creates feature names that show the first 3 most important words in +# the topic associated with each feature. As we can see in the plot above, this helps +# inspecting the model. If we had used a |MinHashEncoder| instead, the features would be +# much less helpful, with names such as ``employee_position_title_0``, +# ``employee_position_title_1``, etc. + +############################################################################### +# We can see that features such the time elapsed since being hired, having a full-time +# employment, and the position, seem to be the most informative for prediction. However, +# feature importances must not be over-interpreted -- they capture statistical +# associations `rather than causal effects +# `_. Moreover, the +# fast feature importance method used here suffers from biases favouring features with +# larger cardinality, as illustrated in a scikit-learn `example +# `_. +# In general we should prefer |permutation importances|, but it is a slower method. + +############################################################################### +# Conclusion +# ---------- +# +# In this example, we motivated the need for a simple machine learning +# pipeline, which we built using the |TableVectorizer| and a +# |HGBR|. +# +# We saw that by default, it works well on a heterogeneous dataset. +# +# To better understand our dataset, and without much effort, we were also able +# to plot the feature importances. diff --git a/skrub/_docs/examples/01_encoding/0020_text_with_string_encoders.py b/skrub/_docs/examples/01_encoding/0020_text_with_string_encoders.py new file mode 100644 index 000000000..2d530a5cd --- /dev/null +++ b/skrub/_docs/examples/01_encoding/0020_text_with_string_encoders.py @@ -0,0 +1,346 @@ +""" +.. _example_string_encoders: + +===================================================== +Various string encoders: a sentiment analysis example +===================================================== + +In this example, we explore the performance of string and categorical encoders +available in skrub. + +.. |GapEncoder| replace:: + :class:`~skrub.GapEncoder` + +.. |MinHashEncoder| replace:: + :class:`~skrub.MinHashEncoder` + +.. |TextEncoder| replace:: + :class:`~skrub.TextEncoder` + +.. |StringEncoder| replace:: + :class:`~skrub.StringEncoder` + +.. |TableReport| replace:: + :class:`~skrub.TableReport` + +.. |TableVectorizer| replace:: + :class:`~skrub.TableVectorizer` + +.. |pipeline| replace:: + :class:`~sklearn.pipeline.Pipeline` + +.. |HistGradientBoostingClassifier| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingClassifier` + +.. |RandomizedSearchCV| replace:: + :class:`~sklearn.model_selection.RandomizedSearchCV` + +.. |GridSearchCV| replace:: + :class:`~sklearn.model_selection.GridSearchCV` +""" + +# %% +# The Toxicity dataset +# -------------------- +# We focus on the toxicity dataset, a corpus of 1,000 tweets, evenly balanced +# between the binary labels "Toxic" and "Not Toxic". +# Our goal is to classify each entry between these two labels, using only the +# text of the tweets as features. +import pandas as pd + +from skrub.datasets import fetch_toxicity + +# %% +# We load the dataset from the path using pandas. +file_path = fetch_toxicity().path + +X = pd.read_csv(file_path) + +# %% +# When it comes to displaying large chunks of text, the |TableReport| is especially +# useful! Click on any cell below to expand and read the tweet in full. +from skrub import TableReport + +TableReport(X) + +# %% +# We prepare the target variable by mapping the binary labels "Toxic" and "Not Toxic" +# to 1 and 0, respectively. The target is reused throughout the example. + +y = X.pop("is_toxic").map({"Toxic": 1, "Not Toxic": 0}) + +# %% +# GapEncoder +# ^^^^^^^^^^ +# First, let's vectorize our text column using the |GapEncoder|, one of the +# `high cardinality categorical encoders `_ +# provided by skrub. +# As introduced in the :ref:`previous example`, the |GapEncoder| +# performs matrix factorization for topic modeling. It builds latent topics by +# capturing combinations of substrings that frequently co-occur, and encoded vectors +# correspond to topic activations. +# +# To interpret these latent topics, we select for each of them a few labels from +# the input data with the highest activations. In the example below we select 3 labels +# to summarize each topic. +from skrub import GapEncoder + +gap = GapEncoder(n_components=30) +X_trans = gap.fit_transform(X["text"]) +# Add the original text as a first column +X_trans.insert(0, "text", X["text"]) +TableReport(X_trans) + +# %% +# We can use a heatmap to highlight the highest activations, making them more visible +# for comparison against the original text and vectors above. + +import numpy as np +from matplotlib import pyplot as plt + + +def plot_gap_feature_importance(X_trans): + x_samples = X_trans.pop("text") + + # We slightly format the topics and labels for them to fit on the plot. + topic_labels = [x.replace("text: ", "") for x in X_trans.columns] + labels = x_samples.str[:50].values + "..." + + # We clip large outliers to make activations more visible. + X_trans = np.clip(X_trans, a_min=None, a_max=200) + + plt.figure(figsize=(10, 10), dpi=200) + plt.imshow(X_trans.T) + + plt.yticks( + range(len(topic_labels)), + labels=topic_labels, + ha="right", + size=12, + ) + plt.xticks(range(len(labels)), labels=labels, size=12, rotation=50, ha="right") + + plt.colorbar().set_label(label="Topic activations", size=13) + plt.ylabel("Latent topics", size=14) + plt.xlabel("Data entries", size=14) + plt.tight_layout() + plt.show() + + +plot_gap_feature_importance(X_trans.head()) + +# %% +# Now that we have an understanding of the vectors produced by the |GapEncoder|, +# let's evaluate its performance in toxicity classification. The |GapEncoder| excels +# at handling categorical columns with high cardinality, but here the column consists +# of free-form text. Sentences are generally longer, with more unique ngrams than +# high cardinality categories. +# +# To benchmark the performance of the |GapEncoder| against the toxicity dataset, +# we integrate it into a |TableVectorizer|, as introduced in the +# :ref:`previous example`, +# and create a |pipeline| by appending a |HistGradientBoostingClassifier|, which +# consumes the vectors produced by the |GapEncoder|. +# +# We set ``n_components`` to 30; however, to achieve the best performance, we would +# need to find the optimal value for this hyperparameter using either |GridSearchCV| +# or |RandomizedSearchCV|. We skip this part to keep the computation time for this +# small example. +# +# Recall that the ROC AUC is a metric that quantifies the ranking power of estimators, +# where a random estimator scores 0.5, and an oracle —providing perfect predictions— +# scores 1. +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.model_selection import cross_validate +from sklearn.pipeline import make_pipeline + +from skrub import TableVectorizer + + +def plot_box_results(named_results): + fig, ax = plt.subplots() + names, scores = zip( + *[(name, result["test_score"]) for name, result in named_results] + ) + ax.boxplot(scores) + ax.set_xticks(range(1, len(names) + 1), labels=list(names), size=12) + ax.set_ylabel("ROC AUC", size=14) + plt.title( + "AUC distribution across folds (higher is better)", + size=14, + ) + plt.show() + + +results = [] + +# %% +# Now we can evaluate the performance of the |GapEncoder| in toxicity classification. + +gap_pipe = make_pipeline( + TableVectorizer(high_cardinality=GapEncoder(n_components=30)), + HistGradientBoostingClassifier(), +) +gap_results = cross_validate(gap_pipe, X, y, scoring="roc_auc") +results.append(("GapEncoder", gap_results)) + +plot_box_results(results) + +# %% +# MinHashEncoder +# ^^^^^^^^^^^^^^ +# We now compare these results with the |MinHashEncoder|, which is faster +# and produces vectors better suited for tree-based estimators like +# |HistGradientBoostingClassifier|. To do this, we can simply replace +# the |GapEncoder| with the |MinHashEncoder| in the previous pipeline +# using ``set_params()``. + +from skrub import MinHashEncoder + +minhash_pipe = make_pipeline( + TableVectorizer(high_cardinality=MinHashEncoder(n_components=30)), + HistGradientBoostingClassifier(), +) +minhash_results = cross_validate(minhash_pipe, X, y, scoring="roc_auc") +results.append(("MinHashEncoder", minhash_results)) + +plot_box_results(results) + +# %% +# Remarkably, the vectors produced by the |MinHashEncoder| offer less predictive +# power than those from the |GapEncoder| on this dataset. +# +# TextEncoder +# ^^^^^^^^^^^ +# Let's now shift our focus to pre-trained deep learning encoders. Our previous +# encoders are syntactic models that we trained directly on the toxicity dataset. +# To generate more powerful vector representations for free-form text and diverse +# entries, we can instead use semantic models, such as BERT, which have been trained +# on very large datasets. +# +# |TextEncoder| enables you to integrate any Sentence Transformer model from the +# Hugging Face Hub (or from your local disk) into your |pipeline| to transform a text +# column in a dataframe. By default, |TextEncoder| uses the e5-small-v2 model. +from skrub import TextEncoder + +text_encoder = TextEncoder( + "sentence-transformers/paraphrase-albert-small-v2", + device="cpu", +) + +text_encoder_pipe = make_pipeline( + TableVectorizer(high_cardinality=text_encoder), + HistGradientBoostingClassifier(), +) +text_encoder_results = cross_validate(text_encoder_pipe, X, y, scoring="roc_auc") +results.append(("TextEncoder", text_encoder_results)) + +plot_box_results(results) + +# %% +# StringEncoder +# ^^^^^^^^^^^^^ +# |TextEncoder| embeddings are very strong, but they are also quite expensive to +# use. A simpler, faster alternative for encoding strings is the |StringEncoder|, +# which works by first performing a tf-idf (computing vectors of rescaled word +# counts of the text `wiki `_), and then +# following it with TruncatedSVD to reduce the number of dimensions to, in this +# case, 30. +from skrub import StringEncoder + +string_encoder = StringEncoder(ngram_range=(3, 4), analyzer="char_wb", random_state=0) + +string_encoder_pipe = make_pipeline( + TableVectorizer(high_cardinality=string_encoder), + HistGradientBoostingClassifier(), +) + +string_encoder_results = cross_validate(string_encoder_pipe, X, y, scoring="roc_auc") +results.append(("StringEncoder", string_encoder_results)) + +plot_box_results(results) + + +# %% +# The performance of the |TextEncoder| is significantly stronger than that of +# the syntactic encoders, which is expected. But how long does it take to load +# and vectorize text on a CPU using a Sentence Transformer model? Below, we display +# the tradeoff between predictive accuracy and training time. Note that since we are +# not training the Sentence Transformer model, the "fitting time" refers to the +# time taken for vectorization. + + +def plot_performance_tradeoff(results): + fig, ax = plt.subplots(figsize=(5, 4), dpi=200) + markers = ["s", "o", "^", "x"] + for idx, (name, result) in enumerate(results): + ax.scatter( + result["fit_time"], + result["test_score"], + label=name, + marker=markers[idx], + ) + mean_fit_time = np.mean(result["fit_time"]) + mean_score = np.mean(result["test_score"]) + ax.scatter( + mean_fit_time, + mean_score, + color="k", + marker=markers[idx], + ) + std_fit_time = np.std(result["fit_time"]) + std_score = np.std(result["test_score"]) + ax.errorbar( + x=mean_fit_time, + y=mean_score, + yerr=std_score, + fmt="none", + c="k", + capsize=2, + ) + ax.errorbar( + x=mean_fit_time, + y=mean_score, + xerr=std_fit_time, + fmt="none", + c="k", + capsize=2, + ) + ax.set_xscale("log") + + ax.set_xlabel("Time to fit (seconds)") + ax.set_ylabel("ROC AUC") + ax.set_title("Prediction performance / training time trade-off") + + ax.annotate( + "Best time / \nperformance trade-off", + xy=(0.05, 0.95), + xycoords="axes fraction", + xytext=(0.2, 0.8), + textcoords="axes fraction", + arrowprops=dict(arrowstyle="->", lw=1.5, mutation_scale=15), + ) + ax.legend(bbox_to_anchor=(1.02, 0.3)) + plt.show() + + +plot_performance_tradeoff(results) + +# %% +# The black points represent the average time to fit and AUC for each vectorizer, +# and the width of the bars represents one standard deviation. +# +# The green outlier dot on the right side of the plot corresponds to the first time +# the Sentence Transformers model was downloaded and loaded into memory. +# During the subsequent cross-validation iterations, the model is simply copied, +# which reduces computation time for the remaining folds. +# +# Interestingly, |StringEncoder| has a performance remarkably similar to that of +# |GapEncoder|, while being significantly faster. +# +# Conclusion +# ---------- +# In conclusion, |TextEncoder| provides powerful vectorization for text, but at +# the cost of longer computation times and the need for additional dependencies, +# such as torch. |StringEncoder| represents a simpler alternative that can provide +# good performance at a fraction of the cost of more complex methods. diff --git a/skrub/_docs/examples/01_encoding/0030_datetime_encoder.py b/skrub/_docs/examples/01_encoding/0030_datetime_encoder.py new file mode 100644 index 000000000..1b339ac51 --- /dev/null +++ b/skrub/_docs/examples/01_encoding/0030_datetime_encoder.py @@ -0,0 +1,355 @@ +""" +.. _example_datetime_encoder : + +=================================================== +Handling datetime features with the DatetimeEncoder +=================================================== + +In this example, we illustrate how to better integrate datetime features +in machine learning models with the |DatetimeEncoder|. + +This encoder breaks down passed datetime features into relevant numerical +features, such as the month, the day of the week, the hour of the day, etc. + +It is used by default in the |TableVectorizer|. + + +.. |DatetimeEncoder| replace:: + :class:`~skrub.DatetimeEncoder` + +.. |TableVectorizer| replace:: + :class:`~skrub.TableVectorizer` + +.. |OneHotEncoder| replace:: + :class:`~sklearn.preprocessing.OneHotEncoder` + +.. |TimeSeriesSplit| replace:: + :class:`~sklearn.model_selection.TimeSeriesSplit` + +.. |ColumnTransformer| replace:: + :class:`~sklearn.compose.ColumnTransformer` + +.. |make_column_transformer| replace:: + :class:`~sklearn.compose.make_column_transformer` + +.. |RidgeCV| replace:: + :class:`~sklearn.linear_model.RidgeCV` + +.. |SimpleImputer| replace:: + :class:`~sklearn.impute.SimpleImputer` + +.. |StandardScaler| replace:: + :class:`~sklearn.preprocessing.StandardScaler` + +.. |ToDatetime| replace:: + :class:`~skrub.ToDatetime` +""" + +# %% +# A problem with relevant datetime features +# ----------------------------------------- +# +# We will use a dataset of bike sharing demand in 2011 and 2012. +# In this setting, we want to predict the number of bike rentals, based +# on the date, time and weather conditions. + +from pprint import pprint + +import pandas as pd + +from skrub import datasets + +file_path = datasets.fetch_bike_sharing().path +data = pd.read_csv(file_path) + +# Extract our input data (X) and the target column (y) +y = data["cnt"] +X = data[["date", "holiday", "temp", "hum", "windspeed", "weathersit"]] + +X + +# %% +y + +############################################################################### +# We convert the dataframe's ``"date"`` column using |ToDatetime|. + +from skrub import ToDatetime + +date = ToDatetime().fit_transform(X["date"]) + +print("original dtype:", X["date"].dtypes, "\n\nconverted dtype:", date.dtypes) + +############################################################################### +# Encoding the features +# ..................... +# +# We now encode this column with a |DatetimeEncoder|. +# +# During the instantiation of the |DatetimeEncoder|, we specify that we want +# don't want to extract features with a resolution finer than hours. This is +# because we don't want to extract minutes, seconds and lower units, as they +# are unimportant here. + +from skrub import DatetimeEncoder + +# DatetimeEncoder has "hour" as default resolution +date_enc = DatetimeEncoder().fit_transform(date) + +print(date, "\n\nHas been encoded as:\n\n", date_enc) + +############################################################################### +# We see that the encoder is working as expected: the column has +# been replaced by features extracting the month, day, hour, day of the +# week and total seconds since Epoch information. + +############################################################################### +# One-liner with the TableVectorizer +# .................................. +# +# As mentioned earlier, the |TableVectorizer| makes use of the +# |DatetimeEncoder| by default. Note that ``X["date"]`` is still +# a string, but will be automatically transformed into a datetime in the +# |TableVectorizer|. + +from skrub import TableVectorizer + +table_vec = TableVectorizer().fit(X) +pprint(table_vec.get_feature_names_out()) + +############################################################################### +# If we want to customize the |DatetimeEncoder| inside the |TableVectorizer|, +# we can replace its default parameter with a new, custom instance. +# +# Here, for example, we want it to extract the day of the week: + +# use the ``datetime`` argument to use a custom DatetimeEncoder in the TableVectorizer +table_vec_weekday = TableVectorizer(datetime=DatetimeEncoder(add_weekday=True)).fit(X) +pprint(table_vec_weekday.get_feature_names_out()) + +############################################################################### +# .. note: +# For more information on how to customize the |TableVectorizer|, see +# :ref:`sphx_glr_auto_examples_0010_dirty_categories.py`. +# +# Inspecting the |TableVectorizer| further, we can check that the +# |DatetimeEncoder| is used on the correct column(s). +pprint(table_vec_weekday.transformers_) + +############################################################################### +# +# Feature engineering for linear models +# .................................................................... +# +# The |DatetimeEncoder| can generate additional periodic features. These are +# particularly useful for linear models. This is controlled by the +# ``periodic encoding`` parameter which can be either ``circular`` or ``spline``, +# for trigonometric functions or B-Splines respectively. In this example, we use +# ``spline``. +# We can also add the day in the year with the parameter ``add_day_of_year``. + +table_vec_periodic = TableVectorizer( + datetime=DatetimeEncoder( + add_weekday=True, periodic_encoding="spline", add_day_of_year=True + ) +).fit(X) + +############################################################################### +# Prediction with datetime features +# --------------------------------- +# +# For prediction tasks, we recommend using the |TableVectorizer| inside a +# pipeline, combined with a model that can use the features extracted by the +# |DatetimeEncoder|. +# Here we'll use a |RidgeCV| model as our learner. We also fill null values with +# |SimpleImputer| and then rescale numeric features with |StandardScaler|. +# To test the effect of different datetime encodings on the linear model, we train +# three separate pipelines. + +from sklearn.impute import SimpleImputer +from sklearn.linear_model import RidgeCV +from sklearn.pipeline import make_pipeline +from sklearn.preprocessing import StandardScaler + +# Base pipeline with default DatetimeEncoder parameters +pipeline = make_pipeline(table_vec, StandardScaler(), SimpleImputer(), RidgeCV()) +# Datetime encoder with weekday feature +pipeline_weekday = make_pipeline( + table_vec_weekday, StandardScaler(), SimpleImputer(), RidgeCV() +) +# Datetime encoder with periodic features +pipeline_periodic = make_pipeline( + table_vec_periodic, StandardScaler(), SimpleImputer(), RidgeCV() +) + +############################################################################### +# Evaluating the model +# .................... +# +# When using date and time features, we often care about predicting the future. +# In this case, we have to be careful when evaluating our model, because +# the standard settings of the cross-validation do not respect time ordering. +# +# Instead, we can use the |TimeSeriesSplit|, +# which ensures that the test set is always in the future. +from sklearn.model_selection import TimeSeriesSplit, cross_val_score + +score_base = cross_val_score( + pipeline, + X, + y, + scoring="neg_root_mean_squared_error", + cv=TimeSeriesSplit(n_splits=5), +) + +score_weekday = cross_val_score( + pipeline_weekday, + X, + y, + scoring="neg_root_mean_squared_error", + cv=TimeSeriesSplit(n_splits=5), +) + +score_periodic = cross_val_score( + pipeline_periodic, + X, + y, + scoring="neg_root_mean_squared_error", + cv=TimeSeriesSplit(n_splits=5), +) + +print(f"Base transformer - Mean RMSE : {-score_base.mean():.2f}") +print(f"Transformer with weekday - Mean RMSE : {-score_weekday.mean():.2f}") +print(f"Transformer with periodic features - Mean RMSE : {-score_periodic.mean():.2f}") + +############################################################################### +# As expected for linear models, introducing the periodic features improved +# the RMSE by a noticeable amount. + +############################################################################### +# Plotting the prediction +# ....................... +# +# The mean squared error is not obvious to interpret, so we visually +# compare the prediction of our model with the actual values. +# To do so, we will divide our dataset into a train and a test set: +# we use 2011 data to predict what happened in 2012. +import matplotlib.dates as mdates +import matplotlib.pyplot as plt + +mask_train = X["date"] < "2012-01-01" +X_train, X_test = X.loc[mask_train], X.loc[~mask_train] +y_train, y_test = y.loc[mask_train], y.loc[~mask_train] + +pipeline.fit(X_train, y_train) +y_pred = pipeline.predict(X_test) + +pipeline_weekday.fit(X_train, y_train) +y_pred_weekday = pipeline_weekday.predict(X_test) + +pipeline_periodic.fit(X_train, y_train) +y_pred_periodic = pipeline_periodic.predict(X_test) + +X_plot = pd.to_datetime(X.tail(96)["date"]).values +X_test_plot = pd.to_datetime(X_test.tail(96)["date"]).values + +fig, ax = plt.subplots(figsize=(12, 3)) +fig.suptitle("Predictions with linear models") +ax.plot( + X_plot, + y.tail(96).values, + "x-", + alpha=0.2, + label="Actual demand", + color="black", +) +ax.plot( + X_test_plot, + y_pred[-96:], + "x-", + label="DatetimeEncoder() + RidgeCV prediction", +) +ax.plot( + X_test_plot, + y_pred_periodic[-96:], + "x-", + label='DatetimeEncoder(periodic_encoding="spline") + RidgeCV prediction', +) + + +ax.xaxis.set_major_locator(mdates.DayLocator()) +ax.xaxis.set_minor_locator( + mdates.HourLocator( + [0, 6, 12, 18], + ) +) + +# Major formatter: format date as "YYYY-MM-DD" +ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d")) +# # Minor formatter: format time as "HH:MM" +ax.xaxis.set_minor_formatter(mdates.DateFormatter("%H:%M")) + +ax.tick_params(axis="x", labelsize=7, labelrotation=75) +ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d")) +_ = fig.legend(loc="upper left") +plt.tight_layout() +plt.show() +# %% +############################################################################### +# As we can see, the base RidgeCV model struggles to learn the the pattern well +# enough, while the model that is trained on the additional periodic features +# follows the actual demand more accurately. + + +############################################################################### +# Feature importances +# ------------------- +# +# Using the |DatetimeEncoder| allows us to better understand how the date +# impacts the bike sharing demand. To this aim, we can use the function +# :func:`~sklearn.inspection.permutation_importance` to shuffle the features +# created by the |DatetimeEncoder| and measure their importance by observing +# how the model changes its prediction. + + +############################################################################### +from sklearn.inspection import permutation_importance + +# In this case, we don't use the whole pipeline, because we want to compute the +# importance of the features created by the DatetimeEncoder +X_test_transform = pipeline_periodic[:-1].transform(X_test) +result = permutation_importance( + pipeline_periodic[-1], X_test_transform, y_test, n_repeats=10, random_state=0 +) + +result = pd.DataFrame( + dict( + feature_names=pipeline_periodic[0].all_outputs_, + std=result.importances_std, + importances=result.importances_mean, + ) +).sort_values("importances", ascending=True) + +result.plot.barh( + y="importances", + x="feature_names", + title="Feature Importances", + xerr="std", + figsize=(12, 9), +) +plt.tight_layout() +plt.show() + +# %% +# We can clearly see that some of the hour splines (``date_hour_spline_18``, +# ``date_hour_spline_9``) are more important than other features, likely due to +# the fact that they match rush hours in the day. Other features, such as the +# temperature, the month, and the humidity are more important than others. +# +# Conclusion +# ---------- +# +# In this example, we saw how to use the |DatetimeEncoder| to create +# features from a datetime column. +# Also check out the |TableVectorizer|, which automatically recognizes +# and transforms datetime columns by default. diff --git a/skrub/_docs/examples/01_encoding/GALLERY_HEADER.rst b/skrub/_docs/examples/01_encoding/GALLERY_HEADER.rst new file mode 100644 index 000000000..d79d44f0a --- /dev/null +++ b/skrub/_docs/examples/01_encoding/GALLERY_HEADER.rst @@ -0,0 +1,2 @@ +Encoding features +================= diff --git a/skrub/_docs/examples/02_data_ops/1120_multiple_tables.py b/skrub/_docs/examples/02_data_ops/1120_multiple_tables.py new file mode 100644 index 000000000..cb035b11f --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1120_multiple_tables.py @@ -0,0 +1,246 @@ +""" +Multiples tables: building machine learning pipelines with DataOps +================================================================== + +In this example, we show how to build a DataOps plan to handle +pre-processing, validation and hyperparameter tuning of a dataset with **multiple +tables**. + +We consider the credit fraud dataset, which contains two tables: one for +baskets (orders) and one for products. The goal is to predict whether a basket +(a single order that has been placed with the website) is fraudulent or not, +based on the products it contains. + +.. currentmodule:: skrub + +.. |choose_from| replace:: :func:`skrub.choose_from` +.. |choose_int| replace:: :func:`skrub.choose_int` +.. |choose_float| replace:: :func:`skrub.choose_float` +.. |MinHashEncoder| replace:: :class:`~skrub.MinHashEncoder` +.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |var| replace:: :func:`skrub.var` +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |HistGradientBoostingClassifier| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingClassifier` +.. |make_randomized_search| replace:: :func:`~skrub.DataOp.skb.make_randomized_search` +.. |RocCurveDisplay| replace:: :class:`~sklearn.metrics.RocCurveDisplay` + + +""" + +# %% +# The credit fraud dataset +# ------------------------ +# +# We fetch the credit fraud dataset using ``fetch_credit_fraud``. This dataset +# contains two tables: ``baskets`` and ``products``. We load the training split +# of the dataset to train the model. At the end of the example, we will load +# the test split to evaluate the model on unseen data. + +# %% +import pandas as pd + +import skrub +import skrub.datasets + +# Small display detail: open the graphs by default in the visualizations shown +# in the rest of this notebook. +skrub.set_config(data_ops_open_graph_dropdown=True) + +dataset = skrub.datasets.fetch_credit_fraud(split="train") + +# %% +# We define two skrub variables that store the content of the two csv +# files. These variables will be used as inputs to the DataOps plan we will build. +# Later, when we want to apply the resulting model to new data, we will need to +# provide dataframes to the same variables, but with the content of the test split +# of the dataset instead. +baskets = skrub.var("baskets", pd.read_csv(dataset.baskets_path)) +products = skrub.var("products", pd.read_csv(dataset.products_path)) + +# %% +# Now we can use the |TableReport| provided by the Data Ops to inspect the two tables. +# The ``baskets`` table contains the list of basket IDs, and a fraud flag indicating +# whether the basket is fraudulent or not. +baskets +# %% +# We mark the "ID" column of the ``baskets`` table as ``X``, and the +# ``"fraud_flag"`` column as ``y``. This allows the Data Ops to track the indices +# of the variables when splitting for cross-validation. +# so that DataOps can use their indices for train-test splitting and cross-validation. +basket_ids = baskets[["ID"]].skb.mark_as_X() +fraud_flags = baskets["fraud_flag"].skb.mark_as_y() +# %% +# The ``products`` table contains information about the products that have been +# purchased, and the basket they belong to. A basket contains at least one product. +# Products can be associated with the corresponding basket through the "basket_ID" +# column. + +# %% +products +# %% +# A data-processing challenge +# ---------------------------- +# The general structure of the DataOps plan we want to build looks like this: +# +# .. image:: ../../_static/credit_fraud_diagram.svg +# :width: 300 +# +# We want to fit a |HistGradientBoostingClassifier| to predict the fraud +# flag (y). However, since the features for each basket are stored in +# the products table, we need to extract these features, aggregate them +# at the basket level, and merge the result with the basket data. +# +# .. admonition:: Why building a pipeline for this is hard +# :collapsible: closed +# +# We can use the |TableVectorizer| to vectorize the products, but we +# then need to aggregate the resulting vectors to obtain a single row per basket. +# Using a scikit-learn Pipeline is tricky because the |TableVectorizer| would be +# fitted on a table with a different number of rows than the target y (the baskets +# table), which scikit-learn does not allow. +# +# While we could fit the |TableVectorizer| manually, this would forfeit +# scikit-learn’s tooling for managing transformations, storing fitted estimators, +# splitting data, cross-validation, and hyper-parameter tuning. +# We would also have to handle the aggregation and join ourselves, likely with +# error-prone Pandas code. +# +# Fortunately, skrub DataOps provide a powerful alternative for building flexible +# plans that address these problems. + +# %% +# Building a multi-table DataOps plan +# ------------------------------------ +# Since our DataOps expect dataframes for products, baskets and fraud +# flags, we manipulate those objects as we would manipulate pandas dataframes. +# For instance, we filter products to keep only those that match one of the +# baskets in the ``baskets`` table, and then add a column containing the total +# amount for each kind of product in a basket: +# %% +kept_products = products[products["basket_ID"].isin(basket_ids["ID"])] +products_with_total = kept_products.assign( + total_price=kept_products["Nbr_of_prod_purchas"] * kept_products["cash_price"] +) +products_with_total + +# %% +# We then build a skrub |TableVectorizer| with different choices of +# the type of encoder for high-cardinality categorical or string columns, and +# the number of components it uses. +# +# With skrub, there’s no need to specify a separate grid of hyperparameters outside +# the pipeline. +# Instead, within a DataOps plan, we can directly replace a parameter’s value using +# one of skrub’s ``choose_*`` functions, which define the range of values to consider +# during hyperparameter selection. In this example, we use |choose_int| to select +# the number of components for the encoder and |choose_from| to select the type +# of encoder. + +# %% +n = skrub.choose_int(5, 15, name="n_components") +encoder = skrub.choose_from( + { + "MinHash": skrub.MinHashEncoder(n_components=n), + "LSA": skrub.StringEncoder(n_components=n), + }, + name="encoder", +) +vectorizer = skrub.TableVectorizer(high_cardinality=encoder) + +# %% +# We can restrict the vectorizer to a subset of columns: in our case, we want to +# vectorize all columns except the ``"basket_ID"`` column, which is not a +# feature but a link to the basket it belongs to. + +# %% +vectorized_products = products_with_total.skb.apply( + vectorizer, exclude_cols="basket_ID" +) + +# %% +# We then aggregate the vectorized products by basket ID, and then merge the result +# with the baskets table. + +# %% +aggregated_products = vectorized_products.groupby("basket_ID").agg("mean").reset_index() +augmented_baskets = basket_ids.merge( + aggregated_products, left_on="ID", right_on="basket_ID" +).drop(columns=["ID", "basket_ID"]) + +# %% +# Finally, we add a supervised estimator, and use |choose_float| to +# add the learning rate as a hyperparameter to tune. + +# %% +from sklearn.ensemble import HistGradientBoostingClassifier + +hgb = HistGradientBoostingClassifier( + learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="learning_rate") +) +predictions = augmented_baskets.skb.apply(hgb, y=fraud_flags) +predictions + +# %% +# And our DataOps plan is complete! +# +# We can now use |make_randomized_search| to perform hyperparameter +# tuning and find the best hyperparameters for our model. Below, we display the +# hyperparameter combinations that define our search space. + +# %% +print(predictions.skb.describe_param_grid()) + +# %% +# |make_randomized_search| returns a :class:`~skrub.ParamSearch` object, which contains +# our search result and some plotting logic. +search = predictions.skb.make_randomized_search( + scoring="roc_auc", n_iter=8, n_jobs=4, random_state=0, fitted=True +) +search.results_ + +# %% +# We can also display the results of the search in a parallel coordinates plot: +search.plot_results() + +# %% +# It seems here that using the LSA as an encoder brings better test scores, +# but at the expense of training and scoring time. +# +# We can get the best performing :class:`~skrub.SkrubLearner` via +# ``best_learner_``, and use it for inference on new data. +# We load the test split of the credit fraud dataset, and apply the best learner to +# it to obtain predictions. + +new_data = skrub.datasets.fetch_credit_fraud(split="test") + +new_baskets = pd.read_csv(new_data.baskets_path) +new_products = pd.read_csv(new_data.products_path) + +probabilities = search.best_learner_.predict_proba( + {"baskets": new_baskets, "products": new_products} +) +# %% +# We can evaluate the performance of our model by plotting the ROC curve and +# calculating the AUC score. +# We can use the |RocCurveDisplay| from scikit-learn to plot the ROC curve. + +import matplotlib.pyplot as plt +from sklearn.metrics import RocCurveDisplay + +RocCurveDisplay.from_predictions(new_baskets["fraud_flag"], probabilities[:, 1]) +plt.show() +# %% +# Conclusion +# ---------- +# +# In this example, we have shown how to build a multi-table machine learning +# pipeline with skrub DataOps. We have seen how DataOps allow us to use familiar +# Pandas operations to manipulate dataframes, and how we can build a DataOps plan +# that works with multiple tables and performs hyperparameter tuning on the +# resulting pipeline. +# +# If you want to learn more about tuning hyperparameters using skrub DataOps, see +# the :ref:`Tuning Pipelines example ` for an +# in-depth tutorial. diff --git a/skrub/_docs/examples/02_data_ops/1130_choices.py b/skrub/_docs/examples/02_data_ops/1130_choices.py new file mode 100644 index 000000000..550b4981e --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1130_choices.py @@ -0,0 +1,284 @@ +""" + +.. currentmodule:: skrub + +.. _example_tuning_pipelines: + +Hyperparameter tuning with DataOps +================================== + +A machine-learning pipeline typically contains values or choices which +may influence its prediction performance, such as hyperparameters (e.g., the +regularization parameter ``alpha`` of a :class:`~sklearn.linear_model.RidgeClassifier`, +the ``learning_rate`` of a :class:`~sklearn.ensemble.HistGradientBoostingClassifier`), +which estimator to use (e.g., ``RidgeClassifier`` or +``HistGradientBoostingClassifier``), +or which steps to include (e.g., should we join a table to bring additional information +or not). + +We want to tune these choices by trying several options and keeping those that +give the best performance on a validation set. + +Skrub :ref:`DataOps ` provide a convenient way to specify +the range of possible values by inserting them directly in place of the actual +value. For example, we can write: +""" + +# %% +from sklearn.linear_model import RidgeClassifier + +import skrub + +RidgeClassifier(alpha=skrub.choose_from([0.1, 1.0, 10.0], name="α")) + +# %% +# instead of: + +RidgeClassifier(alpha=1.0) + +# %% +# Skrub then inspects our DataOps plan to discover all the places where we used objects +# like :func:`~skrub.choose_from()` and builds a grid of hyperparameters for us. +# +# We will illustrate hyperparameter tuning on the "toxicity" dataset. This +# dataset contains 1,000 texts and the task is to predict if they are +# flagged as being toxic or not. +# +# We start from a very simple pipeline without any hyperparameters. + +# %% +import pandas as pd +from sklearn.ensemble import HistGradientBoostingClassifier + +import skrub +import skrub.datasets + +file_path = skrub.datasets.fetch_toxicity().path +data = pd.read_csv(file_path) + +# This dataset is sorted -- all toxic tweets appear first, so we shuffle it +data = data.sample(frac=1.0, random_state=1) + +texts = data[["text"]] +labels = data["is_toxic"] + +# %% +# We mark the ``texts`` column as the input variable and the ``labels`` column as +# the target variable. +# +# See `the previous example <1110_data_ops_intro.html>`_ +# for a more detailed explanation +# of :func:`skrub.X` and :func:`skrub.y`. +# +# We then encode the text with a :class:`~skrub.MinHashEncoder` and fit a +# :class:`~sklearn.ensemble.HistGradientBoostingClassifier` on the resulting features. + +# %% +X = skrub.X(texts) +X + +# %% +y = skrub.y(labels) +y + +# %% +pred = X.skb.apply(skrub.MinHashEncoder()).skb.apply( + HistGradientBoostingClassifier(), y=y +) +pred.skb.cross_validate(n_jobs=4)["test_score"] + +# %% +# In this example, we will focus on the ``n_components`` of the +# ``MinHashEncoder`` and the ``learning_rate`` of the ``HistGradientBoostingClassifier`` +# to illustrate the choices objects. +# +# When we use a scikit-learn hyperparameter-tuner like +# :class:`~sklearn.model_selection.GridSearchCV` or +# :class:`~sklearn.model_selection.RandomizedSearchCV`, we need to specify a grid of +# hyperparameters separately from the estimator, with something similar to +# ``GridSearchCV(my_pipeline, param_grid={"encoder__n_components: [5, 10, 20]"})``. +# +# Instead, within a skrub DataOps plan we can use +# ``skrub.choose_from(...)`` directly where the actual value +# would normally go. Skrub then takes care of constructing the +# :class:`~sklearn.model_selection.GridSearchCV`'s parameter grid for us. +# +# Note that :func:`skrub.choose_float()` and :func:`skrub.choose_int()` can be given a +# ``log`` argument to sample in log scale, and that it is possible to specify the +# number of steps with the ``n_steps`` argument. + +# %% +X, y = skrub.X(texts), skrub.y(labels) + +encoder = skrub.MinHashEncoder( + n_components=skrub.choose_int(5, 15, n_steps=5, name="N components") +) +classifier = HistGradientBoostingClassifier( + learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="lr") +) +pred = X.skb.apply(encoder).skb.apply(classifier, y=y) + +# %% +# From here, the ``pred`` DataOp can be used to perform hyperparameter search with +# ``.skb.make_grid_search()`` or ``.skb.make_randomized_search()``. They accept +# the same arguments as their scikit-learn counterparts (e.g., ``scoring``, ``cv``, +# ``n_jobs``). Also, like ``.skb.make_learner()``, they accept a ``fitted`` +# argument: if ``fitted=True``, the search is fitted on the data we provided +# when initializing our pipeline's variables. + +search = pred.skb.make_randomized_search( + n_iter=8, n_jobs=4, random_state=1, fitted=True +) +search.results_ + +# %% +# If the plotly library is installed, we can visualize the results of the +# hyperparameter search with :func:`~skrub.ParamSearch.plot_results`. +# In the plot below, each line represents a combination of hyperparameters (in +# this case, only ``N components`` and ``learning rate``), and each column of +# points represents either a hyperparameter or the score of a given +# combination of hyperparameters. +# +# The color of the line represents the score of the combination of hyperparameters. +# The plot is interactive, and you can select only a subset of the +# hyperparameters to visualize by dragging the mouse over each column to select +# the desired range. +# +# This is particularly useful when there are many combinations of hyperparameters, +# and we want to understand which hyperparameters have the largest +# impact on the score. + +search.plot_results() +# %% +# Finally, we can retrieve the best learner from the search results, and save it +# to disk. This learner will contain the best hyperparameter configuration +# found during the search, and can be used to make predictions on new data. + +import pickle + +best_learner = search.best_learner_ +saved_model = pickle.dumps(best_learner) + +# %% +# Default choice values +# --------------------- +# +# The goal of using the different ``choose_*`` functions is to tune choices on +# validation metrics with randomized or grid search. However, even when our +# expression contains such choices we can still use it without tuning, for +# example in previews or to get a quick first result before spending the +# computation time to run the search. When we use :meth:`.skb.make_learner() +# `, we get a pipeline that does not perform any tuning +# and uses those default values. This default pipeline is used for +# :meth:`.skb.eval() `. +# +# We can control what should be the default value for each choice. For +# :func:`choose_int`, :func:`choose_float` and :func:`choose_bool`, we can use +# the ``default`` parameter. For :func:`choose_from`, the default is the first +# item from the list or dict of outcomes we provide. For :func:`optional`, we +# can pass ``default=None`` to force the default to be the alternative +# outcome, ``None``. +# +# When we do not set an explicit default, skrub picks one for depending on the +# kind of choice, as detailed in :ref:`this table` in the +# User Guide. + +# %% +# As mentioned we can control the default value: + +# %% +skrub.choose_float(1.0, 100.0, default=12.0).default() + +# %% +# Choices can appear in many places +# --------------------------------- +# +# Choices are not limited to selecting estimator hyperparameters. They can also be +# used to choose between different estimators, or in place of any value used in +# our pipeline. +# +# For example, here we pass a choice to pandas DataFrame's ``assign`` method. +# We want to add a feature that captures the length of the text, but we are not +# sure if it is better to count length in characters or in words. We do not +# want to add both because it would be redundant. We can add a column to the +# dataframe, which will be chosen among the length in characters or the length +# in words: + +# %% +X, y = skrub.X(texts), skrub.y(labels) + +X.assign( + length=skrub.choose_from( + {"words": X["text"].str.count(r"\b\w+\b"), "chars": X["text"].str.len()}, + name="length", + ) +) + +# %% +# ``choose_from`` can be given a dictionary if we want to provide +# names for the individual outcomes, or a list, when names are not needed: +# ``choose_from([1, 100], name='N')``, +# ``choose_from({'small': 1, 'big': 100}, name='N')``. +# +# Choices can be nested arbitrarily. For example, here we want to choose +# between 2 possible encoder types: the ``MinHashEncoder`` or the +# ``StringEncoder``. Each of the possible outcomes contains a choice itself: +# the number of components. + +# %% +X, y = skrub.X(texts), skrub.y(labels) + +n_components = skrub.choose_int(5, 15, name="N components") + +encoder = skrub.choose_from( + { + "minhash": skrub.MinHashEncoder(n_components=n_components), + "lse": skrub.StringEncoder(n_components=n_components), + }, + name="encoder", +) +X.skb.apply(encoder, cols="text") + +# %% +# In a similar vein, we might want to choose between a HistGradientBoostingClassifier +# and a Ridge classifier, each with its own set of hyperparameters. +# We can then define a choice for the classifier and a choice for the +# hyperparameters of each classifier. + +# %% +from sklearn.linear_model import RidgeClassifier + +hgb = HistGradientBoostingClassifier( + learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="lr") +) +ridge = RidgeClassifier(alpha=skrub.choose_float(0.01, 100, log=True, name="α")) +classifier = skrub.choose_from({"hgb": hgb, "ridge": ridge}, name="classifier") +pred = X.skb.apply(encoder).skb.apply(classifier, y=y) +print(pred.skb.describe_param_grid()) + +# %% +search = pred.skb.make_randomized_search( + n_iter=16, n_jobs=4, random_state=1, fitted=True +) +search.plot_results() + +# %% +# Now that we have a more complex plan, we can draw more conclusions from the +# parallel coordinate plot. For example, we can see that the +# ``HistGradientBoostingClassifier`` +# performs better than the ``RidgeClassifier`` in most cases, that the ``StringEncoder`` +# outperforms the ``MinHashEncoder``, and that the choice of the additional ``length`` +# feature does not have a significant impact on the score. + +# %% +# In this example, we've seen how to use skrub's ``choose_from`` objects to tune +# hyperparameters, choose optional configurations, and nest choices. We then +# examined how different choices affect the plan and prediction scores. +# +# There is more to learn about skrub choices than what is covered here. +# In particular, choices are not limited to choosing estimators and +# their hyperparameters: they can be used anywhere DataOps are used, +# such as the argument of a :func:`deferred` function, or the argument of +# other DataOps' methods or operators. Additionally, choices can be +# inter-dependent. Find more information in the :ref:`user guide +# `. diff --git a/skrub/_docs/examples/02_data_ops/1131_optuna_choices.py b/skrub/_docs/examples/02_data_ops/1131_optuna_choices.py new file mode 100644 index 000000000..6d61f9674 --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1131_optuna_choices.py @@ -0,0 +1,188 @@ +""" +.. currentmodule:: skrub +.. _example_optuna_choices: + +Tuning DataOps with Optuna +========================== + +This example shows how to use `Optuna +`_ to tune the hyperparameters of a +skrub :class:`DataOp`. As seen in the previous example, skrub DataOps can contain +"choices", objects created with :func:`choose_from`, :func:`choose_int`, +:func:`choose_float`, etc. and we can use hyperparameter search techniques to +pick the best outcome for each choice. Performing this search with Optuna +allows us to benefit from its many features, such as state-of-the-art search +strategies, monitoring and visualization, stopping and resuming searches, and +parallel or distributed computation. + +In order to use Optuna with skrub, the package must be installed first. +This can be done with pip: + +.. code-block:: bash + + pip install optuna + +""" + +# %% +# A simple regressor and example data. +# ------------------------------------ +# +# We will fit a regressor containing a few choices on a toy dataset. We +# try 2 regressors: extra trees and ridge. They both have hyperparameters that +# we want to tune. + +# %% +from sklearn.ensemble import ExtraTreesRegressor +from sklearn.linear_model import Ridge + +import skrub + +extra_tree = ExtraTreesRegressor( + min_samples_leaf=skrub.choose_int(1, 32, log=True, name="min_samples_leaf"), +) +ridge = Ridge(alpha=skrub.choose_float(0.01, 10.0, log=True, name="α")) + +regressor = skrub.choose_from( + {"extra_tree": extra_tree, "ridge": ridge}, name="regressor" +) +data = skrub.var("data") +X = data.drop(columns="MedHouseVal", errors="ignore").skb.mark_as_X() +y = data["MedHouseVal"].skb.mark_as_y() +pred = X.skb.apply(regressor, y=y) +print(pred.skb.describe_param_grid()) + +# %% +# Load data for the example + +# %% +import pandas as pd +from sklearn.model_selection import KFold + +# (We subsample the dataset by half to make the example run faster) +file_path = skrub.datasets.fetch_california_housing().path +df = pd.read_csv(file_path).sample(10_000, random_state=0) + +# The environment we will use to fit the learners created by our DataOp. +env = {"data": df} +cv = KFold(n_splits=4, shuffle=True, random_state=0) + +# %% +# Selecting the best hyperparameters with Optuna. +# ----------------------------------------------- +# +# The simplest way to use Optuna is to pass ``backend='optuna'`` to +# :meth:`DataOp.skb.make_randomized_search()`. It is used very similarly as +# with the default backend +# (:class:`sklearn.model_selection.RandomizedSearchCV`). Additional +# parameters are available to control the Optuna sampler, storage and study +# name, and timeout. +# Note that in order to persist the study and resume it later, the ``storage`` +# parameter must be set to a valid database URL (e.g., a SQLite file). Refer to +# the User Guide for an example. + +# %% +search = pred.skb.make_randomized_search( + backend="optuna", cv=cv, n_iter=10, random_state=10 +) +search.fit(env) +search.results_ + +# %% +# The usual ``results_``, ``detailed_results_`` and ``plot_results()`` are +# still available. + +# %% +search.plot_results() + +# %% +# The Optuna :class:`Study ` that was used to run the +# hyperparameter search is available in the attribute ``study_``: + +# %% +search.study_ + +# %% +search.study_.best_params + +# %% +# This allows us to use Optuna's reporting capabilities provided in +# `optuna.visualization +# `_ or +# `optuna-dashboard +# `_. + +# %% +import optuna + +optuna.visualization.plot_slice(search.study_, params=["0:min_samples_leaf"]) + +# %% +# Using Optuna directly for more advanced use cases +# ------------------------------------------------- +# +# Often we may want more control over the use of Optuna, or to access +# functionality not available through :meth:`DataOp.skb.make_randomized_search` +# such as the ask-and-tell interface, trial pruning, callbacks, +# multi-objective optimization, etc. . +# +# Directly using Optuna ourselves is also easy, as we will show now. What makes +# this possible is that we can pass an Optuna Trial to +# :meth:`DataOp.skb.make_learner` in which case the parameters suggested by the +# trial are used to create the learner. +# +# We revisit the example above, following the typical Optuna workflow. +# +# The :class:`optuna.Study ` runs the hyperparameter +# search. +# +# Its method :meth:`optimize ` is given an +# ``objective`` function. The ``objective`` must accept a +# :class:`~optuna.trial.Trial` object (which is produced by the study and picks +# the parameters for a given evaluation of the objective) and return the value +# to maximize (or minimize). +# +# To use Optuna with a :class:`DataOp`, we just need to pass the Trial object +# to :meth:`DataOp.skb.make_learner`. This creates a :class:`SkrubLearner` +# initialized with the parameters picked by the optuna Trial. +# +# We can then cross-validate the SkrubLearner, or score it however we prefer, +# and return the score so that the optuna Study can take it into account. +# +# Here we return a single score (R²), but multi-objective +# optimization is also possible. Please refer to the Optuna documentation for +# more information. + + +# %% + + +def objective(trial): + learner = pred.skb.make_learner(choose=trial) + cv_results = skrub.cross_validate(learner, environment=env, cv=cv) + return cv_results["test_score"].mean() + + +study = optuna.create_study(direction="maximize") +study.optimize(objective, n_trials=10) +study.best_params + +# %% +# We can also use Optuna's visualization capabilities to inspect the study: +optuna.visualization.plot_optimization_history(study) + +# %% +# Now we build a learner with the best hyperparameters and fit it on the full +# dataset: + +# %% +best_learner = pred.skb.make_learner(choose=study.best_trial) + +# This would achieve the same result: +# best_learner = pred.skb.make_learner() +# best_learner.set_params(**study.best_params) + +best_learner.fit(env) +print(best_learner.describe_params()) + +# %% diff --git a/skrub/_docs/examples/02_data_ops/1140_subsampling.py b/skrub/_docs/examples/02_data_ops/1140_subsampling.py new file mode 100644 index 000000000..f06013c5c --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1140_subsampling.py @@ -0,0 +1,108 @@ +""" +.. _example_subsampling: + +Subsampling for faster development +================================== + +Here we show how to use :meth:`.skb.subsample() ` to speed up +interactive construction of a skrub DataOps plan by computing previews on a subsampled +version of the original data. + +.. currentmodule:: skrub + +""" + +# %% + +import pandas as pd + +import skrub +import skrub.datasets + +file_path = skrub.datasets.fetch_employee_salaries().path +dataset = pd.read_csv(file_path) + +full_data = skrub.var("data", dataset) +full_data + +# %% +# We are working with a dataset of over 9K rows. As we build up our plan, +# we see previews of the intermediate results so we can check that it behaves +# as expected. However, if some estimators are slow, fitting them and +# computing results on the whole data can slow us down. +# +# Lightweight construction of the DataOps plan on a subsample +# ---------------------------------------------------------- +# +# We can tell skrub to subsample the data when computing the previews with +# :meth:`.skb.subsample() `. + +# %% +data = full_data.skb.subsample(n=100) +data + +# %% +# The rest of the plan will now use only 100 points for its previews. +# +# .. topic:: Subsampling only applies to previews by default +# +# By default subsampling is applied *only for previews*: the results +# shown when we display the plan, and the output of calling +# :meth:`.skb.preview() `. For other methods such as +# :meth:`.skb.get_learner() ` or +# :meth:`.skb.cross_validate() `, *no subsampling is +# done by default*. We can explicitly ask for it with ``keep_subsampling=True`` +# as we will see below. Even when ``keep_subsampling=True``, subsampling is +# not applied to the ``predict`` method. +# +# To continue building our plan, we now define X and y: + +# %% +employees = data.drop( + columns="current_annual_salary", + errors="ignore", +).skb.mark_as_X() + +salaries = data["current_annual_salary"].skb.mark_as_y() + +# %% +# And finally we apply a TableVectorizer then gradient boosting: + +# %% +from sklearn.ensemble import HistGradientBoostingRegressor + +predictions = employees.skb.apply(skrub.TableVectorizer()).skb.apply( + HistGradientBoostingRegressor(), y=salaries +) + +# %% +# +# All the lines above run very fast, including fitting the predictor above. +# +# When we display our ``predictions`` DataOp, we see that the preview is +# computed on a subsample: the result column has only 100 entries. + +# %% +predictions + +# %% +# We can also turn on subsampling for other DataOps methods, such as +# :meth:`.skb.cross_validate() `. Here we run the +# cross-validation on the small subsample of 100 rows we configured. With such +# a small subsample the scores will be very low but this might help us quickly +# detect errors in our cross-validation scheme. + +# %% +predictions.skb.cross_validate(keep_subsampling=True) + +# %% +# Evaluating the DataOps plan on the full data +# ------------------------------------------- +# By default, when we do not explicitly ask for ``keep_subsampling=True``, no +# subsampling takes place. +# +# Here we run the cross-validation **on the full data**. +# Note the longer ``fit_time`` and much better ``test_score``. + +# %% +predictions.skb.cross_validate() diff --git a/skrub/_docs/examples/02_data_ops/1150_use_case.py b/skrub/_docs/examples/02_data_ops/1150_use_case.py new file mode 100644 index 000000000..09c9f75f3 --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1150_use_case.py @@ -0,0 +1,170 @@ +""" +Use case: developing locally and deploying to production +======================================================= +""" + +# %% +# As a team of data scientists, we are tasked with a project to predict whether an email +# is potentially malicious (i.e., spam or phishing). We develop and test our models +# locally, either in a Jupyter notebook or within a Python script. Once we are satisfied +# with the model's performance, we move on to deploying it. +# +# In this use case, every time the email provider receives a new email, they want to +# verify whether it is spam before displaying it in the recipient's inbox. To achieve +# this, they plan to integrate a machine learning model within a microservice. This +# microservice will accept an email's data as a JSON payload and return a score between +# 0 and 1, indicating the likelihood that the email is spam. +# +# To avoid rewriting the entire data pipeline when moving from model validation to +# production deployment, which is both error-prone and inefficient, we prefer to load an +# object that encapsulates the same processing pipeline used during model development. +# This is where the :class:`~skrub.SkrubLearner` can help. +# +# Adopting this workflow also has the benefit of forcing us to clearly define the type +# of data that will be available at the input of the microservice. It helps ensure we +# build models that rely only on information accessible at this specific point in the +# product pipeline. For example, since we want to detect spam before the email reaches +# the recipient's inbox, we cannot use features that are only available after the +# recipient opens the email. +# +# Since this example is focused on the pipeline construction itself, we won't look at +# our model performance. + +# %% +# Generating the training data +# ---------------------------- +# In this section, we define a few functions that help us with generating the +# training data in dictionary form. We are going to generate a fully random data set. +import random +import string +import uuid +from datetime import datetime, timedelta + +import numpy as np + + +def generate_id(): + return str(uuid.uuid4()) + + +def generate_email(): + length = random.randint(5, 10) + username = "".join(random.choice(string.ascii_lowercase) for _ in range(length)) + domain = ["google", "yahoo", "whatever"] + tld = ["fr", "en", "com", "net"] + return f"{username}@{random.choice(domain)}.{random.choice(tld)}" + + +def generate_datetime(): + random_seconds = random.randint(0, int(timedelta(days=2).total_seconds())) + random_datetime = datetime.now() - timedelta(seconds=random_seconds) + return random_datetime + + +def generate_text(min_str_length, max_str_length): + random_length = random.randint(min_str_length, max_str_length) + random_text = "".join( + random.choice(string.ascii_letters + string.digits + string.punctuation) + for _ in range(random_length) + ) + return random_text + + +# %% +# We generate 1000 training samples and store them in a list of dictionaries: + +n_samples = 1000 + +# %% +# In this use case, the emails to be tested when the model is put in production +# are not contained in a dataframe, but in a JSON. As a result, our training data +# should also be contained in a list of dictionaries. + +X = [ + { + "id": generate_id(), + "sender": generate_email(), + "title": generate_text(max_str_length=10, min_str_length=2), + "content": generate_text(max_str_length=100, min_str_length=10), + "date": generate_datetime(), + "cc_emails": [generate_email() for _ in range(random.randint(0, 5))], + } + for _ in range(n_samples) +] + + +# generate array of 1 and 0 to represent the target variable +y = np.random.binomial(n=1, p=0.9, size=n_samples) + +# %% +# Building the DataOps plan +# ------------------------- +# Let's start our DataOps plan by indicating what the features and the target +# variables are. +import skrub + +X = skrub.X(X) +y = skrub.y(y) + +# %% +# The variable X is currently a list of dictionaries, which estimators cannot +# handle directly. Let's convert it to a pandas DataFrame using +# :func:`~skrub.DataOp.skb.apply_func`. +import pandas as pd + +df = X.skb.apply_func(pd.DataFrame) + +# %% +# For this example, we will use a strong baseline, with skrub's +# :func:`~skrub.tabular_pipeline()`. +tab_pipeline = skrub.tabular_pipeline("classification") + +# We can now apply the predictive model to the data. +# The DataOps plan is ready after applying the model to the data. +predictions = df.skb.apply(tab_pipeline, y=y) + +# We can then explore the full plan: +predictions.skb.draw_graph() + +# %% +# To end the explorative work, we need to build the learner, fit it, and save it to a +# file. +# Passing ``fitted=True`` to the :func:`~skrub.DataOp.skb.make_learner` +# function makes it so that the learner is fitted on the data that has been passed to +# the variables of the DataOps plan. +import joblib + +with open("learner.pkl", "wb") as f: + learner = predictions.skb.make_learner(fitted=True) + joblib.dump(learner, f) + +# %% +# Production phase +# ---------------- +# +# In our microservice, we receive a payload in JSON format. +X_input = { + "id": generate_id(), + "sender": generate_email(), + "title": generate_text(max_str_length=10, min_str_length=2), + "content": generate_text(max_str_length=100, min_str_length=10), + "date": generate_datetime(), + "cc_emails": [generate_email() for _ in range(random.randint(0, 5))], +} + +# We just have to load the learner and use it to predict the score for this input. +with open("learner.pkl", "rb") as f: + loaded_learner = joblib.load(f) +# ``X_input`` must be passed as a list so that it can be parsed correctly as a dataframe +# by Pandas. +prediction = loaded_learner.predict({"X": [X_input]}) +prediction + +# %% +# Conclusion +# ---------- +# +# Thanks to the skrub DataOps and learner, we ensure that all the transformations +# and preprocessing done during model development are exactly the same as those done in +# production. This makes deployment straightforward and reduces the risk of errors +# when moving from development to production environments. diff --git a/skrub/_docs/examples/02_data_ops/1160_pytorch.py b/skrub/_docs/examples/02_data_ops/1160_pytorch.py new file mode 100644 index 000000000..9f26ab54f --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/1160_pytorch.py @@ -0,0 +1,218 @@ +""" +Using PyTorch (via skorch) in DataOps +====================================== + +This example shows how to wrap a PyTorch model with skorch and plug it into a +skrub DataOps plan. + +.. note:: + This example requires the optional dependencies ``torch`` and ``skorch``. + +The main goal here is to show the *integration pattern*: + +- **PyTorch** defines the model (an ``nn.Module``) +- **skorch** wraps it as a scikit-learn compatible estimator +- **skrub DataOps** builds a plan and can tune skorch (and therefore PyTorch) + hyperparameters using the skrub choices. +""" + +# %% +# Loading the data +# ================= +# +# We use scikit-learn's digits dataset because it is small and ships with +# scikit-learn. Each sample is an 8x8 grayscale image of a +# handwritten digit, encoded as 64 pixel intensity values and displays a +# number from 0 to 9. +from sklearn.datasets import load_digits + +digits = load_digits() +X, y = digits.data, digits.target +print(f"Dataset shape: {X.shape}") +print(f"Number of classes: {len(set(y))}") + +# %% +# Start of the DataOps plan +# ========================== +# +# We start the DataOps plan by creating the skrub variables X and y. +import skrub + +X = skrub.X(X) +y = skrub.y(y) + +# %% +# Data preprocessing +# ================== +# +# We start by normalizing the pixel values to [0, 1] by first +# computing the global max value and then dividing the pixel values +# by this max value. Importantly, we freeze the max value (scaling factor) +# after fitting so that the same rescaling is applied later when we use our +# dataop for prediction on new (test) data. +# +# A convolutional network expects images with shape (N, C, H, W) where: +# +# - N: number of samples +# - C: number of color channels (1 for grayscale) +# - H, W: image height and width +# +# So we reshape the images to (N, 1, 8, 8) for the CNN. The -1 means the first +# dimension (N) is inferred automatically from the array size. +# +# The advantage of using DataOps is that the preprocessing steps are tracked +# in the plan and will be automatically applied during prediction. + +max_value = X.max().skb.freeze_after_fit() +X_scaled = X / max_value +X_reshaped = X_scaled.reshape(-1, 1, 8, 8).astype("float32") +X_reshaped.skb.draw_graph() + + +# %% +# Building a NN Classifier +# ========================= +# +# We'll build a tiny CNN using PyTorch and wrap it with skorch to make it +# scikit-learn compatible. The architecture uses a single convolution + pooling +# stage and a small MLP head. The architectural choices below are meant to be: +# +# - **standard**: 3x3 convolutions and 2x2 max-pooling are very common +# - **small**: the dataset and images are tiny, so we keep the model tiny too +# +# If you want more background on CNN building blocks and how convolution/pooling +# changes tensor shapes, see the CS231n notes: +# https://cs231n.github.io/convolutional-networks/ +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim + + +class TinyCNN(nn.Module): + def __init__(self, conv_channels: int = 8, hidden_units: int = 32): + super().__init__() + self.conv_channels = conv_channels + self.hidden_units = hidden_units + + # 2-level CNN with 2x2 max-pooling + self.conv1 = nn.Conv2d( + in_channels=1, out_channels=conv_channels, kernel_size=3, padding=1 + ) + self.conv2 = nn.Conv2d(conv_channels, conv_channels, kernel_size=3, padding=1) + self.pool = nn.MaxPool2d(kernel_size=2) + + # input shape = (8,8) -> conv1: (8,8) -> conv2: (8,8) -> pool: (4,4) + image_shape_after_conv = 4 * 4 + + # MLP head + self.fc1 = nn.Linear(conv_channels * image_shape_after_conv, hidden_units) + self.dropout = nn.Dropout(p=0.25) # Regularization to avoid overfitting + self.fc2 = nn.Linear(hidden_units, 10) # 10 digit classes (0..9) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = self.pool(F.relu(self.conv2(x))) + x = x.flatten(start_dim=1) + x = self.dropout(F.relu(self.fc1(x))) + return self.fc2(x) + + +# %% +# Skorch provides scikit-learn compatible wrappers around torch training loops. +# That makes the torch model usable by skrub DataOps (and scikit-learn tools in +# general). +# +# We use :func:`skrub.choose_from()` to define hyperparameters that the DataOps +# grid search will tune: conv_channels, hidden_units, and max_epochs. +# The other parameters are set to common choices for this task and training data size. + +from skorch import NeuralNetClassifier + +device = "cpu" # use "cuda" or "mps" if available + +net = NeuralNetClassifier( + module=TinyCNN, + # These choices are intentionally small so the example runs quickly. + module__conv_channels=skrub.choose_from([8, 16], name="conv_channels"), + module__hidden_units=skrub.choose_from([8, 16, 32], name="hidden_units"), + max_epochs=skrub.choose_from([10, 15], name="max_epochs"), + optimizer__lr=0.01, + optimizer=optim.Adam, + criterion=nn.CrossEntropyLoss, + device=device, + train_split=None, # We'll use skrub's grid search for validation + verbose=0, +) + + +# %% +# Tuning the model's hyperparameters with DataOps +# =============================================== +# +# We integrate the model into the DataOps plan. First, we +# convert the target labels to integers for the loss computation +# and apply the model to the preprocessed X and y. + +y_int = y.astype("int64") +predictor = X_reshaped.skb.apply(net, y=y_int) +predictor.skb.draw_graph() + +# %% +# Finally, we use 4-fold cross-validation for the hyperparameter +# tuning on our DataOps plan. + +from sklearn.model_selection import KFold + +cv = KFold(n_splits=4, shuffle=True, random_state=42) +search = predictor.skb.make_grid_search( + cv=cv, + fitted=True, + n_jobs=-1, +) +print("\nSearch results:") +print(search.results_.to_string(index=False)) + +# %% +# Let's take a better look at the well-performing models by looking +# at the parallel coordinates plot. We filter to models with +# score >= 0.94 to focus on the top-performing configurations. + +fig = search.plot_results(min_score=0.94) +fig + +# %% +# Interpreting the results +# ======================== +# +# Looking at the search results, we can observe several patterns: +# +# - **Model capacity matters**: Larger configurations with ``conv_channels=16`` +# and ``hidden_units=32`` tend to perform best. Smaller models with +# ``conv_channels=8`` and/or ``hidden_units=8`` perform significantly worse, +# indicating that the task benefits from increased model capacity. +# - **More epochs generally help**: Configurations with ``max_epochs=15`` tend to +# perform slightly better than those with ``max_epochs=10``, though the gains +# are modest compared to architectural changes. + +# %% +# Conclusion +# ========== +# +# In this example, we've shown how to use **PyTorch** and **skorch** within +# skrub DataOps. The key steps were: +# +# 1. Define a PyTorch ``nn.Module`` (our ``TinyCNN``) +# 2. Wrap it with skorch's ``NeuralNetClassifier`` to make it scikit-learn compatible +# 3. Use :func:`skrub.choose_from()` to specify hyperparameters for tuning +# 4. Integrate it into a DataOps plan and use grid search to find the best configuration +# +# This pattern lets you leverage PyTorch's flexibility for model definition while +# benefiting from skrub's hyperparameter tuning and data preprocessing capabilities. +# +# .. seealso:: +# +# * :ref:`example_tuning_pipelines`: Learn more about using +# ``skrub.choose_from()`` and other choice objects to tune hyperparameters +# in DataOps plans. +# * :ref:`example_optuna_choices`: Discover how to use Optuna as a backend +# for more sophisticated hyperparameter search strategies with skrub DataOps. diff --git a/skrub/_docs/examples/02_data_ops/GALLERY_HEADER.rst b/skrub/_docs/examples/02_data_ops/GALLERY_HEADER.rst new file mode 100644 index 000000000..865b2bc81 --- /dev/null +++ b/skrub/_docs/examples/02_data_ops/GALLERY_HEADER.rst @@ -0,0 +1,4 @@ +.. _data_ops_examples_ref: + +Skrub DataOps +================= diff --git a/skrub/_docs/examples/03_joining/0040_fuzzy_joining.py b/skrub/_docs/examples/03_joining/0040_fuzzy_joining.py new file mode 100644 index 000000000..38874b3a4 --- /dev/null +++ b/skrub/_docs/examples/03_joining/0040_fuzzy_joining.py @@ -0,0 +1,408 @@ +""" +.. _example_fuzzy_joining: + +Fuzzy joining dirty tables with the Joiner +========================================== + +Here we show how to combine data from different sources, +with a vocabulary not well normalized. + +Joining is difficult: one entry on one side does not have +an exact match on the other side. + +The |fj| function enables to join tables without cleaning the data by +accounting for the label variations. + +To illustrate, we will join data from the +`2022 World Happiness Report `_, with tables +provided in `the World Bank open data platform `_ +in order to create a first prediction model. + +Moreover, the |joiner| is a scikit-learn Transformer that makes it easy to +use such fuzzy joining multiple tables to bring in information in a +machine-learning pipeline. In particular, it enables tuning parameters of +|fj| to find the matches that maximize prediction accuracy. + + +.. |fj| replace:: :func:`~skrub.fuzzy_join` + +.. |joiner| replace:: :func:`~skrub.Joiner` +""" + +############################################################################### +# Data Importing and preprocessing +# -------------------------------- +# +# We import the happiness score table first: +import pandas as pd + +from skrub import datasets + +happiness_data = datasets.fetch_country_happiness() +df = pd.read_csv(happiness_data.happiness_report_path) + +############################################################################### +# Let's look at the table: +df.head(3) + +############################################################################### +# This is a table that contains the happiness index of a country along with +# some of the possible explanatory factors: GDP per capita, Social support, +# Generosity etc. +# + +############################################################################### +# For the sake of this example, we only keep the country names and our +# variable of interest: the 'Happiness score'. +df = df[["Country", "Happiness score"]] + +############################################################################### +# Additional tables from other sources +# ------------------------------------ +# +# Now, we need to include explanatory factors from other sources, to +# complete our covariates (X table). +# +# Interesting tables can be found on `the World Bank open data platform +# `_, which are also available in the dataset +# We extract the table containing GDP per capita by country: + +gdp_per_capita = pd.read_csv(happiness_data.GDP_per_capita_path) +gdp_per_capita.head(3) + +############################################################################### +# Then another table, with life expectancy by country: +life_exp = pd.read_csv(happiness_data.life_expectancy_path) +life_exp.head(3) + +############################################################################### +# And a table with legal rights strength by country: +legal_rights = pd.read_csv(happiness_data.legal_rights_index_path) +legal_rights.head(3) + +############################################################################### +# A correspondence problem +# ------------------------ +# +# Alas, the entries for countries do not perfectly match between our +# original table (df), and those that we downloaded from the worldbank +# (gdp_per_capita): + +df.sort_values(by="Country").tail(7) + +############################################################################### +gdp_per_capita.sort_values(by="Country Name").tail(7) + +############################################################################### +# We can see that Yemen is written "Yemen*" on one side, and +# "Yemen, Rep." on the other. +# +# We also have entries that probably do not have correspondences: "World" +# on one side, whereas the other table only has country-level data. + +############################################################################### +# Joining tables with imperfect correspondence +# -------------------------------------------- +# +# We will now join our initial table, df, with the 3 additional ones that +# we have extracted. +# + +############################################################################### +# .. _example_fuzzy_join: +# +# 1. Joining GDP per capita table +# ............................... +# +# To join them with skrub, we only need to do the following: +from skrub import fuzzy_join + +augmented_df = fuzzy_join( + df, # our table to join + gdp_per_capita, # the table to join with + left_on="Country", # the first join key column + right_on="Country Name", # the second join key column + add_match_info=True, +) + +augmented_df.tail(20) + +# We merged the first World Bank table to our initial one. + +############################################################################### +# .. topic:: Note: +# +# We set the ``add_match_info`` parameter to `True` to show distances +# between the rows that have been matched, that we will use later to show +# what are the worst matches. + +############################################################################### +# +# We see that our |fj| successfully identified the countries, +# even though some country names differ between tables. +# +# For instance, "Egypt" and "Egypt, Arab Rep." are correctly matched, as are +# "Lesotho*" and "Lesotho". +# +# .. topic:: Note: +# +# This would all be missed out if we were using other methods such as +# `pandas.merge `_, +# which can only find exact matches. +# In this case, to reach the best result, we would have to `manually` clean +# the data (e.g. remove the * after country name) and look +# for matching patterns in every observation. +# +# Let's do some more inspection of the merging done. + +############################################################################### +# Let's print the worst matches, which will give +# us an overview of the situation: + +augmented_df.sort_values("skrub_Joiner_rescaled_distance").tail(10) + +############################################################################### +# We see that some matches were unsuccessful +# (e.g "Palestinian Territories*" and "Palau"), +# because there is simply no match in the two tables. + +############################################################################### +# In this case, it is better to use the threshold parameter (``max_dist``) +# so as to include only precise-enough matches: +# +augmented_df = fuzzy_join( + df, + gdp_per_capita, + left_on="Country", + right_on="Country Name", + max_dist=0.9, + add_match_info=True, +) +augmented_df.sort_values("skrub_Joiner_rescaled_distance", ascending=False).head() + +############################################################################### +# Matches that are not available (or precise enough) are marked as ``NaN``. +# We will remove them using the ``drop_unmatched`` parameter: + +augmented_df = fuzzy_join( + df, + gdp_per_capita, + left_on="Country", + right_on="Country Name", + drop_unmatched=True, + max_dist=0.9, + add_match_info=True, +) + +augmented_df.drop(columns=["Country Name"], inplace=True) + +############################################################################### +# We can finally plot and look at the link between GDP per capital +# and happiness: +import matplotlib.pyplot as plt +import seaborn as sns + +sns.set_context("notebook") + +plt.figure(figsize=(4, 3)) +ax = sns.regplot( + data=augmented_df, + x="GDP per capita (current US$)", + y="Happiness score", + lowess=True, +) +ax.set_ylabel("Happiness index") +ax.set_title("Is a higher GDP per capita linked to happiness?") +plt.tight_layout() +plt.show() + +############################################################################### +# It seems that the happiest countries are those +# having a high GDP per capita. +# However, unhappy countries do not have only low levels +# of GDP per capita. We have to search for other patterns. + +############################################################################### +# 2. Joining life expectancy table +# ................................ +# +# Now let's include other information that may be relevant, such as in the +# life_exp table: +augmented_df = fuzzy_join( + augmented_df, + life_exp, + left_on="Country", + right_on="Country Name", + max_dist=0.9, + add_match_info=True, +) + +augmented_df.drop(columns=["Country Name"], inplace=True) + +augmented_df.head(3) + +############################################################################### +# Let's plot this relation: +plt.figure(figsize=(4, 3)) +fig = sns.regplot( + data=augmented_df, + x="Life expectancy at birth, total (years)", + y="Happiness score", + lowess=True, +) +fig.set_ylabel("Happiness index") +fig.set_title("Is a higher life expectancy linked to happiness?") +plt.tight_layout() +plt.show() + +############################################################################### +# It seems the answer is yes! +# Countries with higher life expectancy are also happier. + + +############################################################################### +# 3. Joining legal rights strength table +# ...................................... +# +# And the table with a measure of legal rights strength in the country: +augmented_df = fuzzy_join( + augmented_df, + legal_rights, + left_on="Country", + right_on="Country Name", + max_dist=0.9, + add_match_info=True, +) + +augmented_df.drop(columns=["Country Name"], inplace=True) + +augmented_df.head(3) + +############################################################################### +# Let's take a look at their correspondence in a figure: +plt.figure(figsize=(4, 3)) +fig = sns.regplot( + data=augmented_df, + x="Strength of legal rights index (0=weak to 12=strong)", + y="Happiness score", + lowess=True, +) +fig.set_ylabel("Happiness index") +fig.set_title("Does a country's legal rights strength lead to happiness?") +plt.tight_layout() +plt.show() + +############################################################################### +# From this plot, it is not clear that this measure of legal strength +# is linked to happiness. + +############################################################################### +# Great! Our joined table has become bigger and full of useful information. +# And now we are ready to apply a first machine learning model to it! + +############################################################################### +# Prediction model +# ---------------- +# +# We now separate our covariates (X), from the target (or exogenous) +# variables: y. +y = augmented_df["Happiness score"] +X = augmented_df.drop(["Happiness score", "Country"], axis=1) + +################################################################### +# Let us now define the model that will be used to predict the happiness score: + +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.model_selection import KFold + +hgdb = HistGradientBoostingRegressor(random_state=0) +cv = KFold(n_splits=5, shuffle=True, random_state=0) + +################################################################# +# To evaluate our model, we will apply a `5-fold cross-validation`. +# We evaluate our model using the `R2` score. +# +# Let's finally assess the results of our models: +from sklearn.model_selection import cross_validate + +cv_results_t = cross_validate(hgdb, X, y, cv=cv, scoring="r2") + +cv_r2_t = cv_results_t["test_score"] + +print(f"Mean R² score is {cv_r2_t.mean():.2f} +- {cv_r2_t.std():.2f}") + +################################################################# +# We have a satisfying first result: an R² of 0.63! +# +# Data cleaning varies from dataset to dataset: there are as +# many ways to clean a table as there are errors. The |fj| +# method is generalizable across all datasets. +# +# Data transformation is also often very costly in both time and resources. +# |fj| is fast and easy-to-use. +# +# Now up to you, try improving our model by adding information into it and +# beating our result! + +####################################################################### +# Using the |joiner| to fuzzy join multiple tables +# ------------------------------------------------- +# A convenient way to merge different tables from the World Bank +# to `X` in a scikit-learn Pipeline and tune the parameters is to use the |joiner|. +# +# The |joiner| is a transformer that can fuzzy-join a table on +# a main table. + +####################################################################### +# .. _example_joiner: +# +# Instantiating the transformer +# ............................. + +y = df["Happiness score"] +df = df.drop("Happiness score", axis=1) + +from sklearn.pipeline import make_pipeline + +from skrub import Joiner, SelectCols + +# We create a selector that we will insert at the end of our pipeline, to +# select the relevant columns before fitting the regressor +selector = SelectCols( + [ + "GDP per capita (current US$) gdp", + "Life expectancy at birth, total (years) life_exp", + "Strength of legal rights index (0=weak to 12=strong) legal_rights", + ] +) + +# And we can now put together the pipeline +pipeline = make_pipeline( + Joiner(gdp_per_capita, main_key="Country", aux_key="Country Name", suffix=" gdp"), + Joiner(life_exp, main_key="Country", aux_key="Country Name", suffix=" life_exp"), + Joiner( + legal_rights, main_key="Country", aux_key="Country Name", suffix=" legal_rights" + ), + selector, + HistGradientBoostingRegressor(), +) + + +########################################################################## +# And the best part is that we are now able to evaluate the parameters of the |fj|. +# For instance, the ``match_score`` was manually picked and can now be +# introduced into a grid search: + +from sklearn.model_selection import GridSearchCV + +# We will test 2 possible values of max_dist: +params = { + "joiner-1__max_dist": [0.1, 0.9], + "joiner-2__max_dist": [0.1, 0.9], + "joiner-3__max_dist": [0.1, 0.9], +} + +grid = GridSearchCV(pipeline, param_grid=params, cv=cv) +grid.fit(df, y) + +print("Best parameters:", grid.best_params_) diff --git a/skrub/_docs/examples/03_joining/0060_multiple_key_join.py b/skrub/_docs/examples/03_joining/0060_multiple_key_join.py new file mode 100644 index 000000000..4ed5b1767 --- /dev/null +++ b/skrub/_docs/examples/03_joining/0060_multiple_key_join.py @@ -0,0 +1,184 @@ +""" +.. _example_multiple_key_join: + +Spatial join for flight data: Joining across multiple columns +============================================================= + +Joining tables may be difficult if one entry on one side does not have +an exact match on the other side. + +This problem becomes even more complex when multiple columns +are significant for the join. For instance, this is the case +for **spatial joins** on two columns, typically +longitude and latitude. + +|joiner| is a scikit-learn compatible transformer that enables +performing joins across multiple keys, +independently of the data type (numerical, string or mixed). + +The following example uses US domestic flights data +to illustrate how space and time information from a +pool of tables are combined for machine learning. + +.. |fj| replace:: :func:`~skrub.fuzzy_join` + +.. |joiner| replace:: :func:`~skrub.Joiner` + +.. |Pipeline| replace:: + :class:`~sklearn.pipeline.Pipeline` +""" + +############################################################################### +# Flight-delays data +# ------------------ +# The goal is to predict flight delays. +# We have a pool of tables that we will use to improve our prediction. +# +# The following tables are at our disposal: + +############################################################################### +# The main table: flights dataset +# ............................... +# - The `flights` dataset. It contains all US flights date, origin +# and destination airports and flight time. +# Here, we consider only flights from 2008. + +import pandas as pd + +from skrub.datasets import fetch_flight_delays + +dataset = fetch_flight_delays() +seed = 1 +flights = pd.read_csv(dataset.flights_path) + +# Sampling for faster computation. +flights = flights.sample(5_000, random_state=seed, ignore_index=True) +flights.head() + +############################################################################### +# Let us see the arrival delay of the flights in the dataset: +import matplotlib.pyplot as plt +import seaborn as sns + +sns.set_theme(style="ticks") + +ax = sns.histplot(data=flights, x="ArrDelay") +ax.set_yscale("log") +plt.show() + +############################################################################ +# Interesting, most delays are relatively short (<100 min), but there +# are some very long ones. + +############################################################################ +# Airport data: an auxiliary table from the same database +# ....................................................... +# - The ``airports`` dataset, with information such as their name +# and location (longitude, latitude). + +airports = pd.read_csv(dataset.airports_path) +airports.head() + +######################################################################## +# Weather data: auxiliary tables from external sources +# .................................................... +# - The ``weather`` table. Weather details by measurement station. +# Both tables are from the Global Historical Climatology Network. +# Here, we consider only weather measurements from 2008. + +weather = pd.read_csv(dataset.weather_path) +# Sampling for faster computation. +weather = weather.sample(10_000, random_state=seed, ignore_index=True) +weather.head() + +######################################################################## +# - The ``stations`` dataset. Provides location of all the weather +# measurement stations in the US. + +stations = pd.read_csv(dataset.stations_path) +stations.head() + +############################################################################### +# Joining: feature augmentation across tables +# ------------------------------------------- +# First we join the stations with weather on the ID (exact join): + +aux = pd.merge(stations, weather, on="ID") +aux.head() + +############################################################################### +# Then we join this table with the airports so that we get all auxiliary +# tables into one. + +from skrub import Joiner + +joiner = Joiner(airports, aux_key=["lat", "long"], main_key=["LATITUDE", "LONGITUDE"]) + +aux_augmented = joiner.fit_transform(aux) + +aux_augmented.head() + +############################################################################### +# Joining airports with flights data: +# Let's instantiate another multiple key joiner on the date and the airport: + +joiner = Joiner( + aux_augmented, + aux_key=["YEAR/MONTH/DAY", "iata"], + main_key=["Year_Month_DayofMonth", "Origin"], +) + +flights.drop(columns=["TailNum", "FlightNum"]) + +############################################################################### +# Training data is then passed through a |Pipeline|: +# +# - We will combine all the information from our pool of tables into "flights", +# our main table. +# - We will use this main table to model the prediction of flight delay. +# + +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.pipeline import make_pipeline + +from skrub import TableVectorizer + +tv = TableVectorizer() +hgb = HistGradientBoostingClassifier() + +pipeline_hgb = make_pipeline(joiner, tv, hgb) + +############################################################################### +# We isolate our target variable and remove useless ID variables: + +y = flights["ArrDelay"] +X = flights.drop(columns=["ArrDelay"]) + +############################################################################### +# We want to frame this as a classification problem: +# suppose that your company is obliged to reimburse the ticket +# price if the flight is delayed. +# +# We have a binary classification problem: +# the flight was delayed (1) or not (0). + +y = (y > 0).astype(int) +y.value_counts() + +############################################################################### +# The results: + +from sklearn.model_selection import train_test_split + +X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed) +pipeline_hgb.fit(X_train, y_train).score(X_test, y_test) + +############################################################################### +# Conclusion +# ---------- +# +# In this example, we have combined multiple tables with complex joins +# on imprecise and multiple-key correspondences. +# This is made easy by skrub's |Joiner| transformer. +# +# Our final cross-validated accuracy score is 0.55. diff --git a/skrub/_docs/examples/03_joining/0070_join_aggregation.py b/skrub/_docs/examples/03_joining/0070_join_aggregation.py new file mode 100644 index 000000000..27426f2b7 --- /dev/null +++ b/skrub/_docs/examples/03_joining/0070_join_aggregation.py @@ -0,0 +1,352 @@ +""" +AggJoiner on a credit fraud dataset +=================================== + +Many problems involve tables whose entities have a one-to-many relationship. +To simplify aggregate-then-join operations for machine learning, we can include +the |AggJoiner| in our pipeline. + + +In this example, we are tackling a fraudulent loan detection use case. +Because fraud is rare, this dataset is extremely imbalanced, with a prevalence of around +1.4%. + +The data consists of two distinct entities: e-commerce "baskets", and "products". +Baskets can be tagged fraudulent (1) or not (0), and are essentially a list of products +of variable size. Each basket is linked to at least one products, e.g. basket 1 can have +product 1 and 2. + +.. image:: ../../_static/08_example_data.png + :width: 450 px + +| + +Our aim is to predict which baskets are fraudulent. + +The products dataframe can be joined on the baskets dataframe using the ``basket_ID`` +column. + +Each product has several attributes: + +- a category (marked by the column ``"item"``), +- a model (``"model"``), +- a brand (``"make"``), +- a merchant code (``"goods_code"``), +- a price per unit (``"cash_price"``), +- a quantity selected in the basket (``"Nbr_of_prod_purchas"``) + +.. |AggJoiner| replace:: + :class:`~skrub.AggJoiner` + +.. |Joiner| replace:: + :class:`~skrub.Joiner` + +.. |DropCols| replace:: + :class:`~skrub.DropCols` + +.. |TableVectorizer| replace:: + :class:`~skrub.TableVectorizer` + +.. |TableReport| replace:: + :class:`~skrub.TableReport` + +.. |MinHashEncoder| replace:: + :class:`~skrub.MinHashEncoder` + +.. |TargetEncoder| replace:: + :class:`~sklearn.preprocessing.TargetEncoder` + +.. |make_pipeline| replace:: + :func:`~sklearn.pipeline.make_pipeline` + +.. |Pipeline| replace:: + :class:`~sklearn.pipeline.Pipeline` + +.. |HGBC| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingClassifier` + +.. |OrdinalEncoder| replace:: + :class:`~sklearn.preprocessing.OrdinalEncoder` + +.. |TunedThresholdClassifierCV| replace:: + :class:`~sklearn.model_selection.TunedThresholdClassifierCV` + +.. |CalibrationDisplay| replace:: + :class:`~sklearn.calibration.CalibrationDisplay` + +.. |pandas.melt| replace:: + :func:`~pandas.melt` + +""" + +# %% +import pandas as pd + +from skrub import TableReport +from skrub.datasets import fetch_credit_fraud + +bunch = fetch_credit_fraud() +products = pd.read_csv(bunch.products_path) +baskets = pd.read_csv(bunch.baskets_path) + +TableReport(products) + +# %% +TableReport(baskets) + +# %% +# Naive aggregation +# ----------------- +# +# Let's explore a naive solution first. +# +# .. note:: +# +# Click :ref:`here` to skip this section and see the AggJoiner +# in action! +# +# +# The first idea that comes to mind to merge these two tables is to aggregate the +# products attributes into lists, using their basket IDs. +products_grouped = products.groupby("basket_ID").agg(list) +TableReport(products_grouped) + +# %% +# Then, we can expand all lists into columns, as if we were "flattening" the dataframe. +# We end up with a products dataframe ready to be joined on the baskets dataframe, using +# ``"basket_ID"`` as the join key. + +products_flatten = [] +for col in products_grouped.columns: + cols = [f"{col}{idx}" for idx in range(24)] + products_flatten.append(pd.DataFrame(products_grouped[col].to_list(), columns=cols)) +products_flatten = pd.concat(products_flatten, axis=1) +products_flatten.insert(0, "basket_ID", products_grouped.index) +TableReport(products_flatten) + +# %% +# Look at the "Stats" section of the |TableReport| above. Does anything strike you? +# +# Not only did we create 144 columns, but most of these columns are filled with NaN, +# which is very inefficient for learning! +# +# This is because each basket contains a variable number of products, up to 24, and we +# created one column for each product attribute, for each position (up to 24) in +# the dataframe. +# +# Moreover, if we wanted to replace text columns with encodings, we would create +# :math:`d \times 24 \times 2` columns (encoding of dimensionality :math:`d`, for +# 24 products, for the ``"item"`` and ``"make"`` columns), which would explode the +# memory usage. +# +# .. _agg-joiner-anchor: +# +# AggJoiner +# --------- +# Let's now see how the |AggJoiner| can help us solve this. We begin with splitting our +# basket dataset in a training and testing set. +from sklearn.model_selection import train_test_split + +X, y = baskets[["ID"]], baskets["fraud_flag"] +X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.1) +X_train.shape, y_train.shape + +# %% +# Before aggregating our product dataframe, we need to vectorize our categorical +# columns. To do so, we use: +# +# - |MinHashEncoder| on "item" and "model" columns, because they both expose typos +# and text similarities. +# - |OrdinalEncoder| on "make" and "goods_code" columns, because they consist in +# orthogonal categories. +# +# We bring this logic into a |TableVectorizer| to vectorize these columns in a +# single step. +# See `this example `_ +# for more details about these encoding choices. +from sklearn.preprocessing import OrdinalEncoder + +from skrub import MinHashEncoder, TableVectorizer + +vectorizer = TableVectorizer( + high_cardinality=MinHashEncoder(), # encode ["item", "model"] + specific_transformers=[ + (OrdinalEncoder(), ["make", "goods_code"]), + ], +) +products_transformed = vectorizer.fit_transform(products) +TableReport(products_transformed) + +# %% +# Our objective is now to aggregate this vectorized product dataframe by +# ``"basket_ID"``, then to merge it on the baskets dataframe, still on +# the ``"basket_ID"``. +# +# .. image:: ../../_static/08_example_aggjoiner.png +# :width: 900 +# +# | +# +# |AggJoiner| can help us achieve exactly this. We need to pass the product dataframe as +# an auxiliary table argument to |AggJoiner| in ``__init__``. The ``aux_key`` argument +# represent both the columns used to groupby on, and the columns used to join on. +# +# The basket dataframe is our main table, and we indicate the columns to join on with +# ``main_key``. Note that we pass the main table during ``fit``, and we discuss the +# limitations of this design in the conclusion at the bottom of this notebook. +# +# The minimum ("min") is the most appropriate operation to aggregate encodings from +# |MinHashEncoder|, for reasons that are out of the scope of this notebook. +# +from skrub import AggJoiner +from skrub import selectors as s + +# Skrub selectors allow us to select columns using regexes, which reduces +# the boilerplate. +minhash_cols_query = s.glob("item*") | s.glob("model*") +minhash_cols = s.select(products_transformed, minhash_cols_query).columns + +agg_joiner = AggJoiner( + aux_table=products_transformed, + aux_key="basket_ID", + main_key="ID", + cols=minhash_cols, + operations=["min"], +) +baskets_products = agg_joiner.fit_transform(baskets) +TableReport(baskets_products) + +# %% +# Now that we understand how to use the |AggJoiner|, we can now assemble our pipeline by +# chaining two |AggJoiner| together: +# +# - the first one to deal with the |MinHashEncoder| vectors as we just saw +# - the second one to deal with the all the other columns +# +# For the second |AggJoiner|, we use the mean, standard deviation, minimum and maximum +# operations to extract a representative summary of each distribution. +# +# |DropCols| is another skrub transformer which removes the "ID" column, which doesn't +# bring any information after the joining operation. +from scipy.stats import loguniform, randint +from sklearn.ensemble import HistGradientBoostingClassifier +from sklearn.pipeline import make_pipeline + +from skrub import DropCols + +model = make_pipeline( + AggJoiner( + aux_table=products_transformed, + aux_key="basket_ID", + main_key="ID", + cols=minhash_cols, + operations=["min"], + ), + AggJoiner( + aux_table=products_transformed, + aux_key="basket_ID", + main_key="ID", + cols=["make", "goods_code", "cash_price", "Nbr_of_prod_purchas"], + operations=["sum", "mean", "std", "min", "max"], + ), + DropCols(["ID"]), + HistGradientBoostingClassifier(), +) +model + +# %% +# We tune the hyper-parameters of the |HGBC| model using ``RandomizedSearchCV``. +# By default, the |HGBC| applies early stopping when there are at least 10_000 +# samples so we don't need to explicitly tune the number of trees (``max_iter``). +# Therefore we set this at a very high level of 1_000. We increase +# ``n_iter_no_change`` to make sure early stopping does not kick in too early. +from time import time + +from sklearn.model_selection import RandomizedSearchCV + +param_distributions = dict( + histgradientboostingclassifier__learning_rate=loguniform(1e-2, 5e-1), + histgradientboostingclassifier__min_samples_leaf=randint(2, 64), + histgradientboostingclassifier__max_leaf_nodes=[None, 10, 30, 60, 90], + histgradientboostingclassifier__n_iter_no_change=[50], + histgradientboostingclassifier__max_iter=[1000], +) + +tic = time() +search = RandomizedSearchCV( + model, + param_distributions, + scoring="neg_log_loss", + refit=False, + n_iter=10, + cv=3, + verbose=1, +).fit(X_train, y_train) +print(f"This operation took {time() - tic:.1f}s") +# %% +# The best hyper parameters are: + +pd.Series(search.best_params_) + +# %% +# To benchmark our performance, we plot the log loss of our model on the test set +# against the log loss of a dummy model that always output the observed probability of +# the two classes. +# +# As this dataset is extremely imbalanced, this dummy model should be a good baseline. +# +# The vertical bar represents one standard deviation around the mean of the cross +# validation log-loss. +import seaborn as sns +from matplotlib import pyplot as plt +from sklearn.dummy import DummyClassifier +from sklearn.metrics import log_loss + +results = search.cv_results_ +best_idx = search.best_index_ +log_loss_model_mean = -results["mean_test_score"][best_idx] +log_loss_model_std = results["std_test_score"][best_idx] + +dummy = DummyClassifier(strategy="prior").fit(X_train, y_train) +y_proba_dummy = dummy.predict_proba(X_test) +log_loss_dummy = log_loss(y_true=y_test, y_pred=y_proba_dummy) + +fig, ax = plt.subplots() +ax.bar( + height=[log_loss_model_mean, log_loss_dummy], + x=["AggJoiner model", "Dummy"], + color=["C0", "C4"], +) +for container in ax.containers: + ax.bar_label(container, padding=4) + +ax.vlines( + x="AggJoiner model", + ymin=log_loss_model_mean - log_loss_model_std, + ymax=log_loss_model_mean + log_loss_model_std, + linestyle="-", + linewidth=1, + color="k", +) +sns.despine() +ax.set_title("Log loss (lower is better)") + +# %% +# Conclusion +# ---------- +# With |AggJoiner|, you can bring the aggregation and joining operations within a +# sklearn pipeline, and train models more efficiently. +# +# One known limitation of both the |AggJoiner| and |Joiner| is that the auxiliary data +# to join is passed during the ``__init__`` method instead of the ``fit`` method, and +# is therefore fixed once the model has been trained. +# This limitation causes two main issues: +# +# 1. **Bigger model serialization:** Since the dataset has to be pickled along with +# the model, it can result in a massive file size on disk. +# +# 2. **Inflexibility with new, unseen data in a production environment:** To use new +# auxiliary data, you would need to replace the auxiliary table in the |AggJoiner| that +# was used during ``fit`` with the updated data, which is a rather hacky approach. +# +# These limitations will be addressed later in skrub. diff --git a/skrub/_docs/examples/03_joining/0080_interpolation_join.py b/skrub/_docs/examples/03_joining/0080_interpolation_join.py new file mode 100644 index 000000000..aec4a7c56 --- /dev/null +++ b/skrub/_docs/examples/03_joining/0080_interpolation_join.py @@ -0,0 +1,214 @@ +""" +Interpolation join: infer missing rows when joining two tables +============================================================== + +We illustrate the :class:`~skrub.InterpolationJoiner`, which is a type of join where +values from the second table are inferred with machine-learning, rather than looked up +in the table. It is useful when exact matches are not available but we have rows that +are close enough to make an educated guess -- in this sense it is a generalization of a +:func:`~skrub.fuzzy_join`. + +The :class:`~skrub.InterpolationJoiner` is therefore a transformer that adds the outputs +of one or more machine-learning models as new columns to the table it operates on. + +In this example we want our transformer to add weather data (temperature, rain, etc.) to +the table it operates on. We have a table containing information about commercial +flights, and we want to add information about the weather at the time and place where +each flight took off. This could be useful to predict delays -- flights are often +delayed by bad weather. + +We have a table of weather data containing, at many weather stations, measurements such +as temperature, rain and snow at many time points. Unfortunately, our weather stations +are not inside the airports, and the measurements are not timed according to the flight +schedule. Therefore, a simple equi-join would not yield any matching pair of rows from +our two tables. Instead, we use the :class:`~skrub.InterpolationJoiner` to *infer* the +temperature at the airport at take-off time. We train supervised +machine-learning models using the weather table, then query them with the times +and locations in the flights table. + +""" + +###################################################################### +# Load weather data +# ----------------- +# We join the table containing the measurements to the table that contains the weather +# stations’ latitude and longitude. We subsample these large tables for the example to +# run faster. + +import pandas as pd + +from skrub.datasets import fetch_flight_delays + +dataset = fetch_flight_delays() +weather = pd.read_csv(dataset.weather_path) +weather = weather.sample(100_000, random_state=0, ignore_index=True) +stations = pd.read_csv(dataset.stations_path) +weather = stations.merge(weather, on="ID")[ + ["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY", "TMAX", "PRCP", "SNOW"] +] +weather["YEAR/MONTH/DAY"] = pd.to_datetime(weather["YEAR/MONTH/DAY"]) + +###################################################################### +# The ``'TMAX'`` is in tenths of degree Celsius -- a ``'TMAX'`` of 297 means the maximum +# temperature that day was 29.7℃. We convert it to degrees for readability + +weather["TMAX"] /= 10 + +###################################################################### +# InterpolationJoiner with a ground truth: joining the weather table on itself +# ---------------------------------------------------------------------------- +# As a first simple example, we apply the :class:`~skrub.InterpolationJoiner` in a +# situation where the ground truth is known. We split the weather table in half and join +# the second half on the first half. Thus, the values from the right side table of the +# join are inferred, whereas the corresponding columns from the left side contain the +# ground truth and we can compare them. + +n_main = weather.shape[0] // 2 +main_table = weather.iloc[:n_main] +main_table.head() + +###################################################################### +aux_table = weather.iloc[n_main:] +aux_table.head() + + +###################################################################### +# Joining the tables +# ------------------ +# Now we join our two tables and check how well the :class:`~skrub.InterpolationJoiner` +# can reconstruct the matching rows that are missing from the right side table. To avoid +# clashes in the column names, we use the ``suffix`` parameter to append ``"predicted"`` +# to the right side table column names. + +from skrub import InterpolationJoiner + +joiner = InterpolationJoiner( + aux_table, + key=["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY"], + suffix="_predicted", +).fit(main_table) +join = joiner.transform(main_table) +join.head() + +###################################################################### +# Comparing the estimated values to the ground truth +# -------------------------------------------------- + +from matplotlib import pyplot as plt + +join = join.sample(2000, random_state=0, ignore_index=True) +fig, axes = plt.subplots( + 3, + 1, + figsize=(5, 9), + gridspec_kw={"height_ratios": [1.0, 0.5, 0.5]}, + layout="compressed", +) +for ax, col in zip(axes.ravel(), ["TMAX", "PRCP", "SNOW"]): + ax.scatter( + join[col].values, + join[f"{col}_predicted"].values, + alpha=0.1, + ) + ax.set_aspect(1) + ax.set_xlabel(f"true {col}") + ax.set_ylabel(f"predicted {col}") +plt.show() + +###################################################################### +# We see that in this case the interpolation join works well for the temperature, but +# not precipitation nor snow. So we will only add the temperature to our flights table. + +aux_table = aux_table.drop(["PRCP", "SNOW"], axis=1) + +###################################################################### +# Loading the flights table +# ------------------------- +# We load the flights table and join it to the airports table using the flights’ +# ``'Origin'`` which refers to the departure airport’s IATA code. We use only a subset +# to speed up the example. + +flights = pd.read_csv(dataset.flights_path) +flights["Year_Month_DayofMonth"] = pd.to_datetime(flights["Year_Month_DayofMonth"]) +flights = flights[["Year_Month_DayofMonth", "Origin", "ArrDelay"]] +flights = flights.sample(20_000, random_state=0, ignore_index=True) +airports = pd.read_csv(dataset.airports_path)[ + ["iata", "airport", "state", "lat", "long"] +] +flights = flights.merge(airports, left_on="Origin", right_on="iata") +# printing the first row is more readable than the head() when we have many columns +flights.iloc[0] + +###################################################################### +# Joining the flights and weather data +# ------------------------------------ +# As before, we initialize our join transformer with the weather table. Then, we use it +# to transform the flights table -- it adds a ``'TMAX'`` column containing the predicted +# maximum daily temperature. +# + +joiner = InterpolationJoiner( + aux_table, + main_key=["lat", "long", "Year_Month_DayofMonth"], + aux_key=["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY"], +) +join = joiner.fit_transform(flights) +join.head() + +###################################################################### +# Sanity checks +# ------------- +# This time we do not have a ground truth for the temperatures. +# We can perform a few basic sanity checks. + +state_temperatures = join.groupby("state")["TMAX"].mean().sort_values() + +###################################################################### +# States with the lowest average predicted temperatures: Alaska, Montana, North Dakota, +# Washington, Minnesota. +state_temperatures.head() + +###################################################################### +# States with the highest predicted temperatures: Puerto Rico, Virgin Islands, Hawaii, +# Florida, Louisiana. +state_temperatures.tail() + +###################################################################### +# Higher latitudes (farther up north) are colder -- the airports in this dataset are in +# the United States. +fig, ax = plt.subplots() +ax.scatter(join["lat"], join["TMAX"]) +ax.set_xlabel("Latitude (higher is farther north)") +ax.set_ylabel("TMAX") +plt.show() + +###################################################################### +# Winter months are colder than spring -- in the north hemisphere January is colder than +# April +# + +import seaborn as sns + +join["month"] = join["Year_Month_DayofMonth"].dt.strftime("%m %B") +plt.figure(layout="constrained") +sns.barplot(data=join.sort_values(by="month"), y="month", x="TMAX") +plt.show() + +###################################################################### +# Of course these checks do not guarantee that the inferred values in our ``join`` +# table’s ``'TMAX'`` column are accurate. But at least the +# :class:`~skrub.InterpolationJoiner` seems to have learned a few reasonable trends from +# its training table. + + +###################################################################### +# Conclusion +# ---------- +# We have seen how to fit an :class:`~skrub.InterpolationJoiner` transformer: we give it +# a table (the weather data) and a set of matching columns (here date, latitude, +# longitude) and it learns to predict the other columns’ values (such as the max daily +# temperature). Then, it transforms tables by *predicting* values that a matching row +# would contain, rather than by searching for an actual match. It is a generalization of +# the :func:`~skrub.fuzzy_join`, as :func:`~skrub.fuzzy_join` is the same thing as an +# :class:`~skrub.InterpolationJoiner` where the estimators are 1-nearest-neighbor +# estimators. diff --git a/skrub/_docs/examples/03_joining/GALLERY_HEADER.rst b/skrub/_docs/examples/03_joining/GALLERY_HEADER.rst new file mode 100644 index 000000000..3b3de2857 --- /dev/null +++ b/skrub/_docs/examples/03_joining/GALLERY_HEADER.rst @@ -0,0 +1,2 @@ +Joining tables with imperfect data +================================== diff --git a/skrub/_docs/examples/GALLERY_HEADER.rst b/skrub/_docs/examples/GALLERY_HEADER.rst new file mode 100644 index 000000000..bac945d55 --- /dev/null +++ b/skrub/_docs/examples/GALLERY_HEADER.rst @@ -0,0 +1,2 @@ +Examples +======== diff --git a/skrub/_docs/exploring_a_dataframe.rst b/skrub/_docs/exploring_a_dataframe.rst new file mode 100644 index 000000000..03f8230fe --- /dev/null +++ b/skrub/_docs/exploring_a_dataframe.rst @@ -0,0 +1,13 @@ +.. _user_guide_exploring_a_dataframe_index: + +Exploring a Dataframe +===================== + +This section covers the :class:`~skrub.TableReport` and how it can be used for exploring +and understanding your dataframes. + + +.. toctree:: + :maxdepth: 3 + + modules/tablereport/exploring_dataframes_interactively diff --git a/skrub/_docs/guides/table_report/01_alter_appearance.rst b/skrub/_docs/guides/table_report/01_alter_appearance.rst new file mode 100644 index 000000000..4ba65d869 --- /dev/null +++ b/skrub/_docs/guides/table_report/01_alter_appearance.rst @@ -0,0 +1,25 @@ +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |set_config| replace:: :func:`~skrub.set_config` +.. |column_associations| replace:: :func:`~skrub.column_associations` + +.. _user_guide_table_report_customize: + +How to tweak the Appearance of the |TableReport| +------------------------------------------------ + +The skrub global configuration includes various parameters that let you tweak +the HTML representation of the |TableReport|. + +For performance reasons, the |TableReport| disables the computation of +distributions and associations for tables with more than 30 columns. +This behavior can be overridden by setting the parameters ``plot_distributions`` +and ``compute_associations`` to ``True`` respectively. + +It is also possible to specify the floating point precision by setting the appropriate +``float_precision`` parameter. + +The column threshold that is used by the |TableReport| can be modified in a given +script by using |set_config| and changing the values of +``table_report_plot_threshold`` and ``table_report_associations_threshold`` to +the desired threshold. Environment variables are also provided to set the threshold +permanently. Refer to :ref:`user_guide_configuration_parameters` for more detail. diff --git a/skrub/_docs/guides/table_report/02_exporting.rst b/skrub/_docs/guides/table_report/02_exporting.rst new file mode 100644 index 000000000..4805d2cf5 --- /dev/null +++ b/skrub/_docs/guides/table_report/02_exporting.rst @@ -0,0 +1,61 @@ +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |set_config| replace:: :func:`~skrub.set_config` +.. |column_associations| replace:: :func:`~skrub.column_associations` + +.. _user_guide_table_report_sharing: +How to export and share the |TableReport| +----------------------------------------- + +The |TableReport| is generated as a standalone HTML file that includes the report +data, the plots, and the Javascript necessary to provide interactivity. + +If it is generated inside a notebook (Jupyter or Marimo), the |TableReport| is +rendered directly inside the cell where it is called. If, instead, it is generated +by a script, the report will need to be opened by calling ``.open()``: + +>>> TableReport(df).open() # doctest: +SKIP + +Note that calling ``.open()`` will start a standalone process that hosts the report, +and a tab will be opened in the default browser. It is not possible to save the +report from the webpage. The function :func:`~skrub.TableReport.write_html` should +be used for that: + +.. code-block:: + + tr = TableReport(df) + tr.write_html("my_report.html") + +It is also possible to export the raw HTML, or a HTML fragment to embed in a page +with :func:`~skrub.TableReport.html` and :func:`~skrub.TableReport.html_snippet` +respectively. + +The report can be exported in JSON format, which allows structured +access to the data and statistics used to build the report with +:func:`~skrub.TableReport.json`. + +.. code-block:: + + tr = TableReport(df) + json_data = tr.json() + +Note that this will export all parts of the |TableReport|, including the distribution +plots in SVG format if they have been generated. If you do not need them, plots should be +disabled directly when generating the table report. + +.. code-block:: + + tr = TableReport(df, plot_distributions=False) + json_data = tr.json() + +Finally, :func:`~skrub.TableReport.markdown` produces a shortened summary of the +report in Markdown format. This summary contains the measured statistics and the +associations (if measured): plots and table preview are skipped from this view. +This format can be shared easily in text form, or fed to an AI agent to obtain +insight about a given table. + +.. warning:: + + No sanitization of the input data is performed, and the report includes raw data + (column names and cell values). Therefore, it should not be used on untrusted data, + or when the resulting summary may be too large as it could lead to security risks + or performance problems. diff --git a/skrub/_docs/guides/table_report/03_finding_correlated_columns.rst b/skrub/_docs/guides/table_report/03_finding_correlated_columns.rst new file mode 100644 index 000000000..746ab5ce4 --- /dev/null +++ b/skrub/_docs/guides/table_report/03_finding_correlated_columns.rst @@ -0,0 +1,38 @@ +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |DropSimilar| replace:: :class:`~skrub.DropSimilar` +.. |column_associations| replace:: :func:`~skrub.column_associations` + +.. _user_guide_table_report_associations: + +How to find correlated columns in a dataframe +============================================ + +In addition to |TableReport|'s **Associations** tab, you can compute associations +using the |column_associations| function, which returns a dataframe containing the +associations. + +Reported metrics include `Cramer’s V statistic `_ +and `Pearson’s Correlation Coefficient `_. +The result is returned as a dataframe that contains the column name and idx for the +left and the right table, and both associations; results are sorted in descending order +by Cramer’s V association. + +This can be useful to have access to the information used in the |TableReport| +for later use (e.g., to select which columns to drop). These associations are +also used by the |DropSimilar| transformer to select which columns should be dropped. + +.. code-block:: + + from skrub import column_associations + from skrub.datasets import fetch_employee_salaries + import pandas as pd + path = fetch_employee_salaries().path + df = pd.read_csv(path) + column_associations(df).head() + + left_column_name left_column_idx right_column_name right_column_idx cramer_v pearson_corr + 0 department 1 department_name 2 1.000000 NaN + 1 assignment_category 4 current_annual_salary 8 0.635525 NaN + 2 division 3 assignment_category 4 0.601097 NaN + 3 assignment_category 4 employee_position_title 5 0.496814 NaN + 4 division 3 employee_position_title 5 0.416034 NaN diff --git a/skrub/_docs/guides/table_report/04_custom_filters.rst b/skrub/_docs/guides/table_report/04_custom_filters.rst new file mode 100644 index 000000000..807e963a0 --- /dev/null +++ b/skrub/_docs/guides/table_report/04_custom_filters.rst @@ -0,0 +1,32 @@ +.. |TableReport| replace:: :class:`~skrub.TableReport` + + +How to define custom filters for the TableReport +================================================ + +It is possible to define custom filters for the |TableReport| using either column +names, or :ref:`skrub selectors `. + +By defining a custom filter, it becomes easier to show and work directly on a given +subset of columns. + +For example, we might want to select only the columns whose name follows a certain +pattern (here, starting with "metric"): + +>>> import pandas as pd +>>> from skrub import TableReport +>>> from skrub import selectors as s +>>> df = pd.DataFrame( +... {"id": [1, 2, 3], "metric1": [1, 2, 3], "metric2": [4, 5, 6], "metric3": [7, 8, 9]} +... ) + +Custom filters should be defined as a dictionary where the key is the name of the +filter that should be displayed in the generated report, and the value is either +a list of columns, the indices of the columns (first column has index 0 etc.), or +a skrub selector, as shown in this example: + +>>> filters = {"only_metrics": s.glob("metric*")} +>>> report = TableReport(df, column_filters=filters) + +Custom filters are placed at the top of the list of filters, in the "Filter columns" +drop-down menu. diff --git a/skrub/_docs/guides/utilities/customizing_configuration.rst b/skrub/_docs/guides/utilities/customizing_configuration.rst new file mode 100644 index 000000000..eb21bffa7 --- /dev/null +++ b/skrub/_docs/guides/utilities/customizing_configuration.rst @@ -0,0 +1,93 @@ +.. |set_config| replace:: :func:`~skrub.set_config` +.. |get_config| replace:: :func:`~skrub.get_config` +.. |config_context| replace:: :func:`~skrub.config_context` + +.. _user_guide_configuration_parameters: + +How to configure and customize the default behavior of skrub +============================================================ + + +Skrub includes a configuration manager that allows setting various parameters +(see the |set_config| documentation for more detail). + +It is possible to change configuration options using the |set_config| function: + +>>> from skrub import set_config +>>> set_config(table_report_verbosity=0) # doctest: +SKIP + +This alters the behavior of skrub in the current script. Each configuration parameter +has an environment variable that can be used to set it permanently. + +Additionally, a |config_context| is provided to allow temporarily altering the +configuration: + +>>> import skrub +>>> with skrub.config_context(table_report_plots_threshold=1): +... pass + +Within this context, only the code executed inside the ``with`` statement is affected. + +The |get_config| function allows to retrieve the current configuration. + +Configuration parameters +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The configuration parameters that can be set with ``set_config`` and ``config_context`` +are available by using + +>>> import skrub +>>> config = skrub.get_config() +>>> config.keys() +dict_keys(['use_table_report_data_ops', 'table_report_plots_threshold', 'table_report_associations_threshold', 'table_report_verbosity', 'subsampling_seed', 'enable_subsampling', 'float_precision', 'cardinality_threshold', 'data_dir', 'eager_data_ops', 'data_ops_open_graph_dropdown']) + +These are the parameters currently available in the global configuration: + +.. list-table:: Skrub Configuration Parameters + :header-rows: 1 + :widths: 20 15 25 40 + + * - Parameter Name + - Default Value + - Env Variable + - Description + * - ``use_table_report_data_ops`` + - ``True`` + - ``SKB_USE_TABLE_REPORT_DATA_OPS`` + - Set the HTML representation used for the Data Ops previews. If ``True``, use the :class:`~skrub.TableReport`, otherwise use the default Pandas or Polars representation. + * - ``table_report_verbosity`` + - ``1`` + - ``SKB_TABLE_REPORT_VERBOSITY`` + - Set the verbosity of the :class:`~skrub.TableReport`. If ``1``, print on screen the progress by column, if ``0`` print nothing. + * - ``table_report_plots_threshold`` + - 30 + - ``SKB_TABLE_REPORT_PLOTS_THRESHOLD`` + - If a dataframe has more columns than the value set here, the :class:`~skrub.TableReport` will skip generating the distribution plots (when ``plot_distributions="auto"``, the default). + * - ``table_report_associations_threshold`` + - 30 + - ``SKB_TABLE_REPORT_ASSOCIATIONS_THRESHOLD`` + - If a dataframe has more columns than the value set here, the :class:`~skrub.TableReport` will skip computing the associations (when ``compute_associations="auto"``, the default). + * - ``subsampling_seed`` + - 0 + - ``SKB_SUBSAMPLING_SEED`` + - Set the random seed of subsampling in :func:`skrub.DataOp.skb.subsample()`, when ``how="random"`` is passed. + * - ``enable_subsampling`` + - ``"default"`` + - ``SKB_ENABLE_SUBSAMPLING`` + - Control the activation of subsampling in :func:`skrub.DataOp.skb.subsample()`. If ``"default"``, the behavior of :func:`skrub.DataOp.skb.subsample()` is used. If ``"disable"``, subsampling is never used, so skb.subsample becomes a no-op. If ``"force"``, subsampling is used in all DataOps evaluation modes (eval(), fit_transform, etc.). + * - ``float_precision`` + - 3 + - ``SKB_FLOAT_PRECISION`` + - Control the number of significant digits shown when formatting floats. Applies overall precision rather than fixed decimal places. + * - ``cardinality_threshold`` + - 40 + - ``SKB_CARDINALITY_THRESHOLD`` + - Set the ``cardinality_threshold`` argument of :class:`~skrub.TableVectorizer`. Additionally, set the threshold for warning the user about high cardinality features in the :class:`~skrub.TableReport`. + * - ``data_dir`` + - ``~/skrub_data`` + - ``SKB_DATA_DIRECTORY`` + - Set the default location used by skrub to store datasets and other data, such as the Data Ops reports. + * - ``eager_data_ops`` + - ``True`` + - ``SKB_EAGER_DATA_OPS`` + - Eagerly perform checks on the DataOps as soon they are created, and compute previews if preview data is available. If disabled, those checks are delayed until the DataOp is actually used diff --git a/skrub/_docs/guides/utilities/deduplicate_categorical_data.rst b/skrub/_docs/guides/utilities/deduplicate_categorical_data.rst new file mode 100644 index 000000000..c9dc31491 --- /dev/null +++ b/skrub/_docs/guides/utilities/deduplicate_categorical_data.rst @@ -0,0 +1,112 @@ +.. |deduplicate| replace:: :func:`~skrub.deduplicate` + +.. _user_guide_deduplicate: + +How to deduplicate categorical data with |deduplicate| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you have a series or list that contains strings with typos, the |deduplicate| +function may be used to remove the typos. This is done by creating a mapping +between the typo strings and the correct strings. + +.. admonition:: How does this work? + :collapsible: closed + + Deduplication is done by first computing the n-gram distance between unique + categories in data, then performing hierarchical clustering on this distance + matrix, and choosing the most frequent element in each cluster as the + 'correct' spelling. This method works best if the true number of + categories is significantly smaller than the number of observed spellings. + +>>> from skrub.datasets import make_deduplication_data +>>> duplicated = make_deduplication_data(examples=['black', 'white'], +... entries_per_example=[5, 5], +... prob_mistake_per_letter=0.3, +... random_state=42) +>>> duplicated # doctest: +SKIP +['blacs', 'black', 'black', 'black', 'black', \ +'uhibe', 'white', 'white', 'white', 'white'] + +To deduplicate the data, we can build a correspondence matrix: + +>>> from skrub import deduplicate +>>> deduplicate_correspondence = deduplicate(duplicated) +>>> deduplicate_correspondence +blacs black +black black +black black +black black +black black +uhibe white +white white +white white +white white +white white +dtype: ... + +>>> deduplicated = list(deduplicate_correspondence) +>>> deduplicated # doctest: +SKIP +['black', 'black', 'black', 'black', 'black', \ +'white', 'white', 'white', 'white', 'white'] + +See the |deduplicate| documentation for caveats and more detail. + +Deduplicating values in a dataframe +----------------------------------- + +|deduplicate| can be used to replace values in a dataframe that contains typos. +This can be done with ``deduplicate_correspondence`` computed above and the +``map`` function in pandas, or the ``replace`` function in polars. + +>>> import pandas as pd +>>> df = pd.DataFrame({'color': duplicated, 'value': range(10)}) +>>> df +color value +0 blacs 0 +1 black 1 +2 black 2 +3 black 3 +4 black 4 +5 uhibe 5 +6 white 6 +7 white 7 +8 white 8 +9 white 9 +>>> df['deduplicated_color'] = df['color'].map(deduplicate_correspondence.to_dict()) +>>> df +color value deduplicated_color +0 blacs 0 black +1 black 1 black +2 black 2 black +3 black 3 black +4 black 4 black +5 uhibe 5 white +6 white 6 white +7 white 7 white +8 white 8 white +9 white 9 white + +With polars: + +>>> import polars as pl # doctest: +SKIP +>>> df = pl.DataFrame({'color': duplicated, 'value': range(10)}) # doctest: +SKIP +>>> df.with_columns(deduplicated_color = pl.col("color").replace( # doctest: +SKIP +... deduplicate_correspondence.to_dict()) +... ) +shape: (10, 3) +┌───────┬───────┬────────────────────┐ +│ color ┆ value ┆ deduplicated_color │ +│ --- ┆ --- ┆ --- │ +│ str ┆ i64 ┆ str │ +╞═══════╪═══════╪════════════════════╡ +│ blacs ┆ 0 ┆ black │ +│ black ┆ 1 ┆ black │ +│ black ┆ 2 ┆ black │ +│ black ┆ 3 ┆ black │ +│ black ┆ 4 ┆ black │ +│ uhibe ┆ 5 ┆ white │ +│ white ┆ 6 ┆ white │ +│ white ┆ 7 ┆ white │ +│ white ┆ 8 ┆ white │ +│ white ┆ 9 ┆ white │ +└───────┴───────┴────────────────────┘ diff --git a/skrub/_docs/guides/utilities/fetching_datasets.rst b/skrub/_docs/guides/utilities/fetching_datasets.rst new file mode 100644 index 000000000..07db3844a --- /dev/null +++ b/skrub/_docs/guides/utilities/fetching_datasets.rst @@ -0,0 +1,46 @@ +Working with the example datasets provided by skrub +------------------------------------------------------- + +Skrub includes a number of datasets used for running examples. Each dataset +can be downloaded using its ``fetch_*`` function, provided in the ``skrub.datasets`` +namespace: + +.. code-block:: python + + from skrub.datasets import fetch_employee_salaries + data = fetch_employee_salaries() + +Datasets are stored as :class:`~sklearn.utils.Bunch` objects, which include a path +to each table in the dataset. Datasets should be loaded using the path: + +.. code-block:: python + + import pandas as pd + df = pd.read_csv(data.path) + + +Some datasets include multiple tables: in this case, ``path`` isn't available and +instead each table should be loaded with its own path: + + +.. code-block:: python + + from skrub.datasets import fetch_credit_fraud + data = fetch_employee_salaries() + baskets = pd.read_csv(data.baskets_path) + products = pd.read_csv(data.products_path) + + +Modifying the download location of ``skrub`` datasets +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, datasets are stored in ``~/skrub_data``, where ``~`` is expanded as +the (OS dependent) home directory of the user. The function +:func:`~skrub.datasets.get_data_dir` shows +the location that ``skrub`` uses to store data. + +If needed, it is possible to change this location by modifying the environment +variable ``SKB_DATA_DIRECTORY`` to an **absolute directory path**. + +See :ref:`user_guide_configuration_parameters` for more info on the global skrub +configuration. diff --git a/skrub/_docs/howto.rst b/skrub/_docs/howto.rst new file mode 100644 index 000000000..994e40380 --- /dev/null +++ b/skrub/_docs/howto.rst @@ -0,0 +1,26 @@ +.. _how_to: + +How-tos +-------- + +This page is the index of skrub's How-to guides: these are short guides and examples +on how to complete specific tasks and address specific circumstances. + +For a more long-form discussion on how skrub works and the reasoning behind specific +design choices, refer to the :ref:`User Guide `. For runnable code, see the +:doc:`Example gallery `. For class and function details, see +the :ref:`API Reference `. + + +.. include:: includes/big_toc_css.rst + +.. toctree:: + :maxdepth: 2 + + guides/table_report/01_alter_appearance.rst + guides/table_report/02_exporting.rst + guides/table_report/03_finding_correlated_columns.rst + guides/table_report/04_custom_filters.rst + guides/utilities/customizing_configuration.rst + guides/utilities/deduplicate_categorical_data.rst + guides/utilities/fetching_datasets.rst diff --git a/skrub/_docs/includes/big_toc_css.rst b/skrub/_docs/includes/big_toc_css.rst new file mode 100644 index 000000000..6008fce8e --- /dev/null +++ b/skrub/_docs/includes/big_toc_css.rst @@ -0,0 +1,160 @@ +.. + File to ..include in a document with a big table of content, to give + it 'style' + +.. raw:: html + + + + diff --git a/skrub/_docs/index.rst b/skrub/_docs/index.rst new file mode 100644 index 000000000..18d745435 --- /dev/null +++ b/skrub/_docs/index.rst @@ -0,0 +1,18 @@ +.. title:: Home + +.. toctree:: + :maxdepth: 2 + +.. currentmodule:: skrub + +.. toctree:: + :hidden: + + install + documentation + howto + reference/index + auto_examples/index + learning_materials + CHANGES + development diff --git a/skrub/_docs/install.rst b/skrub/_docs/install.rst new file mode 100644 index 000000000..3f02d7041 --- /dev/null +++ b/skrub/_docs/install.rst @@ -0,0 +1,238 @@ +.. _installation_instructions: + +.. currentmodule:: skrub + +======= +Install +======= + +.. raw:: html + +
+ + + +
+
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+ +.. code:: console + + pip install skrub -U + +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ pip install skrub[transformers] -U + + +.. raw:: html + +
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+
+ +.. code:: console + + conda install -c conda-forge skrub + +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ conda install -c conda-forge skrub[transformers] + + +.. raw:: html + +
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+ +.. code:: console + + mamba install -c conda-forge skrub + +| + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ mamba install -c conda-forge skrub[transformers] + + +.. raw:: html + +
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+ +.. _installing_from_source: + +Advanced Usage for Contributors +------------------------------- + +1. Fork the project +''''''''''''''''''' + +To contribute to the project, you first need to +`fork skrub on GitHub `_. + +That will enable you to push your commits to a branch *on your fork*. + +2. Clone your fork +'''''''''''''''''' + +Clone your forked repo to your local machine: + +.. code:: console + + git clone https://github.com//skrub + cd skrub + +Next, add the *upstream* remote (i.e. the official skrub repository). This allows you +to pull the latest changes from the main repository: + +.. code:: console + + git remote add upstream https://github.com/skrub-data/skrub.git + +Verify that both the origin (your fork) and upstream (official repo) +are correctly set up: + +.. code:: console + + git remote -v + +You should see something like this: + +.. code:: console + + origin git@github.com:/skrub.git (fetch) + origin git@github.com:/skrub.git (push) + upstream git@github.com:skrub-data/skrub.git (fetch) + upstream git@github.com:skrub-data/skrub.git (push) + + +3. Setup your environment +''''''''''''''''''''''''' + +Now, setup a development environment. +You can set up a virtual environment with Conda, or with python's ``venv``: + +- With `conda `__: + +.. code:: console + + conda create -n env_skrub python=3.13 + conda activate env_skrub + +- With `venv `__: +.. code:: console + + python -m venv env_skrub + source env_skrub/bin/activate + +Then, with the environment activated and at the root of your local copy of skrub, +install the local package in editable mode with development dependencies: + +.. code:: console + + pip install -e ".[dev]" + +Enabling pre-commit hooks ensures code style consistency by triggering checks (mainly formatting) every time you run a ``git commit``. + +.. code:: console + + pre-commit install + + +Optionally, configure Git to ignore certain revisions in git blame and +IDE integrations. These revisions are listed in .git-blame-ignore-revs: + +.. code:: console + + git config blame.ignoreRevsFile .git-blame-ignore-revs + +4. Run the tests +'''''''''''''''' + +To ensure your environment is correctly set up, run the test suite: + +.. code:: console + + pytest --pyargs skrub + +Testing should take about 5 minutes. + +If you see some warnings like: + +.. code:: sh + + UserWarning: Only pandas and polars DataFrames are supported, but input is a Numpy array. Please convert Numpy arrays to DataFrames before passing them to skrub transformers. Converting to pandas DataFrame with columns ['0', '1', …]. + warnings.warn( + +This is expected, and you may proceed with the next steps without worrying about them. +However, no tests should fail at this point: if they do fail, then let us know. + +After that, your environment is ready for development! + +**Deep learning dependencies** + +Deep-learning based encoders like :class:`TextEncoder` require installing optional +dependencies to use them. The following will install +`torch `_, +`transformers `_, +and `sentence-transformers `_. + +.. code:: console + + $ pip install -e ".[transformers]" + + +Now that you're set up, +you may return to :ref:`writing your first pull request` +and start coding! + +.. raw:: html + +
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diff --git a/skrub/_docs/joining_dataframes.rst b/skrub/_docs/joining_dataframes.rst new file mode 100644 index 000000000..074421399 --- /dev/null +++ b/skrub/_docs/joining_dataframes.rst @@ -0,0 +1,11 @@ +.. _user_guide_joining_dataframes: + +Joining Dataframes +================== + +This section covers the various methods provided by skrub to join dataframes. + +.. toctree:: + :maxdepth: 3 + + modules/joining_tables/assembling diff --git a/skrub/_docs/learning_materials.rst b/skrub/_docs/learning_materials.rst new file mode 100644 index 000000000..0ed79113f --- /dev/null +++ b/skrub/_docs/learning_materials.rst @@ -0,0 +1,11 @@ +Learning Materials +================== + +You are being redirected to the new learning materials page. + +.. raw:: html + + + +If you are not redirected automatically, follow this +`link `_. diff --git a/skrub/_docs/modules/column_level_featurizing/advanced_columnwise_operations.rst b/skrub/_docs/modules/column_level_featurizing/advanced_columnwise_operations.rst new file mode 100644 index 000000000..89fcba564 --- /dev/null +++ b/skrub/_docs/modules/column_level_featurizing/advanced_columnwise_operations.rst @@ -0,0 +1,133 @@ +.. currentmodule:: skrub + +.. |ApplyToCols| replace:: :class:`ApplyToCols` +.. |RejectColumn| replace:: :class:`core.RejectColumn` +.. |SingleColumnTranformer| replace:: :class:`core.SingleColumnTranformer` +.. |ToDatetime| replace:: :class:`ToDatetime` + +.. _user_guide_single_column_transformer: + +Advanced columnwise operations +------------------------------ + +.. _single_column_transformer: + +The single column transformer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In cases where we want to apply a custom transformation to a series we need the |ApplyToCols| +structure to handle multiple columns, and if this transformation needs to be able to reject certain +columns and communicate this to |ApplyToCols|, we must to create a transformer from scratch +that raises this exception when appropriate: this can be done with the |SingleColumnTranformer| class. + +For instance, we might want to create a custom transformer specialized in parsing zip codes: +in this example, the zip codes need to have the format ``AB123``, that is two letters +followed by three digits. + +>>> import pandas as pd +>>> df = pd.DataFrame({'sent': ["AB123", "BD601", "HS014"], 'received': ["AB1C45", "DU3K93", "WB9M88"]}) +>>> df + sent received +0 AB123 AB1C45 +1 BD601 DU3K93 +2 HS014 WB9M88 + +We would like to be able to "unpack" the zip code so that we have a column for the +letters and one for the digits; the transformer should also be able to "reject" a column +if it does not satisfy the format we specify. A "rejected" column should be passed +through unchanged, as it cannot be handled by this particular transformer. + +We can therefore define a custom class that inherits from |SingleColumnTranformer| +and that raises |RejectColumn| if a column cannot be handled: + +>>> from skrub.core import RejectColumn, SingleColumnTransformer +>>> class ZipcodeParser(SingleColumnTransformer): +... def __init__(self): +... return +... def fit_transform(self, X, y=None): +... if any(X.map(len) != 5): +... raise RejectColumn('This transformer only takes zip codes of length 5.') +... else: +... letters = X.map(lambda s: s[:2]) +... try: +... numbers = X.map(lambda s: int(s[2:])) +... except: +... raise RejectColumn('Input zip codes must consist of two letters followed by three numbers.') +... return(pd.DataFrame({'letters': letters, 'numbers': numbers})) +>>> ZipcodeParser().fit_transform(df["sent"]) + letters numbers +0 AB 123 +1 BD 601 +2 HS 14 + +We can use |ApplyToCols| to apply this transformer to the entire dataframe at once, +and set ``allow_reject=True`` to let rejected columns through without changes: + +>>> from skrub import ApplyToCols +>>> ApplyToCols(ZipcodeParser(), allow_reject=True).fit_transform(df) +letters numbers received +0 AB 123 AB1C45 +1 BD 601 DU3K93 +2 HS 14 WB9M88 + +Note how the ``"received"`` column has been "rejected" and passed through unmodified. + + + +Rejection handling with |ApplyToCols| and |RejectColumn| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The combination |ApplyToCols| and |RejectColumn| allows allows flexible manipulation +and error checking of dataframe. In the previous example, we decided to ignore the +malformed ``"received"`` column by setting ``allow_reject=True``. If, however, +we want our transformer to fail if it encounters a column that it cannot parse, +we can keep the default value of ``allow_reject=False``, so that the transform +fails as soon as a malformed column is encountered: + +>>> ApplyToCols(ZipcodeParser()).fit_transform(df) # doctest: +SKIP +Traceback (most recent call last): + ... +skrub.core.RejectColumn: This transformer only takes zip codes of length 5. +Transformer ZipcodeParser.fit_transform failed on column 'received'. See above for the full traceback. +Letting rejected columns through can be useful for situations in which we do not +know the content of a column in advance, like when we are trying to convert to +datetime columns in a dataframe, without knowing which ones actually contain dates. + +>>> from skrub import ToDatetime +>>> df = pd.DataFrame(dict(birthday=["29/01/2024"], city=["London"])) +>>> df + birthday city +0 29/01/2024 London +>>> df.dtypes +birthday ... +city ... +dtype: object + +Converting a datetime column would work: + +>>> ToDatetime().fit_transform(df["birthday"]) +0 2024-01-29 +Name: birthday, dtype: datetime64[...] + +While non-datetimes would raise |RejectColumn|: + +>>> ToDatetime().fit_transform(df["city"]) +Traceback (most recent call last): + ... +skrub.core.RejectColumn: Could not find a datetime format for column 'city'. + +The ``allow_reject`` parameter in |ApplyToCols| allows to apply the same transformer +to all columns without having to worry about which columns will actually be converted: +here, |ToDatetime| is applied only to the "birthday" column, while "city" is passed +through unchanged and no exception is raised. + +>>> to_datetime = ApplyToCols(ToDatetime(), allow_reject=True) +>>> transformed = to_datetime.fit_transform(df) +>>> transformed + birthday city +0 2024-01-29 London + +We can see that the only column that has a transformer is "birthday": + +>>> to_datetime.transformers_ +{'birthday': ToDatetime()} diff --git a/skrub/_docs/modules/column_level_featurizing/feature_engineering_categorical.rst b/skrub/_docs/modules/column_level_featurizing/feature_engineering_categorical.rst new file mode 100644 index 000000000..2e159f687 --- /dev/null +++ b/skrub/_docs/modules/column_level_featurizing/feature_engineering_categorical.rst @@ -0,0 +1,132 @@ + +.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` +.. |TextEncoder| replace:: :class:`~skrub.TextEncoder` +.. |MinHashEncoder| replace:: :class:`~skrub.MinHashEncoder` +.. |GapEncoder| replace:: :class:`~skrub.GapEncoder` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |tabular_pipeline| replace:: :func:`~skrub.tabular_pipeline` +.. |OneHotEncoder| replace:: :class:`~sklearn.preprocessing.OneHotEncoder` +.. |OrdinalEncoder| replace:: :class:`~sklearn.preprocessing.OrdinalEncoder` + +.. _user_guide_feature_engineering_categorical: + +Encoding string and text columns as numeric features +====================================================== + +In skrub, categorical features are features that are not parsed as either numbers +or datetimes. They may have a Categorical datatype, or they may simply be strings. +These features are very common in practice, and there are various strategies that +can be employed to handle them. + +A common approach is to use the |OneHotEncoder| or the |OrdinalEncoder| on +categorical features, but both approaches have limitations. The |OneHotEncoder| +becomes expensive when the number of distinct values becomes large, while the +|OrdinalEncoder| introduces order in features that may not have an inherent ordering. + +To address these shortcomings and generalize to more columns, skrub implements +four different transformers, each with its own pros and cons. + +All encoders work like regular scikit-learn transformers. All encoders +take a parameter ``n_components`` to specify how many features should +be generated for each input feature. + +>>> import pandas as pd +>>> from skrub import StringEncoder + +>>> X = pd.Series([ +... "The professor snatched a good interview out of the jaws of these questions.", +... "Bookmarking this to watch later.", +... "When you don't know the lyrics of the song except the chorus", +... ], name='video comments') + +>>> encoder = StringEncoder(n_components=2) + +The result of the ``.fit_transform`` is a new dataframe that contains as many columns +as the number of components specified (here, 2). +Features generated by each encoder (except the |GapEncoder|) are always named after +the original column name (here, ``"video comments"``), followed by the index of the +resulting feature. + +>>> encoder.fit_transform(X) # doctest: +SKIP + video comments_0 video comments_1 +0 1.322969 -0.163066 +1 0.379689 1.659318 +2 1.306402 -0.317126 + +The |GapEncoder| names the columns after the categories it estimates from the +data, which are built by capturing combinations of substrings that frequently co-occur. +More information on the functioning and the theoretical background of the |GapEncoder| +is available in the documentation of the encoder itself. + +>>> from skrub import GapEncoder +>>> GapEncoder(n_components=2).fit_transform(X) # doctest: +SKIP + video comments: bookmarking, except, lyrics video comments: professor, questions, interview +0 0.000786 1.360704 +1 0.559531 0.000717 +2 0.982307 0.099680 + +Choosing the right encoder for the job +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- |StringEncoder|: **the default encoder, strong in most cases**: A strong and quick + baseline for both short strings with high cardinality and long text. This encoder + computes the n-gram frequency using tf-idf vectorization, followed by truncated SVD + (`Latent Semantic Analysis `_). + This is the default encoder used by the |TableVectorizer| and the |tabular_pipeline|. + +- |TextEncoder|: **language model-based, strong on text but expensive to run**: + This encoder encodes string features using pretrained language models from the + HuggingFace Hub. It is a wrapper around `sentence-transformers `_ + compatible with the scikit-learn API and usable in pipelines. Best for free-flowing + text and when columns include context found in the pretrained model (e.g., names of + cities etc.). Note that this encoder can take a very long time to train, especially + on large datasets and on CPU. The |TextEncoder| has additional dependencies that + are not included in the standard skrub installation. + Refer to :ref:`installation_instructions` for info on how to prepare the + environment. + +- |MinHashEncoder|: **very fast encoder, but not as effective as the others**: + This encoder decomposes strings into n-grams, then applies the MinHash method to + convert them into numeric features. Fast to train, but features usually yield worse + results compared to other methods. + +- |GapEncoder|: **an interpretable, if slower encoder**: The |GapEncoder| estimates + "latent categories" on the training data by finding common n-grams between strings, + then encodes the categories as real numbers. It allows access to grouped features + via ``.get_feature_names_out()``, which allows for better interpretability. This + encoder may require a long time to train. + +.. list-table:: + :header-rows: 1 + :widths: 15 15 25 20 25 + + * - Encoder + - Training time + - Performance on categorical data + - Performance on text data + - Notes + * - |StringEncoder| + - Fast + - Good + - Good + - + * - |TextEncoder| + - Very slow + - Mediocre to good + - Very good + - Requires the ``transformers`` package to be installed + * - |GapEncoder| + - Slow + - Good + - Mediocre to good + - Interpretable + * - |MinHashEncoder| + - Very fast + - Mediocre to good + - Mediocre + - + +:ref:`This example ` and this +`blog post `_ +include a more systematic analysis of each method. +The docstrings of each encoder provide additional details on how they work. diff --git a/skrub/_docs/modules/column_level_featurizing/feature_engineering_datetimes.rst b/skrub/_docs/modules/column_level_featurizing/feature_engineering_datetimes.rst new file mode 100644 index 000000000..4df4642bb --- /dev/null +++ b/skrub/_docs/modules/column_level_featurizing/feature_engineering_datetimes.rst @@ -0,0 +1,276 @@ +.. |ToDatetime| replace:: :class:`~skrub.ToDatetime` +.. |to_datetime| replace:: :func:`~skrub.to_datetime` +.. |DatetimeEncoder| replace:: :class:`~skrub.DatetimeEncoder` + +.. _user_guide_feature_engineering_datetimes: + +Handling datetimes: parsing from strings and encoding as numbers +================================================================ +Depending on the input data, timestamps and dates can cause issues, or require +specific parsing. For example, reading input data stored in ``csv`` format results +in datetime columns that are treated as strings. + +In such cases, parsing columns that contain timestamps or dates so that they are +treated as datetime objects allows to make use of advanced functionalities available +in the standard Python library, Pandas and Polars. + +Skrub provides objects that help with parsing such data (|ToDatetime|), as well +as the |DatetimeEncoder|, a datetime-specific encoder that feature engineers +datetime columns. + + +Parsing Datetime Strings with |ToDatetime| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Skrub provides helpers to parse datetime string columns automatically: + +- The |ToDatetime| transformer learns a mapping between columns and their formats. + It then applies this mapping during the transform step. +- The |to_datetime| function applies the |ToDatetime| transformer to all columns + in the dataframe, and tries to parse them as datetimes. The format can be + inferred or user-specified with the ``format`` argument. + + +>>> import pandas as pd +>>> s = pd.Series(["2024-05-05T13:17:52", None, "2024-05-07T13:17:52"], name="when") +>>> s +0 2024-05-05T13:17:52 +1 ... +2 2024-05-07T13:17:52 +Name: when, dtype: ... + +>>> from skrub import ToDatetime + +>>> to_dt = ToDatetime() +>>> to_dt.fit_transform(s) +0 2024-05-05 13:17:52 +1 NaT +2 2024-05-07 13:17:52 +Name: when, dtype: datetime64[...] + +The attributes ``format_``, ``output_dtype_``, ``output_time_zone_`` +record information about the conversion result. + +>>> to_dt.format_ +'%Y-%m-%dT%H:%M:%S' +>>> to_dt.output_dtype_ +dtype('>> to_dt.output_time_zone_ is None +True + +Once |ToDatetime| was successfully fitted, ``transform`` will always try to +parse datetimes with the same format and output the same ``dtype``. Entries that +fail to be converted result in a null value: + +>>> s = pd.Series(["2024-05-05T13:17:52", None, "2024-05-07T13:17:52"], name="when") +>>> to_dt = ToDatetime().fit(s) +>>> to_dt.transform(s) +0 2024-05-05 13:17:52 +1 NaT +2 2024-05-07 13:17:52 +Name: when, dtype: datetime64[...] +>>> s = pd.Series(["05/05/2024", None, "07/05/2024"], name="when") +>>> to_dt.transform(s) +0 NaT +1 NaT +2 NaT +Name: when, dtype: datetime64[...] + + +Dealing with Time zones +^^^^^^^^^^^^^^^^^^^^^^^ + +During ``fit``, parsing strings that contain fixed offsets results in datetimes +in UTC. Mixed offsets are supported and will all be converted to UTC. + +>>> s = pd.Series(["2020-01-01T04:00:00+02:00", "2020-01-01T04:00:00+03:00"]) +>>> to_dt.fit_transform(s) +0 2020-01-01 02:00:00+00:00 +1 2020-01-01 01:00:00+00:00 +dtype: datetime64[..., UTC] +>>> to_dt.format_ +'%Y-%m-%dT%H:%M:%S%z' +>>> to_dt.output_time_zone_ +'UTC' + +Strings with no timezone indication result in naive datetimes: + +>>> s = pd.Series(["2020-01-01T04:00:00", "2020-01-01T04:00:00"]) +>>> to_dt.fit_transform(s) +0 2020-01-01 04:00:00 +1 2020-01-01 04:00:00 +dtype: datetime64[...] +>>> to_dt.output_time_zone_ is None +True + +During ``transform``, outputs are cast to the same ``dtype`` that was found +during ``fit``. This includes the timezone, which is converted if necessary. + +>>> s_paris = pd.to_datetime( +... pd.Series(["2024-05-07T14:24:49", "2024-05-06T14:24:49"]) +... ).dt.tz_localize("Europe/Paris") +>>> s_paris +0 2024-05-07 14:24:49+02:00 +1 2024-05-06 14:24:49+02:00 +dtype: datetime64[..., Europe/Paris] +>>> to_dt = ToDatetime().fit(s_paris) +>>> to_dt.output_dtype_ +datetime64[..., Europe/Paris] + +Here our converter is set to output datetimes with nanosecond resolution, +localized in "Europe/Paris". + +We may have a column in a different timezone: + +>>> s_london = s_paris.dt.tz_convert("Europe/London") +>>> s_london +0 2024-05-07 13:24:49+01:00 +1 2024-05-06 13:24:49+01:00 +dtype: datetime64[..., Europe/London] + +Here the timezone is "Europe/London" and the times are offset by 1 hour. During +``transform`` datetimes will be converted to the original dtype and the +"Europe/Paris" timezone: + +>>> to_dt.transform(s_london) +0 2024-05-07 14:24:49+02:00 +1 2024-05-06 14:24:49+02:00 +dtype: datetime64[..., Europe/Paris] + +Moreover, we may have to transform a timezone-naive column whereas the +transformer was fitted on a timezone-aware column. Note that this is somewhat a +corner case unlikely to happen in practice if the inputs to ``fit`` and +``transform`` come from the same dataframe. + +In this case, we make the arbitrary choice to assume that the timezone-naive +datetimes are in UTC. + +>>> s_naive = s_paris.dt.tz_convert(None) +>>> to_dt.transform(s_naive) +0 2024-05-07 14:24:49+02:00 +1 2024-05-06 14:24:49+02:00 +dtype: datetime64[..., Europe/Paris] + +Conversely, a transformer fitted on a timezone-naive column can convert +timezone-aware columns. Here also, we assume the naive datetimes were in UTC. + +>>> to_dt = ToDatetime().fit(s_naive) +>>> to_dt.transform(s_london) +0 2024-05-07 12:24:49 +1 2024-05-06 12:24:49 +dtype: datetime64[...] + +Caveats when dealing with month first/day first conventions +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +When parsing strings in one of the formats above, |ToDatetime| tries to guess +if the month comes first (USA convention) or the day (rest of the world) from +the data. + +>>> s = pd.Series(["05/23/2024"]) +>>> to_dt.fit_transform(s) +0 2024-05-23 +dtype: datetime64[...] +>>> to_dt.format_ +'%m/%d/%Y' + +Here we could infer ``'%m/%d/%Y'`` because there is no 23rd month in a year. +Similarly, + +>>> s = pd.Series(["23/05/2024"]) +>>> to_dt.fit_transform(s) +0 2024-05-23 +dtype: datetime64[...] +>>> to_dt.format_ +'%d/%m/%Y' + +In the case where it cannot be inferred, the USA convention is used: + +>>> s = pd.Series(["03/05/2024"]) +>>> to_dt.fit_transform(s) +0 2024-03-05 +dtype: datetime64[...] +>>> to_dt.format_ +'%m/%d/%Y' + +.. _user_guide_datetime_encoder: + +Encoding and Feature Engineering with |DatetimeEncoder| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Once datetime columns have been parsed, they can be encoded as numeric features with +the |DatetimeEncoder|, by extracting temporal features (year, month, day, +hour, etc.). No timezone conversion is done; the timezone +in the feature is retained. The |DatetimeEncoder| rejects non-datetime columns, +so it should only be applied after conversion using |ToDatetime|. +If the input column is timezone aware, the extracted features will be in the column's +timezone; this is normally the case when the datetime column has been encoded with |ToDatetime|. + +>>> import pandas as pd +>>> login = pd.to_datetime( +... pd.Series( +... ["2024-05-13T12:05:36", None, "2024-05-15T13:46:02"], name="login") +... ) +>>> login +0 2024-05-13 12:05:36 +1 NaT +2 2024-05-15 13:46:02 +Name: login, dtype: datetime64[...] +>>> from skrub import DatetimeEncoder + +>>> DatetimeEncoder().fit_transform(login) +login_year login_month login_day login_hour login_total_seconds +0 2024.0 5.0 13.0 12.0 1.715602e+09 +1 NaN NaN NaN NaN NaN +2 2024.0 5.0 15.0 13.0 1.715781e+09 + +Additionally, the |DatetimeEncoder| can include the following features: + +- Number of seconds from epoch (``add_total_seconds``, ``True`` by default) +- Day of the week (``add_weekday``) +- Day of the year (``add_day_of_year``) + +Periodic encoding is supported through trigonometric (circular) and spline +encoding: set the ``periodic_encoding`` parameter to ``circular`` or ``spline``. + +.. figure:: /_static/periodic_features.png + :alt: Periodic encoding of datetime features + :align: center + :width: 70% + + Example of periodic encoding of datetime features using circular and spline methods. + +Note that if ``periodic_encoding`` is set, the respective features are removed +to reduce redundancy: + +>>> encoder = DatetimeEncoder() +>>> encoder.fit_transform(login).columns +Index(['login_year', 'login_month', 'login_day', 'login_hour', + 'login_total_seconds'], + dtype=...) +>>> from sklearn.pipeline import make_pipeline +>>> encoder = make_pipeline(ToDatetime(), DatetimeEncoder(periodic_encoding="circular")) +>>> encoder.fit_transform(login).columns +Index(['login_year', 'login_total_seconds', 'login_month_circular_0', + 'login_month_circular_1', 'login_day_circular_0', + 'login_day_circular_1', 'login_hour_circular_0', + 'login_hour_circular_1'], + dtype=...) + + +The |DatetimeEncoder| uses hardcoded values for generating periodic features. +The period of each feature is: + +- ``month``: 12 (month in year) +- ``day``: 30 (day in month) +- ``hour``: 24 (hour in day) +- ``weekday``: 7 (day in week) + +Additionally, we specify the number of splines for each feature to avoid +generating too many features: + +- ``month``: 12 +- ``day``: 4 +- ``hour``: 12 +- ``weekday``: 7 + +All extracted features are provided as ``float32`` columns. diff --git a/skrub/_docs/modules/column_level_featurizing/feature_engineering_numerical.rst b/skrub/_docs/modules/column_level_featurizing/feature_engineering_numerical.rst new file mode 100644 index 000000000..e848d073a --- /dev/null +++ b/skrub/_docs/modules/column_level_featurizing/feature_engineering_numerical.rst @@ -0,0 +1,106 @@ +.. |SquashingScaler| replace:: :class:`~skrub.SquashingScaler` +.. |ToFloat| replace:: :class:`~skrub.ToFloat` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |Cleaner| replace:: :class:`~skrub.Cleaner` +.. |RobustScaler| replace:: :class:`~sklearn.preprocessing.RobustScaler` +.. |RejectColumn| replace:: :class:`~skrub.core.RejectColumn` + +.. _user_guide_feature_engineering_numeric_to_float: + +Parsing and scaling numeric features +==================================== + +Converting heterogeneous numeric values to uniform float32 +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Many tabular datasets stored as csv files contain numeric information stored as +strings, mixed representations, locale-specific formats, or other non-standard +encodings. +Common issues include: + +- Thousands separators (``1,234.56`` or ``1 234,56``) +- Use of apostrophes as separators (``4'567.89``) +- Negative numbers encoded inside parentheses (``(1,234.56)``) +- String columns that contain mostly numeric values, but with occasional invalid entries + +To provide consistent numeric behavior, skrub includes the |ToFloat| transformer, +which standardizes all numeric-like columns to ``float32`` and handles a wide +range of real-world formatting issues automatically. Columns that cannot be parsed +are rejected with a |RejectColumn| exception. + +Converting numbers to ``float32`` has the advantage of reducing memory pressure, +while retaining most of the information for training models. + +>>> import pandas as pd +>>> from skrub import ToFloat +>>> s = pd.Series(['1.1', None, '3.3'], name='x') +>>> to_float = ToFloat() +>>> to_float.fit_transform(s) +0 1.1 +1 NaN +2 3.3 +Name: x, dtype: float32 + +If the transformer is fitted correctly, invalid values encountered at transform +time are replaced by ``NaN``: + +>>> to_float.transform(pd.Series(['3.3', 'invalid'], name='x')) +0 3.3 +1 NaN +Name: x, dtype: float32 + +Locale-dependent decimal separators can be handled by specifying the +``decimal`` and ``thousand`` parameter. Here we use comma as decimal separator, and +a space as thousands separators: + +>>> s = pd.Series(["4 567,89", "12 567,89"], name="x") +>>> ToFloat(decimal=",", thousand=" ").fit_transform(s) +0 4567.8... +1 12567.8... +Name: x, dtype: float32 + +In some contexts, negative numbers may be represented with parentheses, instead of +using ``-``. This case is handled by the ``parentheses`` boolean parameter: + +>>> s = pd.Series(["-1,234.56", "(1,234.56)"], name="neg") +>>> ToFloat(thousand=",", parentheses=True).fit_transform(s) +0 -1234.5... +1 -1234.5... +Name: neg, dtype: float32 + + +.. _user_guide_squashing_scaler: + +Robust scaling of numeric features using |SquashingScaler| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The |SquashingScaler| is a robust scaler for numeric features, particularly +useful when features include outliers (such as infinite values); missing values +are left unchanged (they are not interpolated). +The |SquashingScaler| centers and scales the data in such a way that outliers are +less likely to skew the final result compared to alternative methods. + +Based on the specified ``quantile_range`` parameter, the scaler employs a scikit-learn +|RobustScaler| to rescale the values in a way that the quantile range occupies +interval of length two, centering the median to zero. It therefore ensures that +inliers are spread to a reasonable range. Afterwards, it uses a smooth clipping +function to ensure all values (including outliers and infinite values) are in the +range ``[-max_absolute_value, max_absolute_value]``. By default, +``max_absolute_value=3``. + +>>> import pandas as pd +>>> import numpy as np +>>> from skrub import SquashingScaler + +>>> X = pd.DataFrame(dict(col=[np.inf, -np.inf, 3, -1, np.nan, 2])) +>>> SquashingScaler(max_absolute_value=3).fit_transform(X) +array([[ 3. ], + [-3. ], + [ 0.49319696], + [-1.34164079], + [ nan], + [ 0. ]]) + +More information about the theory behind the scaler is available in the +|SquashingScaler| documentation, while this +:ref:`working example ` compares +different scalers when used on data that include outliers. diff --git a/skrub/_docs/modules/data_ops/basics/building_data_ops_plan.rst b/skrub/_docs/modules/data_ops/basics/building_data_ops_plan.rst new file mode 100644 index 000000000..00e42f316 --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/building_data_ops_plan.rst @@ -0,0 +1,94 @@ +.. currentmodule:: skrub + +.. _user_guide_data_ops_plan: + +Building a simple DataOps plan +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Let's build a simple DataOps plan that adds two variables together. + +We start by declaring the variables: + +>>> import skrub + +>>> a = skrub.var("a") +>>> b = skrub.var("b") + +We then apply transformations (in this case, an addition) composing more complex DataOps. + +>>> c = a + b +>>> c + + +Finally, we can evaluate the plan by passing the **environment** in which the +plan should be evaluated. The environment is a dictionary that maps variable names +to their values. + +>>> c.skb.eval({"a": 10, "b": 6}) +16 + +As shown above, the special ``.skb`` attribute allows to interact with the DataOp +object itself, and :meth:`.skb.eval() ` evaluates the DataOp plan. +By default, :meth:`.skb.eval() ` uses the values passed in the +variable definitions, but it can also take an explicit environment +dictionary as an argument. + + +Finally, we can export the plan as a ``Learner`` that can be fitted and applied to +new data: + +>>> learner = c.skb.make_learner() +>>> learner.fit_transform({"a": 10, "b": 7}) +17 + +When using Data Ops, it is important to ensure that all operations are being tracked +by acting on the Data Ops, rather than (for example) the starting dataframe. +Consider the following example: + +>>> import pandas as pd +>>> df = pd.DataFrame({"col": [1, 2, 3]}) +>>> df + col +0 1 +1 2 +2 3 +>>> df_do = skrub.var("df", df) +>>> df_do + +Result: +――――――― + col +0 1 +1 2 +2 3 + +``df_do`` is a Data Op that wraps ``df``, so its preview shows the content of ``df``. +Then, if we now modify ``df_do`` by doubling the column, we can see that both steps +(the creation of the variable, and the doubling) are now tracked by the final +Data Op. + +>>> df_doubled = df_do.assign(col=df_do["col"]*2) +>>> df_doubled + +Result: +――――――― + col +0 2 +1 4 +2 6 +>>> print(df_doubled.skb.describe_steps()) +Var 'df' +( Var 'df' )* +GetItem 'col' +BinOp: mul +CallMethod 'assign' +* Cached, not recomputed + +On the other hand, working directly on ``df`` leads us to the same result, but +the actual operations are not being tracked. +By working only on Data Ops we ensure that all the operations done on the data +are added correctly to the computational graph, which then allows the resulting +learner to execute all steps as intended. + +See :ref:`sphx_glr_auto_tutorials_1110_data_ops_intro.py` for an introductory +example on how to use skrub DataOps on a single dataframe. diff --git a/skrub/_docs/modules/data_ops/basics/control_flow.rst b/skrub/_docs/modules/data_ops/basics/control_flow.rst new file mode 100644 index 000000000..7cd1fc31a --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/control_flow.rst @@ -0,0 +1,176 @@ +.. currentmodule:: skrub + +.. _user_guide_data_ops_control_flow: + +Control flow in DataOps: eager and deferred evaluation +====================================================== + +DataOps represent computations that have not been executed yet, and will +only be triggered when we call :meth:`.skb.eval() `, or when we +create the pipeline with :meth:`.skb.make_learner() ` and +call one of its methods such as ``fit()``. + +This means we cannot use standard Python control flow statements such as ``if``, +``for``, ``with``, etc. with DataOps, because those constructs would execute +immediately. + +>>> import pandas as pd +>>> import skrub +>>> orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "pen", "fork"], +... "price": [1.5, None, 1.5, 2.2], +... "qty": [1, 1, 2, 4], +... } +... ) +>>> orders = skrub.var("orders", orders_df) +>>> for column in orders.columns: +... pass +Traceback (most recent call last): + ... +TypeError: This object is a DataOp that will be evaluated later, when your learner runs. So it is not possible to eagerly iterate over it now. + +We get an error because the ``for`` statement tries to iterate immediately +over the columns. However, ``orders.columns`` is not an actual list of +columns: it is a skrub DataOp that will produce a list of columns, later, +when we run the computation. + +This remains true even if we have provided a value for ``orders`` and we can +see a result for that value: + +>>> orders.columns + +Result: +――――――― +Index(['item', 'price', 'qty'], dtype=...) + +The "result" we see is an *example* result that the computation produces for the +data we provided. But we want to fit our pipeline and apply it to different +datasets, for which it will return a new object every time. So even if we see a +preview of the output on the data we provided, ``orders.columns`` still +represents a future computation that remains to be evaluated. + +Therefore, we must delay the execution of the ``for`` statement until the computation +actually runs and ``orders.columns`` has been evaluated. + +We can achieve this by defining a function that contains the control flow logic +we need, and decorating it with :func:`deferred`. This decorator defers the execution +of the function: when we call it, it does not run immediately. Instead, it returns +a skrub DataOp that wraps the function call. The original function is only +executed when the DataOp is evaluated, and will return the result as a DataOp. + +>>> @skrub.deferred +... def with_upper_columns(df): +... new_columns = [c.upper() for c in df.columns] +... return df.set_axis(new_columns, axis="columns") + +>>> with_upper_columns(orders) + +Result: +――――――― + ITEM PRICE QTY +0 pen 1.5 1 +1 cup NaN 1 +2 pen 1.5 2 +3 fork 2.2 4 + +When the computation runs, ``orders`` will be evaluated first and the result (an +actual dataframe) will be passed as the ``df`` argument to our function. In practice, +the code inside a deferred function is completely equivalent to eager code, so +it is possible to use any Python control flow statement inside it, as well as +act on the data as if it were a regular DataFrame. + +Within a function decorated with :func:`deferred`, objects are evaluated eagerly, +so it is possible to use standard Python control flow statements such as +``if``, ``for``, and it is possible to treat the inputs as if they were +regular objects (e.g., a Pandas DataFrame or Series). + +When the first argument to our function is a skrub DataOp, rather than +applying ``deferred`` and calling the function as shown above we can use +:meth:`.skb.apply_func() `: + +>>> def with_upper_columns(df): +... new_columns = [c.upper() for c in df.columns] +... return df.set_axis(new_columns, axis="columns") + +>>> orders.skb.apply_func(with_upper_columns) + +Result: +――――――― + ITEM PRICE QTY +0 pen 1.5 1 +1 cup NaN 1 +2 pen 1.5 2 +3 fork 2.2 4 + +Unpacking multiple outputs from deferred functions +-------------------------------------------------- + +When a deferred function returns more than one value, you cannot unpack the +result directly because unpacking iterates over the result. Iteration is not +supported on DataOps until evaluation. + +In general, it is recommended that deferred functions return a single +value whenever possible. Returning multiple outputs should be avoided unless +strictly necessary, as it makes downstream usage more complex. + +Instead, keep the result as a single DataOp and index into it: + +>>> test = skrub.var("test", [1, 2]) +>>> @skrub.deferred +... def process_test_data(test): +... left = test[0] +... right = test[1] +... return left, right +>>> res = test.skb.apply_func(process_test_data) +>>> left = res[0] +>>> right = res[1] + +:func:`deferred` is useful not only for our own functions, but also when we +need to call module-level functions from a library. For example, to delay the +loading of a CSV file, we could write something like: + +>>> csv_path = skrub.var("csv_path") +>>> data = skrub.deferred(pd.read_csv)(csv_path) + +or, with ``apply_func``: + +>>> data = csv_path.skb.apply_func(pd.read_csv) + +Another consequence of the fact that DataOps are evaluated lazily (we are +building a pipeline, not immediately computing a single result), any +transformation that we apply must not modify its input, but leave it unchanged +and return a new value. + +Consider the transformers in a scikit-learn pipeline: each computes a new +result without modifying its input. + +>>> orders['total'] = orders['price'] * orders['qty'] +Traceback (most recent call last): + ... +TypeError: Do not modify a DataOp in-place. Instead, use a function that returns a new value. This is necessary to allow chaining several steps in a sequence of transformations. +For example if df is a pandas DataFrame: +df = df.assign(new_col=...) instead of df['new_col'] = ... + +Note the suggestion in the error message: using :meth:`pandas.DataFrame.assign`. +When we do need assignments or in-place transformations, we can put them in a +:func:`deferred` function. But we should make a (shallow) copy of the inputs and +return a new value. + +Finally, there are other situations where using :func:`deferred` can be helpful: + +- When we have many nodes in our graph and want to collapse a sequence of steps into + a single function call that appears as a single node. +- When certain function calls need to be deferred until the full computation + runs, because they depend on the runtime environment, or on objects that + cannot be pickled with the rest of the computation graph (for example, opening + and reading a file). + +.. rubric:: Examples + +- See :ref:`sphx_glr_auto_examples_data_ops_1110_data_ops_intro.py` for an introductory + example on how to use skrub DataOps on a single dataframe. +- See :ref:`sphx_glr_auto_examples_data_ops_1120_multiple_tables.py` for an example + of how skrub DataOps can be used to process multiple tables using dataframe APIs. +- See :ref:`sphx_glr_auto_examples_data_ops_1130_choices.py` for an example of + hyper-parameter tuning using skrub DataOps. diff --git a/skrub/_docs/modules/data_ops/basics/data_ops_vs_alternatives.rst b/skrub/_docs/modules/data_ops/basics/data_ops_vs_alternatives.rst new file mode 100644 index 000000000..bffd79b2d --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/data_ops_vs_alternatives.rst @@ -0,0 +1,67 @@ +.. currentmodule:: skrub + +.. _user_guide_data_ops_vs_alternatives: + +How do skrub Data Ops differ from the alternatives? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +Skrub DataOps and scikit-learn :class:`sklearn.pipeline.Pipeline` +==================================================================== + +Scikit-learn pipelines represent a linear sequence of transformations on one +table with a fixed number of rows. + +.. image:: ../../../_static/sklearn_pipeline.svg + :width: 500 + +Skrub DataOps, on the other hand, can manipulate any number of variables. +The transformation they perform is not a linear sequence but any Directed +Acyclic Graph of computations. Take the following example, where our task is to predict +item price in dollars: + +.. image:: ../../../_static/dataops_graph.svg + +- Here we use three input variables: two tables ("Items" and "Prices") and a + float ("euro_dollar_rate"). +- For this regression task, we have declared which intermediary step can be + considered as the design matrix X (shown in blue) and as the target y + (shown in orange). +- Akin to scikit-learn pipelines, we apply an estimator (Ridge) at the end of the + processing. + +The rest of this user guide will detail how the DataOps work. + + +Skrub DataOps and orchestrators like Apache Airflow +=================================================================== + +Skrub pipelines are not an `orchestrator `_ +and do not offer capabilities for scheduling runs or provisioning resources and +environments. Instead, they are a generalization of scikit-learn pipelines, which +can still be used within an orchestrator. + +Skrub DataOps and other skrub objects, like :func:`~skrub.tabular_pipeline` +=============================================================================== + +Skrub DataOps are built to maximize flexibility in the construction of complex +pre-processing and machine learning pipelines. On the other hand, the main intent +of skrub objects such as :func:`~skrub.tabular_pipeline` and +:class:`~skrub.TableVectorizer` is to provide interfaces that for common +pre-processing tasks, and simple and robust baselines for +machine learning. As a result, these objects are more opinionated and +less flexible than DataOps. + +However, it is possible to combine DataOps and regular skrub and scikit-learn +transformers to improve their flexibility, particularly in multi-table scenarios. + +Can I use library "x" with skrub DataOps? +========================================== + +Yes, skrub DataOps are designed to be "transparent", so that any method used by +the underlying data structures (e.g., Pandas or Polars) can be accessed directly: +check :ref:`user_guide_direct_access_ref` for more details. +All DataOps-specific operations are available through the ``.skb`` attribute, +which provides access to the DataOps namespace. Other library-specific methods +are available directly from the DataOp object, as if it were a regular object +(like a Pandas or Polars DataFrame or Series). diff --git a/skrub/_docs/modules/data_ops/basics/direct_access_methods.rst b/skrub/_docs/modules/data_ops/basics/direct_access_methods.rst new file mode 100644 index 000000000..a03116b3e --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/direct_access_methods.rst @@ -0,0 +1,81 @@ +.. currentmodule:: skrub +.. _user_guide_direct_access_ref: + +DataOps allow direct access to methods of the underlying data +============================================================= + +DataOps are designed to be flexible and allow direct access to the underlying data, +so that it is possible to use the APIs of the underlying data structures +(e.g., Pandas or Polars) directly: + +Suppose we want to process dataframes that look like this: + +>>> import pandas as pd +>>> orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "pen", "fork"], +... "price": [1.5, None, 1.5, 2.2], +... "qty": [1, 1, 2, 4], +... } +... ) +>>> orders_df + item price qty +0 pen 1.5 1 +1 cup NaN 1 +2 pen 1.5 2 +3 fork 2.2 4 + +We can create a skrub variable to represent that input: + +>>> import skrub +>>> orders = skrub.var("orders", orders_df) + +Because we know that a dataframe will be provided as input to the computation, we +can manipulate ``orders`` as if it were a regular dataframe. + +We can access its attributes: + +>>> orders.columns + +Result: +――――――― +Index(['item', 'price', 'qty'], dtype=...) + +Accessing items, indexing, slicing: + +>>> orders["item"].iloc[1:] + +Result: +――――――― +1 cup +2 pen +3 fork +Name: item, dtype: ... + +We can apply operators: + +>>> orders["price"] * orders["qty"] + +Result: +――――――― +0 1.5 +1 NaN +2 3.0 +3 8.8 +dtype: float64 + +We can call methods: + +>>> orders.assign(total=orders["price"] * orders["qty"]) + +Result: +――――――― + item price qty total +0 pen 1.5 1 1.5 +1 cup NaN 1 NaN +2 pen 1.5 2 3.0 +3 fork 2.2 4 8.8 + +Note that the original ``orders`` variable is not modified by the operations +above. Instead, each operation creates a new DataOp. DataOps cannot be +modified in-place, all operations that we apply must produce a new value. diff --git a/skrub/_docs/modules/data_ops/basics/using_previews.rst b/skrub/_docs/modules/data_ops/basics/using_previews.rst new file mode 100644 index 000000000..c7919096e --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/using_previews.rst @@ -0,0 +1,96 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_using_previews: + +Using previews for easier development and debugging +=================================================== + +To make interactive development easier without having to call ``eval()`` after +each step, it is possible to preview the result of a DataOp by passing a value +along with its name when creating a variable. + +>>> import skrub +>>> a = skrub.var("a", 10) # we pass the value 10 in addition to the name +>>> b = skrub.var("b", 6) +>>> c = a + b +>>> c # now the display of c includes a preview of the result + +Result: +――――――― +16 + +Previews are eager computations on the current data, and since they are computed +immediately they can spot errors early on: + +>>> import pandas as pd +>>> df = pd.DataFrame({"col": [1, 2, 3]}) +>>> a = skrub.var("a", df) # we pass the DataFrame as a value + +Next, we use the pandas ``drop`` column and try to drop a column without +specifying the axis: + +>>> a.drop("col") # doctest: +IGNORE_EXCEPTION_DETAIL +ELLIPSIS +Traceback (most recent call last): + ... +RuntimeError: Evaluation of '.drop()' failed. +You can see the full traceback above. The error message was: +KeyError: "['col'] not found in axis" + +Note that seeing results for the values we provided does *not* change the fact +that we are building a pipeline that we want to reuse, not just computing the +result for a fixed input. The displayed result is only preview of the output on +one example dataset. + +>>> c.skb.eval({"a": 3, "b": 2}) +5 + +It is not necessary to provide a value for every variable: it is however advisable +to do so when possible, as it allows to catch errors early on. + +Note: you can obtain the preview values with :meth:`DataOp.skb.get_data`, and +set different ones with :meth:`DataOp.skb.set_data`. + +Defining a default value for a variable +--------------------------------------- + +If we pass ``becomes_default=True`` to :func:`var`, the provided ``value`` is not +only an example value to use for previews but a default value for this variable +in all contexts -- then it is always optional to pass a value for it in the +environment, and if not found the default is used. + +>>> a = skrub.var('a', 0) +>>> a + +Result: +――――――― +0 +>>> b = skrub.var('b', 1, becomes_default=True) +>>> b + +Result (also the default value): +―――――――――――――――――――――――――――――――― +1 +>>> c = a + b +>>> c.skb.eval({'a': 10}) # the default 1 is used for 'b' +11 + +See the documentation of :func:`var` for details. + +Disabling previews and eager checks +----------------------------------- + +By default, as soon as a DataOp is defined, some validity checks are performed +and the preview results are computed eagerly. In very complex DataOps plans +(100+ nodes), running checks after adding each node can cause a noticeable overhead. +To avoid this, it is possible to disable eager checks with the ``"eager_data_ops"`` +is easily achieved with the ``"eager_data_ops"`` :ref:`configuration +` option. + + +>>> with skrub.config_context(eager_data_ops=False): +... # no checks are performed when b is defined so no error in the line below: +... b = skrub.var('a', 1) + skrub.var('a', 2) +... # checks are still performed (once) before the DataOp is actually used so +... # evaluating the DataOp, using .skb.make_learner() etc _would_ still raise: +... # b.skb.eval() ## raises ValueError: Choice and node names must be unique. +>>> b # Note there is no preview, even though we provided values for the variables + diff --git a/skrub/_docs/modules/data_ops/basics/what_are_data_ops.rst b/skrub/_docs/modules/data_ops/basics/what_are_data_ops.rst new file mode 100644 index 000000000..8392cc279 --- /dev/null +++ b/skrub/_docs/modules/data_ops/basics/what_are_data_ops.rst @@ -0,0 +1,34 @@ +.. currentmodule:: skrub + +.. _user_guide_data_ops_intro: + + +Basics of DataOps: the DataOps plan, variables, and learners +=============================================================== + +**DataOps** are special objects that encapsulate operations on data (such as +applying operators, or calling methods) to record the parameters so that they +can later be replayed on new data. DataOps objects can be combined into a +DataOps plan, which is a directed acyclic graph (DAG) of operations. + +DataOps have a ``.skb`` attribute that provides access to the DataOps namespace, +which contains methods for evaluating the DataOps plan, exporting the plan as a +**learner**, and various other utilities. Any other operation on a DataOp that is +not part of the DataOps namespace is instead applied to the underlying data: this +allows, for example, to make use of Pandas or Polars methods if the DataOp is +encapsulating a DataFrame or Series. + +The entry point of any DataOps plan is :class:`~skrub.var`, +a **variable**: a variable is an input to +our machine learning pipeline, such as a table of data, a target array, or more +generic data such as paths to files, or timestamps. + +Variables can be combined using operators and function calls to build more +complex DataOps plans. The plan is constructed implicitly as we apply these +operations, rather than by specifying an explicit list of transformations. + +At any point in the DataOps plan, we can export the resulting computation graph +as a **learner** with :meth:`~skrub.DataOp.skb.make_learner()`. A learner is a +special object akin to a scikit-learn estimator, but that takes as input a +dictionary of variables rather than a single design matrix ``X`` and a target array +``y``. diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/applying_different_transformers.rst b/skrub/_docs/modules/data_ops/ml_pipeline/applying_different_transformers.rst new file mode 100644 index 000000000..54901bee1 --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/applying_different_transformers.rst @@ -0,0 +1,153 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_applying_different_transformers: + +Applying different transformers using skrub selectors and DataOps +================================================================= + +It is possible to use skrub selectors to define which columns to apply +transformers to, and then apply different transformers to different subsets of +the data. + +For example, this can be useful to apply :class:`~skrub.TextEncoder` to columns +that contain free-flowing text, and :class:`~skrub.StringEncoder` to other string +columns that contain categorical data such as country names. + +Or, a string column may need to be encoded in an ordered way, like in the following +example with grades. + +>>> import skrub +>>> import pandas as pd +>>> data = { +... "subject": ["Math", "English", "History", "Science", "Art"], +... "grade": ["A", "B", "C", "A", "B"] +... } +>>> df = pd.DataFrame(data) +>>> grades = skrub.var("grades", df) +>>> grades + +Result: +――――――― + subject grade +0 Math A +1 English B +2 History C +3 Science A +4 Art B + +We encode the subjects with the :class:`~skrub.StringEncoder`: + +>>> from skrub import StringEncoder +>>> enc_subject = grades.skb.select(cols="subject").skb.apply(StringEncoder(n_components=2)) + +For the grades, we define a :func:`~skrub.deferred` function that maps the strings +to the order we want. +Remember that objects inside deferred functions are regular Python +objects (more detail in :ref:`user_guide_data_ops_control_flow`). + +>>> @skrub.deferred +... def encode_ordered(df): +... grade_order = {"A": 3, "B": 2, "C": 1} +... return df["grade"].map(grade_order) +>>> enc_grades = grades.skb.apply_func(encode_ordered) +>>> enc_grades + +Result: +――――――― +0 3 +1 2 +2 1 +3 3 +4 2 +Name: grade, dtype: int64 + +Finally, we combine the resulting dataframe and series using another deferred +function. + +>>> @skrub.deferred +... def combine(subjects, grades): +... subjects["grade"] = grades +... return subjects +>>> combine(enc_subject, enc_grades) # doctest: +SKIP + +Result: +――――――― + subject_0 subject_1 grade +0 1.800470e-07 1.704487e+00 3 +1 1.675736e-07 -1.998386e-08 2 +2 1.615310e+00 2.142048e-07 1 +3 -4.709333e-08 5.155605e-08 3 +4 -5.441046e-01 4.167525e-09 2 + + +In the next example, we apply a :class:`~skrub.StringEncoder` to columns +with high cardinality, a mathematical operation to columns with nulls, and a +:class:`~skrub.TableVectorizer` to all other columns. We use the skrub +:ref:`selectors ` to select the columns based on our requirements. + +>>> import pandas as pd +>>> import skrub +>>> orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "pen", "fork"], +... "price": [1.5, None, 1.5, 2.2], +... "qty": [1, 1, 2, 4], +... } +... ) +>>> orders = skrub.var("orders", orders_df) +>>> orders + +Result: +――――――― + item price qty +0 pen 1.5 1 +1 cup NaN 1 +2 pen 1.5 2 +3 fork 2.2 4 + +We create some selectors with different conditions: + +>>> from skrub import selectors as s +>>> high_cardinality = s.string() - s.cardinality_below(2) +>>> has_nulls = s.has_nulls() +>>> leftover = s.all() - high_cardinality - has_nulls + +>>> vectorizer = skrub.StringEncoder(n_components=2) +>>> vectorized_items = orders.skb.select(high_cardinality).skb.apply(vectorizer) +>>> vectorized_items # doctest: +SKIP + +Result: +――――――― + item_0 item_1 price qty +0 1.511858e+00 9.380015e-08 1.5 1 +1 -1.704687e-07 1.511858e+00 NaN 1 +2 1.511858e+00 9.380015e-08 1.5 2 +3 -5.458670e-09 -6.917769e-08 2.2 4 + +>>> vectorized_has_nulls = orders.skb.select(cols=has_nulls) * 11 +>>> vectorized_has_nulls + + Result: + ――――――― + price + 0 16.5 + 1 NaN + 2 16.5 + 3 24.2 +>>> everything_else = orders.skb.select(cols=leftover).skb.apply(skrub.TableVectorizer()) + +After encoding the columns, the resulting DataOps can be concatenated together +to obtain the final result: + +>>> encoded = ( +... everything_else.skb.concat([vectorized_items, vectorized_has_nulls], axis=1) +... ) +>>> encoded # doctest: +SKIP + qty item_0 item_1 price +0 1.0 1.594282e+00 -1.224524e-07 16.5 +1 1.0 9.228692e-08 1.473794e+00 NaN +2 2.0 1.594282e+00 -1.224524e-07 16.5 +3 4.0 7.643604e-09 6.080018e-01 24.2 + +More info on advanced column selection and manipulation be found in +:ref:`user_guide_selectors` and example +:ref:`sphx_glr_auto_examples_0090_apply_to_cols.py`. diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/applying_ml_estimators.rst b/skrub/_docs/modules/data_ops/ml_pipeline/applying_ml_estimators.rst new file mode 100644 index 000000000..f0bceb017 --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/applying_ml_estimators.rst @@ -0,0 +1,66 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_applying_ml_estimators: + +Applying machine-learning estimators +===================================== + +In addition to working directly with the API provided by the underlying data, +DataOps can also be used to apply machine-learning estimators from +scikit-learn or skrub to the data. This is done through the +:func:`.skb.apply() ` method: + +>>> import pandas as pd +>>> import skrub +>>> orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "pen", "fork"], +... "price": [1.5, None, 1.5, 2.2], +... "qty": [1, 1, 2, 4], +... } +... ) +>>> orders = skrub.var("orders", orders_df) + +>>> orders.skb.apply(skrub.TableVectorizer()) + +Result: +――――――― + item_cup item_fork item_pen price qty +0 0.0 0.0 1.0 1.5 1.0 +1 1.0 0.0 0.0 NaN 1.0 +2 0.0 0.0 1.0 1.5 2.0 +3 0.0 1.0 0.0 2.2 4.0 + +It is also possible to apply a transformer to a subset of the columns. The ``cols`` +parameter can also use a skrub :ref:`selector ` for finer +grained control. +Note that any column that is not selected is passed through unchanged, like below: + +>>> vectorized_orders = orders.skb.apply( +... skrub.StringEncoder(n_components=3), cols="item" +... ) +>>> vectorized_orders # doctest: +SKIP + +Result: +――――――― + item_0 item_1 item_2 price qty +0 9.999999e-01 1.666000e-08 4.998001e-08 1.5 1 +1 -1.332800e-07 -1.199520e-07 1.000000e+00 NaN 1 +2 9.999999e-01 1.666000e-08 4.998001e-08 1.5 2 +3 3.942477e-08 9.999999e-01 7.884953e-08 2.2 4 + +Then, we can export the transformation as a learner with +:meth:`.skb.make_learner() ` + +>>> learner = vectorized_orders.skb.make_learner(fitted=True) +>>> new_orders = pd.DataFrame({"item": ["fork"], "price": [2.2], "qty": [5]}) +>>> learner.transform({"orders": new_orders}) # doctest: +SKIP + item_0 item_1 item_2 price qty +0 5.984116e-09 1.0 -1.323546e-07 2.2 5 + +Note that here the learner is **fitted** on the preview data, but in general it can +be exported without fitting, and then fitted on new data provided as an environment +dictionary. By default, the learner is returned without fitting. + +>>> learner = vectorized_orders.skb.make_learner() +>>> learner.fit({"orders": orders_df}) +SkrubLearner(data_op=) diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/documenting_data_ops_plan.rst b/skrub/_docs/modules/data_ops/ml_pipeline/documenting_data_ops_plan.rst new file mode 100644 index 000000000..cb05d1010 --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/documenting_data_ops_plan.rst @@ -0,0 +1,40 @@ +.. currentmodule:: skrub +.. _user_guide_documenting_data_ops_plan: + +Documenting the DataOps plan with node names and descriptions +============================================================= + +We can improve the readability of the DataOps plan by giving names and descriptions +to the nodes in the plan. This is done with :meth:`.skb.set_name() ` +and :meth:`.skb.set_description() `. + +>>> import skrub +>>> a = skrub.var('a', 1) +>>> b = skrub.var('b', 2) +>>> c = (a + b).skb.set_description('the addition of a and b') +>>> c.skb.description +'the addition of a and b' +>>> d = c.skb.set_name('d') +>>> d.skb.name +'d' + +Both names and descriptions can be used to mark relevant parts of the learner, and +they can be accessed from the computational graph and the plan report. + +Additionally, names can be used to bypass the computation of a node and override its +result by passing it as a key in the ``environment`` dictionary. + +>>> e = d * 10 +>>> e + +Result: +――――――― +30 +>>> e.skb.eval() +30 +>>> e.skb.eval({'a': 10, 'b': 5}) +150 +>>> e.skb.eval({'d': -1}) # -1 * 10 +-10 + +More info can be found in section :ref:`user_guide_data_ops_truncating_dataplan`. diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/evaluating_debugging_data_ops.rst b/skrub/_docs/modules/data_ops/ml_pipeline/evaluating_debugging_data_ops.rst new file mode 100644 index 000000000..60440ffcd --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/evaluating_debugging_data_ops.rst @@ -0,0 +1,32 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_evaluating_debugging_dataops: + +Evaluating and debugging the DataOps plan with :meth:`.skb.full_report() ` +================================================================================================== + +All operations on DataOps are recorded in a computational graph, which can be +inspected with :meth:`.skb.full_report() `. This method +generates an HTML report that shows the full plan, including all nodes, their names, +descriptions, and the transformations applied to the data. It is possible to give a +title to the evaluation report this way: +``my_data_op.skb.full_report(title="my title")``. + +An example of the report can be found +`here <../../../_static/credit_fraud_report/index.html>`_. + +For each node in the plan, the report shows: + +- The name and the description of the node, if present. +- Predecessor and successor nodes in the computational graph. +- Where the code relative to the node is defined. +- The estimator fitted in the node along with its parameters (if applicable). +- The preview of the data at that node. + +Additionally, if computations fail in the plan, the report shows the offending +node and the error message, which can help in debugging the plan. + +By default, reports are saved in the ``skrub_data/execution_reports`` directory, but +they can be saved to a different location with the ``output_dir`` parameter. +Note that the default path can be altered with the +``SKRUB_DATA_DIR`` environment variable. See :ref:`user_guide_configuration_parameters` +for more details. diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/subsampling_data.rst b/skrub/_docs/modules/data_ops/ml_pipeline/subsampling_data.rst new file mode 100644 index 000000000..51a045feb --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/subsampling_data.rst @@ -0,0 +1,29 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_subsampling: + +Subsampling data for easier development and debugging +===================================================== + +If the data used for the preview is large, it can be useful to work on a +subsample of the data to speed up the development and debugging process. +This can be done by calling the :meth:`.skb.subsample() ` method +on a variable: this signals to skrub that what is shown when printing DataOps, or +returned by :meth:`.skb.preview() ` is computed on a subsample +of the data. + +Note that subsampling is "local": if it is applied to a variable, it only +affects the variable itself. This may lead to unexpected results and errors +if, for example, ``X`` is subsampled but ``y`` is not. + +Subsampling **is turned off** by default when we call other methods such as +:meth:`.skb.eval() `, +:meth:`.skb.cross_validate() `, +:meth:`.skb.train_test_split `, +:meth:`DataOp.skb.make_learner`, +:meth:`DataOp.skb.make_randomized_search`, etc. +However, all of those methods have a ``keep_subsampling`` parameter that we can +set to ``True`` to force using the subsampling when we call them. Note that +even if we set ``keep_subsampling=True``, subsampling is not applied when using +``predict``. + +See more details in a :ref:`full example `. diff --git a/skrub/_docs/modules/data_ops/ml_pipeline/using_part_of_data_ops_plan.rst b/skrub/_docs/modules/data_ops/ml_pipeline/using_part_of_data_ops_plan.rst new file mode 100644 index 000000000..83f5edd82 --- /dev/null +++ b/skrub/_docs/modules/data_ops/ml_pipeline/using_part_of_data_ops_plan.rst @@ -0,0 +1,80 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_truncating_dataplan: + +Using only a part of a DataOps plan +=================================== + +Besides documenting a DataOps plan, the :meth:`.skb.set_name() ` +has additional functions. By setting a name, we can: + +- Bypass the computation of that node and override its result by passing it as a + key in the ``environment`` argument. +- Truncate the computational graph after this node to obtain the intermediate result with + :meth:`SkrubLearner.truncated_after`. +- Retrieve that node and inspect the estimator that was fitted in it, if the + node was created with :meth:`.skb.apply() `. + +Here is a toy example with 4 steps: + +>>> def load_data(url): +... print("load: ", url) +... return [1, 2, 3, 4] + + +>>> def transform(x): +... print("transform") +... return [item * 10 for item in x] + + +>>> def agg(x): +... print("agg") +... return max(x) + + +>>> import skrub +>>> url = skrub.var("url") +>>> output = ( +... url.skb.apply_func(load_data) +... .skb.set_name("loaded") +... .skb.apply_func(transform) +... .skb.set_name("transformed") +... .skb.apply_func(agg) +... ) + +Above, we give a name to each intermediate result with ``.skb.set_name()`` so +that we can later refer to it when manipulating a fitted learner. + +>>> learner = output.skb.make_learner() +>>> learner.fit({"url": "file:///example.db"}) +load: file:///example.db +transform +agg +SkrubLearner(data_op=) + +>>> learner.transform({"url": "file:///example.db"}) +load: file:///example.db +transform +agg +40 + +Below, we bypass the data loading. Because we directly provide a value for the +intermediate result that we named ``"loaded"``, the corresponding computation is +skipped and the provided value is used instead. We can see that +``"load: ..."`` is not printed and that the rest of the computation proceeds +using ``[6, 5, 4]`` (instead of ``[1, 2, 3, 4]`` as before). + +>>> learner.transform({"loaded": [6, 5, 4]}) +transform +agg +60 + +Now we show how to stop at the result we named ``"transformed"``. With +``truncated_after``, we obtain a learner that computes that intermediate result +and returns it instead of applying the last transformation; note that ``"agg"`` +is not printed and we get the output of ``transform()``, not of ``agg()``: + +>>> truncated = learner.truncated_after("transformed") +>>> truncated.transform({"url": "file:///example.db"}) +load: file:///example.db +transform +[10, 20, 30, 40] diff --git a/skrub/_docs/modules/data_ops/validation/exporting_data_ops.rst b/skrub/_docs/modules/data_ops/validation/exporting_data_ops.rst new file mode 100644 index 000000000..2e462c4e5 --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/exporting_data_ops.rst @@ -0,0 +1,66 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_exporting: + +Exporting the DataOps plan as a learner and reusing it +======================================================== + +DataOps are designed to build complex pipelines that can be reused on new, unseen +data in potentially different environments from where they were created. This can +be achieved by exporting the DataOps plan as a **learner**: the learner is special +transformer that is similar to a scikit-learn estimator, but that takes as input +the **environment** that should be used to execute the operations. The environment +is a dictionary of variables rather than a single design matrix +``X`` and a target array ``y``. + +>>> import pandas as pd +>>> orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "pen", "fork"], +... "price": [1.5, None, 1.5, 2.2], +... "qty": [1, 1, 2, 4], +... } +... ) +>>> import skrub +>>> from skrub import TableVectorizer +>>> orders = skrub.var("orders", orders_df) +>>> transformed_orders = orders.skb.apply(TableVectorizer()) +>>> learner = transformed_orders.skb.make_learner() + +The learner can be fitted as it is exported by setting ``fitted=True`` when +creating it with :meth:`.skb.make_learner() `. +This will fit the learner on the data used for previews when the variables are defined +(``orders_df`` in the case above): + +>>> learner = transformed_orders.skb.make_learner(fitted=True) + +Alternatively, the learner can be fitted on a different dataset by passing +the data to the ``fit()`` method: + +>>> new_orders_df = pd.DataFrame( +... { +... "item": ["pen", "cup", "spoon"], +... "price": [1.5, 2.0, 1.0 ], +... "qty": [1, 2, 3], +... } +... ) +>>> learner.fit({"orders": new_orders_df}) +SkrubLearner(data_op=) + + +The learner can be fitted and applied to new data +using the same methods as a scikit-learn estimator, such as ``fit()``, +``fit_transform()``, and ``predict()``. + +The learner can be pickled and saved to disk, so that it can be reused later +or in a different environment: + +>>> import pickle +>>> with open("learner.pkl", "wb") as f: +... pickle.dump(learner, f) +>>> with open("learner.pkl", "rb") as f: +... loaded_learner = pickle.load(f) +>>> loaded_learner.fit({"orders": new_orders_df}) +SkrubLearner(data_op=) + +See :ref:`sphx_glr_auto_examples_data_ops_1150_use_case.py` for an example of how +to use the learner in a microservice. diff --git a/skrub/_docs/modules/data_ops/validation/hyperparameter_tuning.rst b/skrub/_docs/modules/data_ops/validation/hyperparameter_tuning.rst new file mode 100644 index 000000000..0e2110c81 --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/hyperparameter_tuning.rst @@ -0,0 +1,227 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_hyperparameter_tuning: + +Using the skrub ``choose_*`` functions to tune hyperparameters +============================================================== + +skrub provides a convenient way to declare ranges of possible values, and tune +those choices to keep the values that give the best predictions on a validation +set. + +Rather than specifying a grid of hyperparameters separately from the pipeline, +we simply insert special skrub objects in place of the value. + +We define the same set of operations as before: + +>>> from sklearn.datasets import load_diabetes +>>> from sklearn.linear_model import Ridge +>>> import skrub +>>> diabetes_df = load_diabetes(as_frame=True)["frame"] +>>> data = skrub.var("data", diabetes_df) +>>> X = data.drop(columns="target", errors="ignore").skb.mark_as_X() +>>> y = data["target"].skb.mark_as_y() +>>> pred = X.skb.apply(Ridge(), y=y) + +Now, we can +replace the hyperparameter ``alpha`` (which should be a float) with a range +created by :func:`skrub.choose_float`. skrub can use it to select the best value +for ``alpha``. + + + +>>> pred = X.skb.apply( +... Ridge(alpha=skrub.choose_float(1e-6, 10.0, log=True, name="α")), y=y +... ) + +.. warning:: + + When we do :meth:`.skb.make_learner() `, the + pipeline we obtain does not perform any hyperparameter tuning. The pipeline + we obtain by default uses default values for each of the choices. For numeric + choices it is the middle of the range (unless an explicit default has been + set when creating the choice), and for :func:`choose_from` it is the first + option we give it. We can also obtain random choices, or choices suggested by + an Optuna :class:`trial `, by passing the ``choose`` + parameter. + + To get a pipeline that runs an internal cross-validation to select the best + hyperparameters, we must use :meth:`.skb.make_grid_search() + ` or :meth:`.skb.make_randomized_search() + `. We can also use `Optuna + `_ to choose the best hyperparameters as shown + in :ref:`this example `. + + +Here are the different kinds of choices, along with their default outcome when +we are not using hyperparameter search: + +.. _choice-defaults-table: + +.. list-table:: Default choice outcomes + :header-rows: 1 + + * - Choosing function + - Description + - Default outcome + * - :func:`choose_from([10, 20]) ` + - Choose between the listed options (10 and 20). + - First outcome in the list: ``10`` + * - :func:`choose_from({"a_name": 10, "b_name": 20}) ` + - Choose between the listed options (10 and 20). Dictionary keys serve as + names for the options. + - First outcome in the dictionary: ``10`` + * - :func:`optional(10) ` + - Choose between the provided value and ``None`` (useful for optional + transformations in a pipeline, e.g., ``optional(StandardScaler())``). + - The provided ``value``: ``10`` + * - :func:`choose_bool() ` + - Choose between True and False. + - ``True`` + * - :func:`choose_float(1.0, 100.0) ` + - Sample a floating-point number in a range. + - The middle of the range: ``50.5`` + * - :func:`choose_int(1, 100) ` + - Sample an integer in a range. + - The integer closest to the middle of the range: ``50`` + * - :func:`choose_float(1.0, 100.0, log=True) ` + - Sample a float in a range on a logarithmic scale. + - The middle of the range on a log scale: ``10.0`` + * - :func:`choose_int(1, 100, log=True) ` + - Sample an integer in a range on a logarithmic scale. + - The integer closest to the middle of the range on a log scale: ``10`` + * - :func:`choose_float(1.0, 100.0, n_steps=4) ` + - Sample a float on a grid. + - The step closest to the middle of the range: ``34.0`` (steps: ``[1.0, 34.0, 67.0, 100.0]``) + * - :func:`choose_int(1, 100, n_steps=4) ` + - Sample an integer on a grid. + - The step closest to the middle of the range: ``34`` (steps: ``[1, 34, 67, 100]``) + * - :func:`choose_float(1.0, 100.0, log=True, n_steps=4) ` + - Sample a float on a logarithmically spaced grid. + - The step closest to the middle of the range on a log scale: ``4.64`` + (steps: ``[1.0, 4.64, 21.54, 100.0]``) + * - :func:`choose_int(1, 100, log=True, n_steps=4) ` + - Sample an integer on a logarithmically spaced grid. + - The step closest to the middle of the range on a log scale: ``5`` + (steps: ``[1, 5, 22, 100]``) + + +The default choices for a DataOp, those that get used when calling +:meth:`.skb.make_learner() `, can be inspected with +:meth:`.skb.describe_defaults() `: + +>>> pred.skb.describe_defaults() +{'α': 0.00316...} + +We can then find the best hyperparameters. + +>>> search = pred.skb.make_randomized_search(fitted=True) +>>> search.results_ # doctest: +SKIP + α mean_test_score +0 0.000480 0.482327 +1 0.000287 0.482327 +2 0.000014 0.482317 +3 0.000012 0.482317 +4 0.000006 0.482317 +5 0.134157 0.478651 +6 0.249613 0.472019 +7 0.612327 0.442312 +8 2.664713 0.308492 +9 3.457901 0.275007 + +A human-readable description of parameters for a pipeline can be obtained with +:meth:`SkrubLearner.describe_params`: + +>>> search.best_learner_.describe_params() # doctest: +SKIP +{'α': 0.000479...} + +It is also possible to use :meth:`ParamSearch.plot_results` to visualize the results +of the search using a parallel coordinates plot. + +This could also be done with Optuna, either by passing ``backend='optuna'`` to +:meth:`DataOp.skb.make_randomized_search`, or by using Optuna directly: + +>>> import optuna # doctest: +SKIP +>>> def objective(trial): # doctest: +SKIP +... learner = pred.skb.make_learner(choose=trial) +... cv_results = skrub.cross_validate(learner, pred.skb.get_data()) +... return cv_results['test_score'].mean() +>>> study = optuna.create_study(direction="maximize") # doctest: +SKIP +>>> study.optimize(objective, n_trials=10) # doctest: +SKIP +>>> best_learner = pred.skb.make_learner(choose=study.best_trial) # doctest: +SKIP +>>> best_learner.describe_params() # doctest: +SKIP +{'α': 0.0006391165935023005} + + +Rather than fitting a randomized or grid search to find the best combination, it +is also possible to obtain an iterator over different parameter combinations to +inspect their outputs or to have manual control over the model selection. This can +be done with :meth:`.skb.iter_learners_grid() ` or +:meth:`.skb.iter_learners_randomized() ` ( +which yield the candidate pipelines that are explored by the grid and randomized +search respectively), or with the ``choose`` parameter of +:meth:`.skb.make_learner() `. + +A full example of how to use hyperparameter search is available in +:ref:`sphx_glr_auto_examples_data_ops_1130_choices.py`, and a full example using +Optuna is in :ref:`example_optuna_choices`. + +| + + +.. _user_guide_data_ops_feature_selection: + +Feature selection with skrub :class:`SelectCols` and :class:`DropCols` +======================================================================= +It is possible to combine :class:`SelectCols` and :class:`DropCols` with +:func:`choose_from` to perform feature selection by dropping specific columns +and evaluating how this affects the downstream performance. + +Consider this example. We first define the variable: + +>>> import pandas as pd +>>> import skrub.selectors as s +>>> from sklearn.preprocessing import StandardScaler, OneHotEncoder +>>> df = pd.DataFrame({"text": ["foo", "bar", "baz"], "number": [1, 2, 3]}) +>>> X = skrub.X(df) + +Then, we use the :ref:`skrub selectors ` to encode each +column with a different transformer: + +>>> X_enc = X.skb.apply(StandardScaler(), cols=s.numeric()).skb.apply( +... OneHotEncoder(sparse_output=False), cols=s.string() +... ) +>>> X_enc + +Result: +――――――― + number text_bar text_baz text_foo +0 -1.224745 0.0 0.0 1.0 +1 0.000000 1.0 0.0 0.0 +2 1.224745 0.0 1.0 0.0 + +Now we can use :class:`skrub.DropCols` to define two possible selection strategies: +first, we drop the column ``number``, then we drop all columns that start with +``text``. We rely again on the skrub selectors for this: + +>>> from skrub import DropCols +>>> drop = DropCols(cols=skrub.choose_from( +... {"number": s.cols("number"), "text": s.glob("text_*")}) +... ) +>>> X_enc.skb.apply(drop) + +Result: +――――――― + text_bar text_baz text_foo +0 0.0 0.0 1.0 +1 1.0 0.0 0.0 +2 0.0 1.0 0.0 + +We can see the generated parameter grid with :func:`DataOps.skb.describe_param_grid()`. + +>>> X_enc.skb.apply(drop).skb.describe_param_grid() +"- choose_from({'number': …, 'text': …}): ['number', 'text']\n" + +A more advanced application of this technique is used in +`this tutorial on forecasting timeseries `_, +along with the feature engineering required to prepare the columns, and the +analysis of the results. diff --git a/skrub/_docs/modules/data_ops/validation/nested_cross_validation.rst b/skrub/_docs/modules/data_ops/validation/nested_cross_validation.rst new file mode 100644 index 000000000..b9a5d81d0 --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/nested_cross_validation.rst @@ -0,0 +1,45 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_nested_cross_validation: + +Validating hyperparameter search with nested cross-validation +============================================================= + +To avoid overfitting hyperparameters, the best combination must be evaluated on +data that has not been used to select hyperparameters. This can be done with a +single train-test split or with nested cross-validation. + +Using the same examples as the previous sections: + +>>> from sklearn.datasets import load_diabetes +>>> from sklearn.linear_model import Ridge +>>> import skrub +>>> diabetes_df = load_diabetes(as_frame=True)["frame"] +>>> data = skrub.var("data", diabetes_df) +>>> X = data.drop(columns="target", errors="ignore").skb.mark_as_X() +>>> y = data["target"].skb.mark_as_y() +>>> pred = X.skb.apply( +... Ridge(alpha=skrub.choose_float(0.01, 10.0, log=True, name="α")), y=y +... ) + +Single train-test split: + +>>> split = pred.skb.train_test_split() +>>> search = pred.skb.make_randomized_search() +>>> search.fit(split['train']) +ParamSearch(data_op=, + search=RandomizedSearchCV(estimator=None, param_distributions=None)) +>>> search.score(split['test']) # doctest: +SKIP +0.4922874902029253 + +For nested cross-validation we use :func:`skrub.cross_validate`, which accepts a +``pipeline`` parameter (as opposed to +:meth:`.skb.cross_validate() ` +which always uses the default hyperparameters): + +>>> skrub.cross_validate(pred.skb.make_randomized_search(), pred.skb.get_data()) # doctest: +SKIP + fit_time score_time test_score +0 0.891390 0.002768 0.412935 +1 0.889267 0.002773 0.519140 +2 0.928562 0.003124 0.491722 +3 0.890453 0.002732 0.428337 +4 0.889162 0.002773 0.536168 diff --git a/skrub/_docs/modules/data_ops/validation/nesting_choices_choosing_pipelines.rst b/skrub/_docs/modules/data_ops/validation/nesting_choices_choosing_pipelines.rst new file mode 100644 index 000000000..d19f7a271 --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/nesting_choices_choosing_pipelines.rst @@ -0,0 +1,110 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_nesting_choices: + +Going beyond estimator hyperparameters: nesting choices and choosing pipelines +------------------------------------------------------------------------------ + +Choices are not limited to scikit-learn hyperparameters: we can use choices +wherever we use DataOps. The choice of the estimator to use, any argument of +a DataOp's method or :func:`deferred` function call, etc. can be replaced +with choices. We can also choose between several DataOps to compare +different pipelines. + +As an example of choices outside of scikit-learn estimators, we can consider +several ways to perform an aggregation on a pandas DataFrame: + +>>> import skrub +>>> ratings = skrub.var("ratings") +>>> agg_ratings = ratings.groupby("movieId")["rating"].agg( +... skrub.choose_from(["median", "mean"], name="rating_aggregation") +... ) +>>> print(agg_ratings.skb.describe_param_grid()) +- rating_aggregation: ['median', 'mean'] + +We can also choose between several completely different pipelines by turning a +choice into a DataOp, via its ``as_data_op`` method (or by using +:func:`as_data_op` on any object). + +>>> from sklearn.preprocessing import StandardScaler +>>> from sklearn.ensemble import RandomForestRegressor +>>> from sklearn.datasets import load_diabetes +>>> from sklearn.linear_model import Ridge +>>> import skrub +>>> diabetes_df = load_diabetes(as_frame=True)["frame"] +>>> data = skrub.var("data", diabetes_df) +>>> X = data.drop(columns="target", errors="ignore").skb.mark_as_X() +>>> y = data["target"].skb.mark_as_y() + +>>> ridge_pred = X.skb.apply(skrub.optional(StandardScaler())).skb.apply( +... Ridge(alpha=skrub.choose_float(0.01, 10.0, log=True, name="α")), y=y +... ) +>>> rf_pred = X.skb.apply( +... RandomForestRegressor(n_estimators=skrub.choose_int(5, 50, name="N 🌴")), y=y +... ) +>>> pred = skrub.choose_from({"ridge": ridge_pred, "rf": rf_pred}).as_data_op() +>>> print(pred.skb.describe_param_grid()) +- choose_from({'ridge': …, 'rf': …}): 'ridge' + optional(StandardScaler()): [StandardScaler(), None] + α: choose_float(0.01, 10.0, log=True, name='α') +- choose_from({'ridge': …, 'rf': …}): 'rf' + N 🌴: choose_int(5, 50, name='N 🌴') + +Also note that as seen above, choices can be nested arbitrarily. For example it +is frequent to choose between several estimators, each of which contains choices +in its hyperparameters. + +| + + +Linking choices depending on other choices +------------------------------------------ + +Choices can depend on another choice made with :func:`choose_from`, +:func:`choose_bool` or :func:`optional` through those objects' ``.match()`` +method. + +Suppose we want to use either ridge regression, random forest or gradient +boosting, and that we want to use imputation for ridge and random forest (only), +and scaling for the ridge (only). We can start by choosing the kind of +estimators and make further choices depend on the estimator kind: + +>>> import skrub +>>> from sklearn.impute import SimpleImputer, KNNImputer +>>> from sklearn.preprocessing import StandardScaler, RobustScaler +>>> from sklearn.linear_model import Ridge +>>> from sklearn.ensemble import RandomForestRegressor, HistGradientBoostingRegressor + +>>> estimator_kind = skrub.choose_from( +... ["ridge", "random forest", "gradient boosting"], name="estimator" +... ) +>>> imputer = estimator_kind.match( +... {"gradient boosting": None}, +... default=skrub.choose_from([SimpleImputer(), KNNImputer()], name="imputer"), +... ) +>>> scaler = estimator_kind.match( +... {"ridge": skrub.choose_from([StandardScaler(), RobustScaler()], name="scaler")}, +... default=None, +... ) +>>> predictor = estimator_kind.match( +... { +... "ridge": Ridge(), +... "random forest": RandomForestRegressor(), +... "gradient boosting": HistGradientBoostingRegressor(), +... } +... ) +>>> pred = skrub.X().skb.apply(imputer).skb.apply(scaler).skb.apply(predictor) +>>> print(pred.skb.describe_param_grid()) +- estimator: 'ridge' + imputer: [SimpleImputer(), KNNImputer()] + scaler: [StandardScaler(), RobustScaler()] +- estimator: 'random forest' + imputer: [SimpleImputer(), KNNImputer()] +- estimator: 'gradient boosting' + +Note that only relevant choices are included in each subgrid. For example, when +the estimator is ``'random forest'``, the subgrid contains several options for +imputation but not for scaling. + +In addition to ``match``, choices created with :func:`choose_bool` have an +``if_else()`` method which is a convenience helper equivalent to +``match({True: ..., False: ...})``. diff --git a/skrub/_docs/modules/data_ops/validation/tuning_validating_data_ops.rst b/skrub/_docs/modules/data_ops/validation/tuning_validating_data_ops.rst new file mode 100644 index 000000000..9cbd371cb --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/tuning_validating_data_ops.rst @@ -0,0 +1,266 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_tuning_validating_dataops: + +Tuning and validating skrub DataOps plans +========================================= + +To evaluate the prediction performance of our plan, we can fit it on a training +dataset, then obtaining prediction on an unseen, test dataset. + +In scikit-learn, we pass to estimators and pipelines an ``X`` and ``y`` matrix +with one row per observation from the start. Therefore, we can split the +data into a training and test set independently from the pipeline. + +However, in many real-world scenarios, our data sources are not already +organized into ``X`` and ``y`` matrices. Some transformations may be necessary to +build them, and we want to keep those transformations inside the pipeline so +that they can be reliably re-applied to new data. + +Therefore, we must start our pipeline by creating the design matrix and targets, +then tell skrub which intermediate results in the pipeline constitute ``X`` and +``y`` respectively. + +Let us consider a toy example where we simply obtain ``X`` and +``y`` from a single table. More complex transformations would be handled in +the same way. + +>>> from sklearn.datasets import load_diabetes +>>> from sklearn.linear_model import Ridge +>>> import skrub + +>>> diabetes_df = load_diabetes(as_frame=True)["frame"] + +In the original data, all features and the target are in the same dataframe. + +>>> data = skrub.var("data", diabetes_df) + +We build our design matrix by dropping the target. Note we use +``errors="ignore"`` so that pandas does not raise an error if the column we want +to drop is already missing. Indeed, when we will need to make actual useful +predictions on unlabelled data, the "target" column will not be available. + +>>> X = data.drop(columns="target", errors="ignore").skb.mark_as_X() + +We use :meth:`.skb.mark_as_X() ` to indicate that this +intermediate result (the dataframe obtained after dropping "target") is the +``X`` design matrix. This is the dataframe that will be split into a training +and a testing part when we split our dataset or perform cross-validation. + +Similarly for ``y``, we use :meth:`.skb.mark_as_y() `: + +>>> y = data["target"].skb.mark_as_y() + +Now we can add our supervised estimator: + +>>> pred = X.skb.apply(Ridge(), y=y) +>>> pred # doctest: +SKIP + +Result: +――――――― + target +0 182.673354 +1 90.998607 +2 166.113476 +3 156.034880 +4 133.659575 +.. ... +437 180.323365 +438 135.798908 +439 139.855630 +440 182.645829 +441 83.564413 +[442 rows x 1 columns] + + +Once a pipeline is defined and the ``X`` and ``y`` nodes are identified, skrub +is able to split the dataset and perform cross-validation. + +Improving the confidence in our score through cross-validation +============================================================== + +We can increase our confidence in our score by using cross-validation instead of +a single split. The same mechanism is used but we now fit and evaluate the model +on several splits. This is done with :meth:`.skb.cross_validate() +`. + +>>> pred.skb.cross_validate() # doctest: +SKIP + fit_time score_time test_score +0 0.002816 0.001344 0.321665 +1 0.002685 0.001323 0.440485 +2 0.002468 0.001308 0.422104 +3 0.002748 0.001321 0.424661 +4 0.002649 0.001309 0.441961 + +.. _user_guide_data_ops_splitting_data: + +Splitting the data in train and test sets +========================================= + +We can use :meth:`.skb.train_test_split() ` to +perform a single train-test split. skrub first evaluates the DataOps on +which we used :meth:`.skb.mark_as_X() ` and +:meth:`.skb.mark_as_y() `: the first few steps of the +pipeline are executed until we have a value for ``X`` and for ``y``. +Then, those +dataframes are split using the provided split function (by default +scikit-learn's :func:`sklearn.model_selection.train_test_split`). + +>>> split = pred.skb.train_test_split(shuffle=False) +>>> split.keys() +dict_keys(['train', 'test', 'X_train', 'X_test', 'y_train', 'y_test']) + +``train`` and ``test`` are the full dictionaries corresponding to the training +and testing data. The corresponding ``X`` and ``y`` are the values, in those +dictionaries, for the nodes marked with +:meth:`.skb.mark_as_X() ` +and :meth:`.skb.mark_as_y() `. + +We can now fit our pipeline on the training data: + +>>> learner = pred.skb.make_learner() +>>> learner.fit(split["train"]) +SkrubLearner(data_op=) + +Only the training part of ``X`` and ``y`` are used. The subsequent steps are +evaluated, using this data, to fit the rest of the pipeline. + +And we can obtain predictions on the test part: + +>>> test_pred = learner.predict(split["test"]) +>>> test_y_true = split["y_test"] + +>>> from sklearn.metrics import r2_score + +>>> r2_score(test_y_true, test_pred) # doctest: +SKIP +0.440999149220359 + +It is possible to define a custom split function to use instead of +:func:`sklearn.model_selection.train_test_split`. + +Passing additional arguments to the splitter +============================================ + +Sometimes we want to pass additional data to the cross-validation splitter. + +For example, if there is a group structure in our data (such as sites, +hospitals, etc.) and we want the model to generalize to unseen groups, we must +ensure while evaluating it that each group goes entirely in the train set or the +test set, but is not divided among the 2. This can be done with +:class:`sklearn.model_selection.GroupKFold`, +:class:`sklearn.model_selection.LeavePGroupsOut`, etc. . The ``split`` function +of those objects accepts a ``groups`` parameter. We can compute the groups +inside of the DataOp and pass them to :meth:`DataOp.skb.mark_as_X` and they will +be passed to the splitter. + +>>> df = skrub.datasets.toy_products() +>>> df + description price seller category +0 screen 100 supermarket.com electronics +1 hammer 15 bestproducts.com tools +2 keyboard 20 supermarket.com electronics +3 usb key 9 bestproducts.com electronics +4 charger 13 bestproducts.com electronics +5 screwdriver 12 supermarket.com tools + +Suppose we want to assess generalization to new sellers. While splitting for +cross-validation we must group products by seller. We do it with +:class:`sklearn.model_selection.LeaveOneGroupOut`. + +>>> from sklearn.dummy import DummyClassifier +>>> from sklearn.model_selection import LeaveOneGroupOut + +>>> data = skrub.var("df", df) +>>> groups = data["seller"] +>>> X = data[["description", "price"]].skb.mark_as_X( +... cv=LeaveOneGroupOut(), split_kwargs={"groups": groups} +... ) +>>> y = data["category"].skb.mark_as_y() +>>> pred = X.skb.apply(DummyClassifier(), y=y) +>>> split = pred.skb.train_test_split() + +The train set only contains data from the "supermarket.com" seller. + +>>> split["X_train"] + description price +0 screen 100 +2 keyboard 20 +5 screwdriver 12 + +The test set only contains data from the "bestproducts.com" seller. + +>>> split["X_test"] + description price +1 hammer 15 +3 usb key 9 +4 charger 13 + +Passing additional arguments to the scorer +========================================== + +Sometimes we have additional information to pass to the scorer such as sample +weights, group information etc. + +We can control how scoring is performed by using +:meth:`DataOp.skb.with_scoring`. It has a ``scoring`` parameter, which can be +anything scikit-learn's :func:`~sklearn.model_selection.cross_validate` accepts +for ``scoring`` such as a metric name, callable scorer, or dict mapping metric +names to scorers (see the reference documentation of +:meth:`DataOp.skb.with_scoring` for details). + +It also accepts a ``kwargs`` argument, which are passed to the scorer when +evaluating the learner. + +Importantly, the ``scoring`` and ``kwargs`` can be DataOps, which will be +computed when scoring the learner -- so for example, sample weights can be +computed dynamically. + +Using the same toy dataset as above, suppose we want to give more weight to more +expensive products: + +>>> X = data[["description", "price"]].skb.mark_as_X(cv=2) +>>> y = data["category"].skb.mark_as_y() +>>> pred = X.skb.apply(DummyClassifier(), y=y) + +The default score is the (unweighted) accuracy: + +>>> pred.skb.cross_validate() # doctest: +SKIP + fit_time score_time test_score +0 0.003982 0.002405 0.666667 +1 0.002582 0.002169 0.666667 + +We set the scoring to provide the sample weights: + +>>> sample_weight = X["price"] +>>> pred.skb.with_scoring( +... "accuracy", kwargs={"sample_weight": sample_weight} +... ).skb.cross_validate() # doctest: +SKIP + fit_time score_time test_accuracy +0 0.003045 0.003275 0.888889 +1 0.002659 0.003026 0.647059 + +Besides passing extra arguments, :meth:`DataOp.skb.with_scoring` can also be +useful to control what should be used as the default scoring metric for our +learner, just as the ``cv`` parameter of :meth:`DataOp.skb.mark_as_X` defines +the default cross-validation splitting strategy. + +>>> split = pred.skb.train_test_split() +>>> learner = pred.skb.with_scoring('neg_log_loss').skb.make_learner() +>>> learner.fit(split['train']) +SkrubLearner(data_op= (1 scorers)> + This DataOp will be scored with: + - 'neg_log_loss' + Use .skb.cross_validate(…) or .skb.make_learner(…).score(…) to compute scores.) +>>> learner.score(split['test']) # doctest: +SKIP +-0.6365141682948128 + +Note that the score above is negative: it is the negative log loss we passed to +``with_scoring``, and not the default score (accuracy, which would be positive). + +:meth:`DataOp.skb.with_scoring` only changes how scoring is performed +(the outputs of :meth:`DataOp.skb.cross_validate`, +:meth:`DataOp.skb.make_randomized_search`, :class:`SkrubLearner.score ` etc.), +**not** the actual outputs of the learner (it does _not_ affect the outputs of +:meth:`DataOp.skb.eval`, :class:`SkrubLearner.predict `, etc.) + +This method can be called several times to add scorers that take different +kwargs. See the reference documentation for details. diff --git a/skrub/_docs/modules/data_ops/validation/tuning_with_optuna.rst b/skrub/_docs/modules/data_ops/validation/tuning_with_optuna.rst new file mode 100644 index 000000000..be52c0637 --- /dev/null +++ b/skrub/_docs/modules/data_ops/validation/tuning_with_optuna.rst @@ -0,0 +1,219 @@ +.. currentmodule:: skrub +.. _user_guide_data_ops_tuning_optuna: + +.. |make_randomized_search| replace:: :func:`~skrub.DataOp.skb.make_randomized_search` + + +Tuning DataOps with Optuna +========================== + +Optuna is a powerful hyperparameter optimization framework that +can be used to efficiently search for the best hyperparameters for machine +learning models; Optuna includes both sophisticated search algorithms and +tools to monitor and visualize the optimization process. + +There are two main ways of using Optuna with skrub DataOps: either by using +Optuna as a ``backend`` in the +|make_randomized_search| +method, or by creating an Optuna study directly and using it to pick values for +skrub choices when calling :meth:`DataOp.skb.make_learner()`. + +.. note:: + + To use Optuna with skrub, you need to have Optuna installed in your Python + environment. You can install it using pip: + + .. code-block:: bash + + pip install optuna + + +Using Optuna as a backend for randomized search +------------------------------------------------- +The easiest way to use Optuna with skrub is to use it as a backend for +|make_randomized_search|. This allows us to leverage Optuna's advanced +sampling algorithms and features while keeping same the familiar interface as +for other search methods. + +We start by defining a DataOp containing choices: + +>>> import skrub +>>> from sklearn.datasets import make_classification +>>> from sklearn.linear_model import LogisticRegression +>>> from sklearn.feature_selection import SelectKBest +>>> from sklearn.ensemble import HistGradientBoostingClassifier +>>> from sklearn.dummy import DummyClassifier + +>>> X_a, y_a = make_classification(random_state=0) +>>> X, y = skrub.X(X_a), skrub.y(y_a) +>>> selector = SelectKBest(k=skrub.choose_int(4, 20, log=True, name='k')) +>>> logistic = LogisticRegression(C=skrub.choose_float(0.1, 10.0, log=True, name="C")) +>>> hgb = HistGradientBoostingClassifier( +... learning_rate=skrub.choose_float(.01, .5, log=True, name="learning_rate"), +... random_state=0, +... ) +>>> classifier = skrub.choose_from( +... {"logistic": logistic, "hgb": hgb, "dummy": DummyClassifier()}, name="classifier" +... ) +>>> pred = X.skb.apply(selector, y=y).skb.apply(classifier, y=y) +>>> print(pred.skb.describe_param_grid()) +- k: choose_int(4, 20, log=True, name='k') + classifier: 'logistic' + C: choose_float(0.1, 10.0, log=True, name='C') +- k: choose_int(4, 20, log=True, name='k') + classifier: 'hgb' + learning_rate: choose_float(0.01, 0.5, log=True, name='learning_rate') +- k: choose_int(4, 20, log=True, name='k') + classifier: 'dummy' + + +Now, we can create a randomized search using Optuna as the backend: + +>>> search = pred.skb.make_randomized_search(fitted=True, random_state=0, backend="optuna") # doctest: +SKIP +Running optuna search for study skrub_randomized_search_c4af73b2-45fb-49ca-9f06-092d74aa8118 in storage .../tmpuor7hqjm_skrub_optuna_search_storage/optuna_storage + +It is possible to access the same parameters as with the default backend: + +>>> search.results_ # doctest: +SKIP + k C learning_rate classifier mean_test_score +0 4 NaN 0.013146 hgb 0.93 +1 4 NaN 0.040454 hgb 0.92 +2 19 NaN 0.019968 hgb 0.92 +3 4 0.645966 NaN logistic 0.92 +4 4 NaN 0.023337 hgb 0.92 +5 8 NaN 0.097994 hgb 0.90 +6 9 NaN 0.104104 hgb 0.88 +7 14 0.391899 NaN logistic 0.81 +8 20 NaN NaN dummy 0.50 +9 9 NaN NaN dummy 0.50 + +The best learner and best hyperparameters can be accessed as usual: + +>>> search.best_learner_.describe_params() # doctest: +SKIP +{'k': 4, 'learning_rate': 0.01314593370942781, 'classifier': 'hgb'} + +|make_randomized_search| +accepts ``sampler`` and ``timeout`` parameters to customize the Optuna study. +Optuna studies feature a wide range of additional parameters, which can be accessed +by using Optuna directly with skrub learners, as shown in the next section. + +A more complete example that includes more advanced usage is available in +:ref:`example_optuna_choices`. + +Setting a storage for the Optuna study +-------------------------------------- +When using Optuna as a backend for hyperparameter search, it is possible to +specify a storage option to persist the study and its results. This allows us to +resume the search later or analyze the results after the search is complete. +This can be done by providing the ``storage`` parameter to +|make_randomized_search|. + +.. code-block:: python + + search = pred.skb.make_randomized_search( + fitted=True, + random_state=0, + backend="optuna", + storage="sqlite:///optuna_study.db", # Use a SQLite database file + ) + +If no storage is provided, a temporary storage is used during optimization, then +the study is moved to an in-memory storage once the search completes so the +resulting search object is self-contained. + +Using Optuna directly +--------------------- +It is also possible to use Optuna directly with skrub DataOps. This allows for more +flexibility and control over the optimization process, as we can define custom +objectives and leverage Optuna's advanced features, such as the ask-and-tell interface, +trial pruning, and multi-objective optimization. + +In this case, rather than running the hyperparameter search through +|make_randomized_search|, +the :class:`optuna.Study ` runs the hyperparameter +search by defining an objective function that uses a skrub +learner with hyperparameters suggested by Optuna. + +:meth:`optimize ` is given an +``objective`` function. The ``objective`` must accept a +:class:`~optuna.trial.Trial` object (which is produced by the study and picks +the parameters for a given evaluation of the objective) and return the value +to maximize (or minimize). + +To use Optuna with a :class:`DataOp`, we just need to pass the Trial object +to :meth:`DataOp.skb.make_learner`. This creates a :class:`SkrubLearner` +initialized with the parameters picked by the optuna Trial. + +We can then cross-validate the:class:`SkrubLearner`, or score it however we prefer, +and return the score so that the optuna Study can take it into account. + +Here we return a single score (R²), but multi-objective +optimization is also possible. Please refer to the Optuna documentation for +more information. + +>>> import optuna # doctest: +SKIP + +>>> def objective(trial): # doctest: +SKIP +... learner = pred.skb.make_learner(choose=trial) +... cv_results = skrub.cross_validate(learner, environment=pred.skb.get_data(), cv=4) +... return cv_results["test_score"].mean() + +>>> study = optuna.create_study(direction="maximize") # doctest: +SKIP +>>> study.optimize(objective, n_trials=16) # doctest: +SKIP +>>> best_params = study.best_params # doctest: +SKIP + +Then, we can create the best learner using the best trial found by Optuna: + +>>> best_learner = pred.skb.make_learner(choose=study.best_trial) # doctest: +SKIP + +The learner can also be defined as follows: + +>>> best_learner = pred.skb.make_learner() # doctest: +SKIP +>>> best_learner.set_params(**study.best_params) # doctest: +SKIP +SkrubLearner(data_op=) + +Then, we can inspect the parameters as usual: + +>>> best_learner.describe_params() # doctest: +SKIP +{'k': 12, 'learning_rate': 0.06401143720094754, 'classifier': 'hgb'} + +You can find a more complete example in :ref:`example_optuna_choices`. + + +Parallelism +----------- + +Optuna's :meth:`optuna.study.Study.optimize` uses thread-based parallelism. When +we use :meth:`DataOp.skb.make_randomized_search` with the Optuna backend, both +threading and multiprocessing can be used. Skrub will choose based on the joblib +configuration: if joblib is configured to use processes (the default), +parallelization is done with joblib, and if joblib is configured to use the +"threading" backend, Optuna's built-in thread-based parallelism is used instead. + +When the ``timeout`` parameter is used, Optuna's built-in, thread-based +parallelization is always used regardless of the joblib configuration. + + +Using the Optuna dashboard +-------------------------- +Optuna provides a dashboard that allows us to visualize +and monitor the optimization process in real-time. This can be especially useful +for long-running hyperparameter searches. +To use the Optuna Dashboard, we need to install it first: + +.. code-block:: bash + + pip install optuna-dashboard + +We can then start the dashboard by running the following command in the terminal: + +.. code-block:: bash + + optuna-dashboard STORAGE_URL + +Where ``STORAGE_URL`` is the same storage URL used in the Optuna study. + +We can then access the dashboard in our web browser at +``http://localhost:8080`` (by default). The dashboard provides various visualizations +and tools to analyze the optimization process, such as parameter importance, +optimization history, and parallel coordinate plots. diff --git a/skrub/_docs/modules/default_wrangling/apply_to_cols.rst b/skrub/_docs/modules/default_wrangling/apply_to_cols.rst new file mode 100644 index 000000000..fc9b6e07c --- /dev/null +++ b/skrub/_docs/modules/default_wrangling/apply_to_cols.rst @@ -0,0 +1,160 @@ +.. currentmodule:: skrub + +.. |ApplyToCols| replace:: :class:`ApplyToCols` +.. |TableVectorizer| replace:: :class:`TableVectorizer` +.. |selectors| replace:: :mod:`skrub.selectors` +.. |s.string| replace:: :meth:`~skrub.selectors.string` +.. |s.numeric| replace:: :meth:`~skrub.selectors.numeric` +.. |RejectColumn| replace:: :class:`core.RejectColumn` +.. |ToDatetime| replace:: :class:`ToDatetime` +.. |SingleColumnTransformer| replace:: :class:`~skrub.core.SingleColumnTransformer` +.. |StandardScaler| replace:: :class:`~sklearn.preprocessing.StandardScaler` +.. |OneHotEncoder| replace:: :class:`~sklearn.preprocessing.OneHotEncoder` +.. |OrdinalEncoder| replace:: :class:`~sklearn.preprocessing.OrdinalEncoder` +.. |make_pipeline| replace:: :class:`~sklearn.pipeline.make_pipeline` + +.. _user_guide_multiple_columns: + +Transforming only some columns with |ApplyToCols| +=========================================================== + +Very often and for various reasons, transformers must be applied only to some of the +columns in a dataframe. For example, all numeric columns in a dataframe may need +to be scaled at the same time, while string columns should be left alone. +While the heuristics used by the :class:`TableVectorizer` are usually good enough +to apply the proper transformers to different datatypes, using it may not be an +option in all cases. + +|ApplyToCols| (optionally paired with the |selectors|) allows to transform specific +columns with a large degree of control: |ApplyToCols| maps a transformer to columns +in a dataframe, so that all columns that satisfy a certain condition are transformed, +while the others are left untouched. |ApplyToCols| and the |selectors| are similar +to scikit-learn's :class:`~sklearn.compose.ColumnTransformer`. + + +Using selectors to choose or exclude columns +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If a skrub transformer has a ``cols`` parameter to specify a column list, +that can be a selector as well. Selectors give more control over which columns +are being transformed: they are discussed at length in the +:ref:`selectors user guide`. + +|ApplyToCols| can be used to transform a subset of columns in a dataframe, while +leaving the non-selected columns unchanged. In this example, we want to apply +an |OrdinalEncoder| only on the text column, and a |StandardScaler| on the numeric +column. Columns that aren't selected are passed through unchanged, and this allows +to concatenate |ApplyToCols| transformers with |make_pipeline|. + +>>> import pandas as pd +>>> df = pd.DataFrame({"text": ["foo", "bar", "baz"], "number": [1, 2, 3]}) + +We use the |s.string| selector to choose only the text column, and |s.numeric| +to select only the numeric column: + +>>> import skrub.selectors as s +>>> from skrub import ApplyToCols +>>> from sklearn.preprocessing import OrdinalEncoder, StandardScaler +>>> +>>> numeric = ApplyToCols(StandardScaler(), cols=s.numeric()) +>>> string = ApplyToCols(OrdinalEncoder(), cols=s.string()) + +We then concatenate the two with |make_pipeline|: + +>>> from sklearn.pipeline import make_pipeline +>>> transformed = make_pipeline(numeric, string).fit_transform(df) +>>> transformed + number text +0 -1.224745 2.0 +1 0.000000 0.0 +2 1.224745 1.0 + +If |ApplyToCols| is used with a transformer that inherits from +|SingleColumnTransformer|, or one that has the ``__single_column_transformer__`` +attribute, then the transformer will be cloned and applied separately to each +column. Most skrub transformers belong to this category. + +Here we want to apply |ToDatetime| to each of the datetime columns to convert +them to datetime dtype. |ApplyToCols| automatically detects that |ToDatetime| +should be applied to each column separately: + +>>> from skrub._to_datetime import ToDatetime +>>> df = pd.DataFrame({ +... 'date_1': ['2024-01-15', '2024-02-20', '2024-03-10'], +... 'date_2': ['2023-12-01', '2024-01-05', '2024-02-28'] +... }) +>>> df_enc = ApplyToCols(ToDatetime()).fit_transform(df) +>>> df_enc + date_1 date_2 +0 2024-01-15 2023-12-01 +1 2024-02-20 2024-01-05 +2 2024-03-10 2024-02-28 +>>> df_enc.dtypes +date_1 datetime64[...] +date_2 datetime64[...] +dtype: ... + +We can also combine |ApplyToCols| with |TableVectorizer| to only vectorize columns +specific columns and avoid others, like ID columns: + +>>> from skrub import TableVectorizer +>>> df = pd.DataFrame({ +... 'id': ["c1", "c2", "c3"], +... 'city': ['Paris', 'Rome', 'Madrid'], +... 'date': ['2023-01-15', '2023-02-20', '2023-03-10'] +... }) +>>> ApplyToCols(TableVectorizer(), cols=s.all() - "id").fit_transform(df) # doctest: +SKIP +id city_Madrid city_Paris city_Rome date_year date_month date_day date_total_seconds +0 c1 0.0 1.0 0.0 2023.0 1.0 15.0 1.673741e+09 +1 c2 0.0 0.0 1.0 2023.0 2.0 20.0 1.676851e+09 +2 c3 1.0 0.0 0.0 2023.0 3.0 10.0 1.678406e+09 + +Note that the column "id" was not encoded and was instead left as-is. + +Rejecting columns that cannot be handled by a transformer +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +|ApplyToCols| can allow the underlying encoder to decide which columns it can be applied to. +For example, if we do not know in advance which columns can be transformed to datetime, +we can use |ApplyToCols| to map |ToDatetime| to all columns in a dataframe and pass +``allow_reject=True``. In that case, non-datetime columns. By default, all columns in +``cols`` must be transformed, and if one of them cannot be transformed an exception +will be raised and the transformation will fail. + +It is possible to change how rejected columns are handled through the ``allow_reject`` +parameter. +By default, no special handling is performed and rejections are considered +to be errors: + +>>> from skrub._to_datetime import ToDatetime +>>> df = pd.DataFrame(dict(birthday=["29/01/2024"], city=["London"])) +>>> df + birthday city +0 29/01/2024 London +>>> to_datetime = ApplyToCols(ToDatetime()) +>>> to_datetime.fit_transform(df) # doctest: +SKIP +Traceback (most recent call last): + ... +skrub.core.RejectColumn: Could not find a datetime format for column 'city'. +Transformer ToDatetime.fit_transform failed on column 'city'. See above for the full traceback. + +However, setting ``allow_reject=True`` gives the transformer itself some +control over which columns it should be applied to. For example, we can try to +parse all columns but allow +the transformer to reject those that, upon inspection, do not contain dates. + +>>> to_datetime = ApplyToCols(ToDatetime(), allow_reject=True) +>>> transformed = to_datetime.fit_transform(df) +>>> transformed + birthday city +0 2024-01-29 London + +>>> transformed.dtypes +birthday datetime64[...] +city ... +dtype: ... + +Advanced usage of |ApplyToCols| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +For more advanced use cases, refer to the examples section of the |ApplyToCols| +docstring, and to :ref:`this user guide section `. diff --git a/skrub/_docs/modules/default_wrangling/cleaning_dataframes.rst b/skrub/_docs/modules/default_wrangling/cleaning_dataframes.rst new file mode 100644 index 000000000..e739d7832 --- /dev/null +++ b/skrub/_docs/modules/default_wrangling/cleaning_dataframes.rst @@ -0,0 +1,121 @@ +.. |DropUninformative| replace:: :class:`~skrub.DropUninformative` +.. |Cleaner| replace:: :class:`~skrub.Cleaner` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |ToDatetime| replace:: :class:`~skrub.ToDatetime` + +.. _user_guide_cleaning_dataframes: + +|Cleaner|: sanitizing a dataframe +--------------------------------- + +Very often, the first steps in preparing a dataframe for further use involve +understanding the datatypes in the data and changing them into a more suitable format +(e.g., from string to number or datetime). + +The |Cleaner| aids with this by running common operations on each column, including +replacing "null-looking" strings (e.g., ``NULL``) with actual null values, and +parsing datetimes and numbers. + +.. admonition:: All the transformations done by the |Cleaner| + :collapsible: closed + + - Clean null strings: Replace strings typically used to represent missing values + with a null value suitable for the column under consideration. + + - |DropUninformative|: Drop the column if it is considered "uninformative." + A column is considered "uninformative" if it contains only missing values + (``drop_null_fraction``), or only a constant value (``drop_if_constant``). + By default, the |Cleaner| keeps all columns + unless they contain only missing values. Refer to :ref:`user_guide_drop_uninformative` + for more detail on this operation. + + - |ToDatetime|: Parse datetimes represented as strings and return them as + actual datetimes with the correct dtype. If ``datetime_format`` is provided, + it is forwarded to |ToDatetime|. Otherwise, the format is guessed according + to common datetime formats. + + - Convert to strings: Convert columns to strings unless they have a more informative + dtype, such as numeric, categorical, or datetime. + +If ``parse_numbers`` is set to ``True``, the ``Cleaner`` will parse +string columns that contain only numbers and convert them to ``float32``. +If ``cast_to_float32=True``, the ``Cleaner`` will also convert numeric columns +(e.g. ``float64``, ``int64``) to ``float32``. + +The |Cleaner| is a scikit-learn compatible transformer: + +>>> from skrub import Cleaner +>>> import pandas as pd +>>> df = pd.DataFrame({ +... "id": [1, 2, 3], +... "all_missing": ["", "", ""], +... "date": ["2024-05-05", "2024-05-06", "2024-05-07"], +... }) +>>> df_clean = Cleaner().fit_transform(df) +>>> df_clean + id date + 0 1 2024-05-05 + 1 2 2024-05-06 + 2 3 2024-05-07 +>>> df_clean.dtypes +id int64 +date datetime64[...] +dtype: ... + +Note that the ``"all_missing"`` column has been dropped, and that the ``"date"`` +column has been correctly parsed as a datetime column. + +Parsing numeric-looking strings with the |Cleaner| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, when the |Cleaner| encounters a string series that contains only +numeric-looking values (for example ``["1", "2", "3"]``), it leaves it +unchanged. + +The |Cleaner| can parse those values by setting ``parse_numbers=True``: + +>>> from skrub import Cleaner +>>> cleaner = Cleaner(parse_numbers=True) +>>> import pandas as pd +>>> df = pd.DataFrame({ +... "id_as_str": ["1", "2", "3"], +... "id": [1, 2, 3], +... }) +>>> df.dtypes +id_as_str ... +id int64 +dtype: ... +>>> df_cleaned = cleaner.fit_transform(df) +>>> df_cleaned.dtypes +id_as_str float32 +id int64 +dtype: ... + +Parsed string values are converted to ``float32`` (not to ``int64`` or +``float64``), to keep a consistent numeric representation that is compatible +with downstream scikit-learn transformers. + +When ``parse_numbers=False`` (default), both columns keep their original dtypes. + +Downcasting float dtypes to ``float32`` with the |Cleaner| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, floating-point columns (e.g. ``float64``) keep their original dtype. +To downcast numeric columns to ``float32``, set +``cast_to_float32=True``: + +>>> from skrub import Cleaner +>>> cleaner = Cleaner(cast_to_float32=True) +>>> import pandas as pd +>>> df = pd.DataFrame({ +... "f64": [1.0, 2.0, 3.0], +... "i64": [1, 2, 3], +... }) +>>> df.dtypes +f64 float64 +i64 int64 +dtype: ... +>>> cleaner.fit_transform(df).dtypes +f64 float32 +i64 float32 +dtype: ... diff --git a/skrub/_docs/modules/default_wrangling/table_vectorizer.rst b/skrub/_docs/modules/default_wrangling/table_vectorizer.rst new file mode 100644 index 000000000..a4b4be443 --- /dev/null +++ b/skrub/_docs/modules/default_wrangling/table_vectorizer.rst @@ -0,0 +1,152 @@ +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |Cleaner| replace:: :class:`~skrub.Cleaner` +.. |DropUninformative| replace:: :class:`~skrub.DropUninformative` +.. |DatetimeEncoder| replace:: :class:`~skrub.DatetimeEncoder` +.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` +.. |OneHotEncoder| replace:: :class:`~sklearn.preprocessing.OneHotEncoder` +.. |OrdinalEncoder| replace:: :class:`~sklearn.preprocessing.OrdinalEncoder` +.. |TextEncoder| replace:: :class:`~skrub.TextEncoder` +.. |ApplyToCols| replace:: :class:`~skrub.ApplyToCols` +.. |ToCategorical| replace:: :class:`~skrub.ToCategorical` + +.. _user_guide_table_vectorizer: + +Transforming a table into an array of numeric features: |TableVectorizer| +------------------------------------------------------------------------- + +In tabular machine learning pipelines, practitioners often convert categorical +features to numeric features using various encodings (|OneHotEncoder|, |OrdinalEncoder|, +etc.). + +The objective of the |TableVectorizer| is to take any dataframe as input, and +produce as output a feature-engineered version of the dataframe. + +Initially, the |TableVectorizer| parses the data type of each column and maps each +column to an encoder, in order to produce numeric features for machine learning +models. + +Parsing is handled internally by running a |Cleaner| on the input data. +Note that in this case numeric values are always converted to ``float32`` +(whereas the default |Cleaner| behavior is to keep the original datatype): this +is to ensure that the numeric dtype (including that of the missing values) is +consistent for the downstream methods. For most applications, ``float32`` has a +sufficient precision, and reduces the memory footprint of the resulting features. + +The same parameters used for the |Cleaner| can also be set when creating the +|TableVectorizer|: this includes parameters for |DropUninformative| +(``drop_null_fraction`` etc.), and a ``datetime_format`` parameter for the +datetime parsing step. + + +After detecting the datatypes, the |TableVectorizer| maps columns to one of +four groups depending either on the datatype, and the number of unique values +for categorical/string columns + +The default transformers used by the |TableVectorizer| for each column category +are the following: + +- **High-cardinality categorical columns**: |StringEncoder| +- **Low-cardinality categorical columns**: scikit-learn |OneHotEncoder| +- **Numeric columns**: "passthrough" (no transformation) +- **Datetime columns**: |DatetimeEncoder| + +**High cardinality** categorical columns are those with more than 40 unique values, +while all other categorical columns are considered **low cardinality**: the +threshold can be changed by setting the ``cardinality_threshold`` parameter of +|TableVectorizer|, or by changing the configuration parameter with the same name +using :func:`~skrub.set_config`. + +To change the encoder or alter default parameters, instantiate an encoder and pass +it to |TableVectorizer|. + +>>> from skrub import TableVectorizer, DatetimeEncoder, TextEncoder, SquashingScaler + +>>> datetime_enc = DatetimeEncoder(periodic_encoding="circular") +>>> text_enc = TextEncoder() +>>> num_enc = SquashingScaler() +>>> table_vec = TableVectorizer(datetime=datetime_enc, high_cardinality=text_enc, numeric=num_enc) +>>> table_vec +TableVectorizer(datetime=DatetimeEncoder(periodic_encoding='circular'), + high_cardinality=TextEncoder(), numeric=SquashingScaler()) + + +Besides the transformers provided by skrub, the |TableVectorizer| can also take +user-specified transformers that are applied to given columns. + +>>> from sklearn.preprocessing import OrdinalEncoder +>>> import pandas as pd +>>> encoder = OrdinalEncoder() +>>> df = pd.DataFrame({ +... "values": ["A", "B", "C"] +... }) + +We define the list of column-specific transformers: + +>>> specific_transformers=[(encoder, ["values"])] + +We can then encode the result: + +>>> TableVectorizer(specific_transformers=specific_transformers).fit_transform(df) + values +0 0.0 +1 1.0 +2 2.0 + +Note that the columns specified in ``specific_transformers`` are passed to the +transformer without any modification, which means that the transformer must be +able to handle the content of the column on its own. + +If you need to define complex transformers to pass to a single instance of +|TableVectorizer|, consider using the :ref:`skrub Data Ops `, +|ApplyToCols|, or the :ref:`skrub selectors ` instead, as +they are more versatile and allow a higher degree +of control over which operations are applied to which columns. + +The |TableVectorizer| is used in :ref:`example_encodings`, while the +docstring of the class provides more details on the parameters and usage, as well +as various examples. + +Numeric strings and categorical encoding +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +By default, columns that contain only numeric strings (e.g. ``["1", "2", "3"]``) +are parsed as numeric features by the |TableVectorizer|. The recommended way to +treat such values as categorical (e.g. IDs or codes) is to convert the column +to pandas' ``category`` dtype using |ToCategorical| with |ApplyToCols| before +vectorizing, rather than relying on keeping them as strings. + +Default behavior: numeric strings are parsed as a single numeric column (feature +names are not one-hot encoded): + +>>> import pandas as pd +>>> from skrub import TableVectorizer +>>> df = pd.DataFrame({"c": ["1", "2", "3"]}) +>>> tv = TableVectorizer().fit(df) +>>> list(map(str, sorted(tv.get_feature_names_out()))) +['c'] +>>> tv = TableVectorizer() +>>> tv.fit_transform(df) +c +0 1.0 +1 2.0 +2 3.0 + +With |ToCategorical| and |ApplyToCols|, the column is treated as categorical +and produces one-hot encoded feature names: + +>>> from skrub import ApplyToCols, TableVectorizer, ToCategorical +>>> from sklearn.pipeline import make_pipeline +>>> pipe = make_pipeline( +... ApplyToCols(ToCategorical(), cols=["c"]), +... TableVectorizer(), +... ) +>>> pipe.fit(df) +Pipeline(steps=[('applytocols', ...), + ('tablevectorizer', ...)]) +>>> list(map(str, sorted(pipe.named_steps["tablevectorizer"].get_feature_names_out()))) +['c_1', 'c_2', 'c_3'] +>>> pipe.fit_transform(df) + c_1 c_2 c_3 +0 1.0 0.0 0.0 +1 0.0 1.0 0.0 +2 0.0 0.0 1.0 diff --git a/skrub/_docs/modules/default_wrangling/tabular_pipeline.rst b/skrub/_docs/modules/default_wrangling/tabular_pipeline.rst new file mode 100644 index 000000000..d2b3067eb --- /dev/null +++ b/skrub/_docs/modules/default_wrangling/tabular_pipeline.rst @@ -0,0 +1,146 @@ + +.. currentmodule:: skrub + +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |tabular_pipeline| replace:: :func:`~skrub.tabular_pipeline` +.. |HistGradientBoostingRegressor| replace:: :class:`~sklearn.ensemble.HistGradientBoostingRegressor` +.. |HistGradientBoostingClassifier| replace:: :class:`~sklearn.ensemble.HistGradientBoostingClassifier` +.. |Pipeline| replace:: :class:`~sklearn.pipeline.Pipeline` +.. |SquashingScaler| replace:: :class:`~skrub.SquashingScaler` +.. |SimpleImputer| replace:: :class:`~sklearn.impute.SimpleImputer` +.. |ToCategorical| replace:: :class:`~skrub.ToCategorical` + +.. _user_guide_tabular_pipeline: + +Building robust ML baselines with |tabular_pipeline| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The |tabular_pipeline| is a function that, given a scikit-learn estimator, +returns a full scikit-learn |Pipeline| that contains a |TableVectorizer| +followed by the given estimator. +If the estimator is a linear model (e.g., ``Ridge``, ``LogisticRegression``), +|tabular_pipeline| adds a |SquashingScaler| and a |SimpleImputer| to the pipeline. + +>>> from sklearn.linear_model import LinearRegression +>>> from skrub import tabular_pipeline +>>> tabular_pipeline(LinearRegression()) +Pipeline(steps=[('tablevectorizer', + TableVectorizer(datetime=DatetimeEncoder(periodic_encoding='spline'))), + ('simpleimputer', SimpleImputer(add_indicator=True)), + ('squashingscaler', SquashingScaler(max_absolute_value=5)), + ('linearregression', LinearRegression())]) + +It is also possible to call the function with the name of the task that must be +performed (``regression``/``regressor``, ``classification``/``classifier``) to +build a pipeline that uses a +|HistGradientBoostingRegressor|/|HistGradientBoostingClassifier|. + +>>> from skrub import tabular_pipeline +>>> tabular_pipeline("regression") +Pipeline(steps=[('tablevectorizer', + TableVectorizer(...), + ('histgradientboostingregressor', + HistGradientBoostingRegressor(...))]) + +The pipeline prepared by |tabular_pipeline| is a strong first baseline for most +problems, but may not beat properly tuned ad-hoc pipelines. + +.. list-table:: Parameter values choice of :class:`TableVectorizer` when using the :func:`tabular_pipeline` function + :header-rows: 1 + :widths: 25 25 25 25 + + * - Parameter + - ``RandomForest`` models + - ``HistGradientBoosting`` models + - Linear models and others + * - Low-cardinality encoder + - :class:`~sklearn.preprocessing.OrdinalEncoder` + - Native support + - :class:`~sklearn.preprocessing.OneHotEncoder` + * - High-cardinality encoder + - :class:`StringEncoder` + - :class:`StringEncoder` + - :class:`StringEncoder` + * - Numeric preprocessor + - No processing + - No processing + - :class:`~skrub.SquashingScaler` + * - Date preprocessor + - :class:`DatetimeEncoder` + - :class:`DatetimeEncoder` + - :class:`DatetimeEncoder` with spline encoding + * - Missing value strategy + - Native support + - Native support + - :class:`~sklearn.impute.SimpleImputer` + + +The logic used by the tabular pipeline is quite simple +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The logic that is used by the |tabular_pipeline| is in fact quite simple, so +users do not lose much if they decide to write their own pipeline instead. +In practice it does only three things: + +- It chooses a |TableVectorizer| configuration from the estimator type. For + example, linear models get spline datetime features, while histogram gradient + boosting models with ``categorical_features="from_dtype"`` get + ``low_cardinality=ToCategorical()``. +- It inserts a |SimpleImputer| when the estimator cannot handle missing values. +- It inserts a |SquashingScaler| for estimators that benefit from scaling, and + skips it for tree ensembles. + +If your use case needs more control, writing the full pipeline yourself is +usually straightforward and gives you access to the exact same building blocks. +See the source of :func:`~skrub.tabular_pipeline` for the exact logic. + +Extending the pipeline with the ``.steps`` attribute +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +You can use the ``.steps`` attribute of the resulting pipeline together with +:func:`~sklearn.pipeline.make_pipeline` to build a new pipeline that has more +than just the steps in the tabular pipeline. The ``steps`` attribute of a +scikit-learn |Pipeline| is a list of ``(name, estimator)`` pairs, so we can +extract the estimators from the preprocessing steps and pass them to +``make_pipeline`` while inserting an extra transformation before the final +estimator: + +>>> from sklearn.feature_selection import SelectPercentile, f_regression +>>> from sklearn.pipeline import make_pipeline +>>> from skrub import tabular_pipeline +>>> base_pipeline = tabular_pipeline("regressor") +>>> extended_pipeline = make_pipeline( +... *[step[1] for step in base_pipeline.steps[:-1]], +... SelectPercentile(score_func=f_regression, percentile=50), +... base_pipeline.steps[-1][1], +... ) +>>> [name for name, _ in extended_pipeline.steps] +['tablevectorizer', 'selectpercentile', 'histgradientboostingregressor'] + +Here ``[step[1] for step in base_pipeline.steps[:-1]]`` extracts the +estimators from all preprocessing steps, while omitting the final estimator. +Those preprocessing estimators are unpacked into ``make_pipeline``, then a +supervised feature-selection step and the original estimator are appended. This +pattern is useful whenever you want to add something such as feature selection, +dimensionality reduction, or calibration without rewriting the whole pipeline +from scratch. + +Using a pipeline as the estimator +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The estimator passed to |tabular_pipeline| can itself be a |Pipeline|. This is +often the simplest way to add estimator-specific postprocessing while keeping +the default table preprocessing: + +>>> from sklearn.decomposition import PCA +>>> from sklearn.linear_model import Ridge +>>> from sklearn.pipeline import make_pipeline +>>> from skrub import tabular_pipeline +>>> model_pipeline = make_pipeline(PCA(n_components=20), Ridge()) +>>> full_pipeline = tabular_pipeline(model_pipeline) +>>> [name for name, _ in full_pipeline.steps] +['tablevectorizer', 'simpleimputer', 'squashingscaler', 'pipeline'] + +The user-provided estimator pipeline is appended as a single final step. This +means that ``tabular_pipeline`` can still decide which preprocessing steps to +add before your own estimator logic. diff --git a/skrub/_docs/modules/joining_tables/assembling.rst b/skrub/_docs/modules/joining_tables/assembling.rst new file mode 100644 index 000000000..51601324c --- /dev/null +++ b/skrub/_docs/modules/joining_tables/assembling.rst @@ -0,0 +1,61 @@ +.. currentmodule:: skrub + +Assembling: joining multiple tables +=================================== + +Assembling is the process of collecting and joining together tables. Good analytics +requires including as much information as possible, often from different sources. + +Skrub allows you to join tables on keys of different types (string, numerical, +datetime) with imprecise correspondence. + + + +Joining external tables for machine learning +-------------------------------------------- + +Joining is straightforward for two tables because you only need to identify +the common key. + +In addition, skrub also enable more advanced analysis: + +- :class:`Joiner`: fuzzy-joins an external table using a scikit-learn + transformer, which can be used in a scikit-learn :class:`~sklearn.pipeline.Pipeline`. + Pipelines are useful for cross-validation and hyper-parameter search, but also + for model deployment. + +- :class:`AggJoiner`: instead of performing 1:1 joins like :class:`Joiner`, + :class:`AggJoiner` + aggregates the external table first, then joins it on the main table. + Alternatively, it can aggregate the main table and then join it back onto itself. + +- :class:`AggTarget`: in some settings, one can derive powerful features from + the target ``y`` itself. AggTarget aggregates the target without risking data + leakage, then joins the result back on the main table, similar to AggJoiner. + +- :class:`MultiAggJoiner`: extension of the :class:`AggJoiner` that joins multiple + auxiliary tables onto the main table. + +Fuzzy joining tables +--------------------- + +Joining two dataframes can be hard as the corresponding keys may be different. + +:func:`~skrub.fuzzy_join` uses similarities in entries to join tables on one or more +related columns. Furthermore, it chooses the type of fuzzy matching used based +on the column type (string, numeric or datetime). It also outputs a similarity +score, to single out bad matches, so that they can be dropped or replaced. + +In sum, equivalent to :func:`pandas.merge`, the :func:`fuzzy_join` +has no need for pre-cleaning. + + +Using the :class:`InterpolationJoiner` to join tables using ML predictions +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The :class:`InterpolationJoiner` is a transformer that performs an operation similar +to that of a regular equi-join, but that can handle the presence of missing rows +in the right table (the table to be added). This is done by estimating the value +that the missing rows would have by training a machine learning model on the data +we have access to. + +This transformer is explored in more detail in :ref:`this example `. diff --git a/skrub/_docs/modules/multi_column_operations/advanced_selectors.rst b/skrub/_docs/modules/multi_column_operations/advanced_selectors.rst new file mode 100644 index 000000000..c14ddf716 --- /dev/null +++ b/skrub/_docs/modules/multi_column_operations/advanced_selectors.rst @@ -0,0 +1,126 @@ +.. currentmodule:: skrub.selectors + +.. |StandardScaler| replace:: :class:`~sklearn.preprocessing.StandardScaler` +.. |filter| replace:: :func:`filter` +.. |filter_names| replace:: :func:`filter_names` + +.. _user_guide_advanced_selectors: + +|filter| and |filter_names| to select with user-defined criteria +----------------------------------------------------------------- + +:func:`filter` and :func:`filter_names` allow +selecting columns based on arbitrary user-defined criteria. These are also used to +implement many of the other selectors provided in this module. + +:func:`filter` accepts a function which will be called on a column +(i.e., a Pandas or polars Series). This function, called a predicate, must return +``True`` if the column should be selected. + +>>> import pandas as pd +>>> import skrub.selectors as s +>>> df = pd.DataFrame( +... { +... "height_mm": [297.0, 420.0], +... "width_mm": [210.0, 297.0], +... "kind": ["A4", "A3"], +... "ID": [4, 3], +... } +... ) +>>> s.select(df, s.filter(lambda col: "A4" in col.tolist())) + kind +0 A4 +1 A3 + +:func:`filter_names` accepts a predicate that is passed the column name, +instead of the column. + +>>> s.select(df, s.filter_names(lambda name: name.endswith('mm'))) + height_mm width_mm +0 297.0 210.0 +1 420.0 297.0 + +We can pass args and kwargs that will be forwarded to the predicate, to help avoid +lambda or local functions and thus ensure the selector is picklable. + +>>> s.select(df, s.filter_names(str.endswith, 'mm')) + height_mm width_mm +0 297.0 210.0 +1 420.0 297.0 + + +Example of custom criteria in :func:`filter`: selecting columns with outliers +............................................................................. + +The :func:`filter` selector can be used to select columns based on custom +criteria. For example, we can define a function that checks if a column contains +outliers using the Interquartile Range (IQR) method, and then use this function +with :func:`filter` to select such columns. + +Specifically, we define a function that computes the IQR (Inter Quartile Range) of a column +and checks if any data points extend further than 2 IQRs of the lower and upper quartile. + +>>> def has_outliers(column): +... q1 = column.quantile(0.25) +... q3 = column.quantile(0.75) +... IQR = q3 - q1 +... lower_bound = q1 - 2 * IQR +... upper_bound = q3 + 2 * IQR +... outliers = (column < lower_bound) | (column > upper_bound) +... return any(outliers) + +>>> from skrub import SelectCols +>>> select = SelectCols(s.filter(has_outliers)) +>>> data = pd.DataFrame({ +... "A": [10, 12, 14, 15, 100], # Outlier in column A +... "B": [20, 22, 21, 19, 20], # No outliers in column B +... "C": [30, 29, 31, 32, 300] # Outlier in column C +... }) +>>> select.fit_transform(data) + A C +0 10 30 +1 12 29 +2 14 31 +3 15 32 +4 100 300 + + +Select columns with null values +-------------------------------- +Selectors :func:`has_nulls` and :ref:`user_guide_drop_uninformative` can be used to get information +about columns with null values. The selector :func:`has_nulls` selects columns that contain +null values and it accepts an optional ``proportion`` parameter that allows **selecting** columns +based on the proportion of null values they contain. + +Example: Selecting columns by null percentage with :func:`has_nulls` +..................................................................... + +The :func:`has_nulls` selector can filter columns based on their proportion of missing values. +This is useful for identifying columns that may need imputation or further investigation. + +>>> import pandas as pd +>>> import skrub.selectors as s +>>> from skrub import SelectCols + +Create a dataset with varying amounts of missing data: + +>>> df = pd.DataFrame({ +... 'patient_id': [1, 2, 3, 4, 5, 6, 7, 8], +... 'age': [25.0, 30.0, None, 45.0, 50.0, None, 60.0, 65.0], # 25% nulls +... 'blood_pressure': [120, None, None, None, 140, None, None, 150], # 62.5% nulls +... 'diagnosis': ['flu', 'cold', None, None, None, None, None, None], # 75% nulls +... 'treatment': ['med_A', 'med_B', 'med_C', 'med_D', 'med_E', 'med_F', 'med_G', 'med_H'] # no nulls +... }) + +Select columns with at least 25% missing values: + +>>> s.select(df, s.has_nulls(proportion=0.25)) + blood_pressure diagnosis +0 120.0 flu +1 NaN cold +2 NaN ... +3 NaN ... +4 140.0 ... +5 NaN ... +6 NaN ... +7 150.0 ... diff --git a/skrub/_docs/modules/multi_column_operations/drop_uninformative.rst b/skrub/_docs/modules/multi_column_operations/drop_uninformative.rst new file mode 100644 index 000000000..2dd5555d9 --- /dev/null +++ b/skrub/_docs/modules/multi_column_operations/drop_uninformative.rst @@ -0,0 +1,107 @@ +.. |DropUninformative| replace:: :class:`~skrub.DropUninformative` +.. |ApplyToCols| replace:: :class:`~skrub.ApplyToCols` +.. |Cleaner| replace:: :class:`~skrub.Cleaner` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` + +.. _user_guide_drop_uninformative: + +Removing unneeded columns with |DropUninformative| and |Cleaner| +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Data tables often include columns that do not provide meaningful information. +These columns increase computational cost and may reduce downstream performance. + +The |DropUninformative| transformer removes features that are deemed "uninformative" +using various heuristics. These heuristics include: + +- **Dropping columns with excessive missing values**: Columns are dropped if the + fraction of missing values exceeds the specified threshold. By default, the + threshold is 1, meaning only columns with all missing values are dropped. Adjust + this behavior by setting the ``drop_null_fraction`` parameter. Setting it to + ``None`` disables this check entirely. + +- **Dropping constant columns**: Columns containing only a single unique value are + removed. This behavior is controlled by the ``drop_if_constant`` parameter, which + is set to ``False`` by default. Note that missing values are treated as distinct + values, so constant columns with missing values will not be dropped. + +|DropUninformative| is used by both |TableVectorizer| and |Cleaner|, and both +accept the same parameters for dropping columns. + +Consider the following example: + +>>> import numpy as np +>>> import pandas as pd +>>> from skrub import Cleaner +>>> data = { +... 'Const int': [1, 1, 1], # Single unique value +... 'B': [2, 3, 2], # Multiple unique values +... 'Const str': ['x', 'x', 'x'], # Single unique value +... 'D': [4, 5, 6], # Multiple unique values +... 'All nan': [np.nan, np.nan, np.nan], # All missing values +... 'All empty': ['', '', ''], # All empty strings +... } +>>> df = pd.DataFrame(data) +>>> df + Const int B Const str D All nan All empty +0 1 2 x 4 NaN +1 1 3 x 5 NaN +2 1 2 x 6 NaN + +To drop constant columns and those with only single values: + +>>> cleaner = Cleaner(drop_if_constant=True) +>>> df_cleaned = cleaner.fit_transform(df) +>>> df_cleaned + B D +0 2 4 +1 3 5 +2 2 6 + +| +Dropping columns with many missing values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Columns with too many missing values may not provide useful information for +downstream models. The ``drop_null_fraction`` parameter allows dropping such +columns when the proportion of missing values exceeds a specified threshold. + +Consider the following dataset: + +>>> import pandas as pd +>>> from skrub import DropUninformative, ApplyToCols + +>>> df = pd.DataFrame({ +... 'patient_id': [1, 2, 3, 4, 5, 6, 7, 8], +... 'age': [25.0, 30.0, None, 45.0, 50.0, None, 60.0, 65.0], +... 'blood_pressure': [120, None, None, None, 140, None, None, 150], +... 'diagnosis': ['flu', 'cold', None, None, None, None, None, None], +... 'treatment': ['med_A', 'med_B', 'med_C', 'med_D', 'med_E', 'med_F', 'med_G', 'med_H'] +... }) + +Applying |DropUninformative| only to a subset of columns +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We can apply the |DropUninformative| transformer to specific columns using +|ApplyToCols| and the skrub selectors. In this case, we want to drop columns with +more than 50% missing values, but only if they have ``string`` type: + +>>> import skrub.selectors as s +>>> cleaner = ApplyToCols(DropUninformative(drop_null_fraction=0.5), cols=s.string()) +>>> cleaned_df = cleaner.fit_transform(df) +>>> cleaned_df + patient_id age blood_pressure treatment +0 1 25.0 120.0 med_A +1 2 30.0 NaN med_B +2 3 NaN NaN med_C +3 4 45.0 NaN med_D +4 5 50.0 140.0 med_E +5 6 NaN NaN med_F +6 7 60.0 NaN med_G +7 8 65.0 150.0 med_H + + +You can apply the |DropUninformative| transformer to specific columns using +For more advanced filtering operations, refer to the User Guide on +:ref:`user_guide_selectors` and the |ApplyToCols| documentation for details +on applying transformers to specific columns. diff --git a/skrub/_docs/modules/multi_column_operations/selectors.rst b/skrub/_docs/modules/multi_column_operations/selectors.rst new file mode 100644 index 000000000..1e5004d8d --- /dev/null +++ b/skrub/_docs/modules/multi_column_operations/selectors.rst @@ -0,0 +1,237 @@ +.. _user_guide_selectors: + +Skrub Selectors, for selecting columns in a dataframe +===================================================== + +In skrub, a selector represents a column selection rule, such as "all columns +that have numeric data types, except the column ``'User ID'``". + +Selectors have two main benefits: + +- Expressing complex selection rules in a simple and concise way by combining + selectors with operators. A range of useful selectors is provided by this module. +- Delayed selection: passing a selection rule which will evaluated later on a dataframe + that is not yet available. For example, without selectors, it is not possible to + instantiate a :class:`~skrub.SelectCols` that selects all columns except those with + the suffix 'ID' if the data on which it will be fitted is not yet available. + +Introduction to selectors +------------------------------ + +Here is an example dataframe. Note that selectors support both Pandas and Polars +dataframes:: + + >>> import pandas as pd + >>> df = pd.DataFrame( + ... { + ... "height_mm": [297.0, 420.0], + ... "width_mm": [210.0, 297.0], + ... "kind": ["A4", "A3"], + ... "ID": [4, 3], + ... } + ... ) + +:func:`~skrub.selectors.cols` is a simple kind of selector which selects a fixed list of +column names:: + + >>> from skrub import selectors as s + >>> mm_cols = s.cols('height_mm', 'width_mm') + >>> mm_cols + cols('height_mm', 'width_mm') + +Using selectors: + +* **select function**: the above selector can be passed to the :func:`~skrub.selectors.select` function:: + + >>> s.select(df, mm_cols) + height_mm width_mm + 0 297.0 210.0 + 1 420.0 297.0 + +* **transformers**: various transformers in skrub use selectors to select and transform columns + in a scikit-learn pipeline: :class:`~skrub.ApplyToCols`, + :class:`~skrub.DropCols`, :class:`~skrub.SelectCols`, as + :ref:`detailed below `. + +* **DataOps** selectors can be passed to + :ref:`skrub DataOps ` when applying an + estimator with the :func:`skrub.DataOp.skb.apply` function:: + + >>> import skrub + >>> from sklearn.preprocessing import StandardScaler + >>> skrub.X(df).skb.apply(StandardScaler(), cols=mm_cols) + + Result: + ――――――― + kind ID height_mm width_mm + 0 A4 4 -1.0 -1.0 + 1 A3 3 1.0 1.0 + +Type of selectors +----------------- + +:func:`~skrub.selectors.all` is another simple selector, especially useful for default +arguments since it keeps all columns:: + + >>> from skrub import SelectCols + >>> SelectCols(cols=s.all()).fit_transform(df) + height_mm width_mm kind ID + 0 297.0 210.0 A4 4 + 1 420.0 297.0 A3 3 + +Selectors can be combined with operators, for example if we wanted all columns +except the "mm" columns above:: + + >>> SelectCols(s.all() - s.cols("height_mm", "width_mm")).fit_transform(df) + kind ID + 0 A4 4 + 1 A3 3 + +This module provides several kinds of selectors, which allow to select columns by +name, data type, contents, or according to arbitrary user-provided rules:: + + >>> SelectCols(s.numeric()).fit_transform(df) + height_mm width_mm ID + 0 297.0 210.0 4 + 1 420.0 297.0 3 + + >>> SelectCols(s.glob('*_mm')).fit_transform(df) + height_mm width_mm + 0 297.0 210.0 + 1 420.0 297.0 + +.. seealso:: + + * :ref:`selectors_details` explains more the various selectors + + * :ref:`selectors_ref` gives the exhaustive list of selectors + + * :ref:`user_guide_advanced_selectors` + +Selectors can be combined with the set operators +------------------------------------------------ + +The available operators are ``|``, ``&``, ``-``, ``^`` with the meaning of usual +python sets, and ``~`` to invert a selection: + +>>> SelectCols(s.glob('*_mm')).fit_transform(df) +height_mm width_mm +0 297.0 210.0 +1 420.0 297.0 + +>>> SelectCols(~s.glob('*_mm')).fit_transform(df) +kind ID +0 A4 4 +1 A3 3 + +>>> SelectCols(s.glob('*_mm') | s.cols('ID')).fit_transform(df) +height_mm width_mm ID +0 297.0 210.0 4 +1 420.0 297.0 3 + +>>> SelectCols(s.glob('*_mm') & s.glob('height_*')).fit_transform(df) +height_mm +0 297.0 +1 420.0 + +>>> SelectCols(s.glob('*_mm') ^ s.string()).fit_transform(df) +height_mm width_mm kind +0 297.0 210.0 A4 +1 420.0 297.0 A3 + +The operators respect the usual short-circuit rules. For example, the +following selector won't compute the cardinality of non-categorical columns: + +>>> s.categorical() & s.cardinality_below(10) +(categorical() & cardinality_below(10)) + +.. _user_guide_selectors_expand: +Using selectors with dataframe libraries +---------------------------------------- + +All selectors have the :meth:`expand` method, which allows dataframe manipulation +outside of a skrub workflow: applying it to any dataframe will return the list +of column names from the dataframe that the selector would keep. This allows selectors +to be applied on a variety of standard dataframe libraries, and can be particularly +useful on complicated combinations of selectors. For instance, the following filter +only keeps columns that do not end in ``_mm``: + +>>> some_selector = ~s.glob("*_mm") +>>> import pandas as pd +>>> df = pd.DataFrame( +... { +... "height_mm": [210.0, 297.0], +... "width_mm": [188.5, 210.0], +... "kind": ["A5", "A4"], +... "ID": [5, 4], +... } +... ) +>>> some_selector.expand(df) +['kind', 'ID'] + + +The :meth:`expand_index` method also exists: rather than returning a list of column names, it returns the corresponding indices from the input dataframe's column list: + +>>> some_selector.expand_index(df) +[2, 3] + +.. _selectors_and_transformer: + +Using selectors with other skrub transformers +------------------------------------------------- + +Skrub selectors are designed to be used in conjunction with :class:`~skrub.ApplyToCols`, +:class:`skrub.SelectCols`, and :class:`skrub.DropCols`, as well as +:func:`~skrub.DataOp.skb.apply` to improve their versatility in how they modify +columns. + +For example, it is possible to drop columns that have more unique values than a +certain amount by combining :func:`~skrub.selectors.cardinality_below` with +:class:`skrub.DropCols`. +To do so, a selector targeting columns that have more than 3 unique values +is defined, and its inverse is used as a parameter for :class:`skrub.DropCols`: + +>>> df = pd.DataFrame({ +... "not a lot": [1, 1, 1, 2, 2], +... "too_many": [1, 2, 3, 4, 5]}) + +>>> from skrub import DropCols +>>> DropCols(cols=~s.cardinality_below(3)).fit_transform(df) + not a lot +0 1 +1 1 +2 1 +3 2 +4 2 + +Selectors can be used in conjunction with :class:`~skrub.ApplyToCols` to transform columns +based on specific requirements. + +Consider the following example: + +>>> import pandas as pd +>>> data = { +... "subject": ["Math", "English", "History", "Science", "Art"], +... "grade": [5, 4, 3, 4, 3] +... } +>>> df = pd.DataFrame(data) +>>> df + subject grade +0 Math 5 +1 English 4 +2 History 3 +3 Science 4 +4 Art 3 + +We might want to apply the :class:`~sklearn.preprocessing.StandardScaler` only to the numeric column. We can +do this like this: + +>>> from skrub import ApplyToCols +>>> from sklearn.preprocessing import StandardScaler +>>> ApplyToCols(StandardScaler(), cols=s.numeric()).fit_transform(df) + subject grade +0 Math 1.603567 +1 English 0.267261 +2 History -1.069045 +3 Science 0.267261 +4 Art -1.069045 diff --git a/skrub/_docs/modules/multi_column_operations/type_of_selectors.rst b/skrub/_docs/modules/multi_column_operations/type_of_selectors.rst new file mode 100644 index 000000000..87d598187 --- /dev/null +++ b/skrub/_docs/modules/multi_column_operations/type_of_selectors.rst @@ -0,0 +1,98 @@ +.. _selectors_details: + +Selecting based on dtype or data properties +------------------------------------------- + +Selectors can filter columns based on different conditions. + +:func:`~skrub.selectors.all` is a simple selector, especially useful for default +arguments since it keeps all columns: + +>>> import pandas as pd +>>> from skrub import SelectCols +>>> import skrub.selectors as s +>>> df = pd.DataFrame( +... { +... "height_mm": [297.0, 420.0], +... "width_mm": [210.0, 297.0], +... "kind": ["A4", "A3"], +... "ID": [4, 3], +... } +... ) +>>> SelectCols(cols=s.all()).fit_transform(df) + height_mm width_mm kind ID +0 297.0 210.0 A4 4 +1 420.0 297.0 A3 3 + +Selectors can be combined with operators, for example if we wanted all columns +except the "mm" columns above: + +>>> SelectCols(s.all() - s.cols("height_mm", "width_mm")).fit_transform(df) + kind ID +0 A4 4 +1 A3 3 + +This module provides several kinds of selectors, which allow to select columns by +name, data type, contents, or according to arbitrary user-provided rules. + +>>> SelectCols(s.numeric()).fit_transform(df) + height_mm width_mm ID +0 297.0 210.0 4 +1 420.0 297.0 3 + +Selectors can be inverted with ``~``, or :func:`~skrub.selectors.inv`: + +>>> SelectCols(~s.numeric()).fit_transform(df) + kind +0 A4 +1 A3 + +>>> SelectCols(s.inv(s.numeric())).fit_transform(df) + kind +0 A4 +1 A3 + + +Selectors can work on the column names. For example, to select the columns that +end with ``_mm`` we can do: + +>>> SelectCols(s.glob('*_mm')).fit_transform(df) + height_mm width_mm +0 297.0 210.0 +1 420.0 297.0 + +| + +Categories of selectors +----------------------- + +The selectors in this module can be categorized based on what aspect of the columns +they examine: + +Selectors based on column data types +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- :func:`~skrub.selectors.numeric`: Select columns with numeric data types (float and integer) +- :func:`~skrub.selectors.integer`: Select columns with integer data types +- :func:`~skrub.selectors.float`: Select columns with floating-point data types +- :func:`~skrub.selectors.has_dtype`: Select columns whose dtype exactly matches one of the provided dtypes +- :func:`~skrub.selectors.any_date`: Select columns with date or datetime data types +- :func:`~skrub.selectors.categorical`: Select columns with categorical data types +- :func:`~skrub.selectors.string`: Select columns with string data types +- :func:`~skrub.selectors.boolean`: Select columns with boolean data types + +Selectors based on column content and properties +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- :func:`~skrub.selectors.cardinality_below`: Select columns with fewer unique + values than a threshold +- :func:`~skrub.selectors.has_nulls`: Select columns that contain at least one + null value + +Selectors based on column names +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +- :func:`~skrub.selectors.cols`: Select columns explicitly by name +- :func:`~skrub.selectors.glob`: Select columns by name using Unix shell-style + pattern matching +- :func:`~skrub.selectors.regex`: Select columns by name using regular expressions diff --git a/skrub/_docs/modules/tablereport/exploring_dataframes_interactively.rst b/skrub/_docs/modules/tablereport/exploring_dataframes_interactively.rst new file mode 100644 index 000000000..96af04aed --- /dev/null +++ b/skrub/_docs/modules/tablereport/exploring_dataframes_interactively.rst @@ -0,0 +1,62 @@ +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |set_config| replace:: :func:`~skrub.set_config` +.. |column_associations| replace:: :func:`~skrub.column_associations` + +.. _user_guide_table_report_start: + +Exploring dataframes interactively with the |TableReport| +========================================================= + +The |TableReport| gives a high-level overview of a Dataframe or Series, suitable for +quick exploratory analysis. The report shows the first +and last 5 rows of the dataframe (decided by the ``n_rows`` parameter), as well +as additional information in other tabs. + +- The **Stats** tab reports high-level statistics for each column. +- The **Distribution** tab collects summary plots for each column (max 30 by default). +- The **Associations** tab shows `Cramer V `_ + and `Pearson correlation `_ + between columns. +- Built-in filters allow selection of columns by dtype and other conditions. + +The |TableReport| of a table can be generated as follows: + +>>> from skrub import TableReport +>>> import pandas as pd +>>> df = pd.DataFrame({ +... "id": [1, 2, 3], +... "value": [10, 20, 30], +... }) +>>> TableReport(df) # from a notebook cell + + +The command ``TableReport(df).open()`` opens the report in a browser window. + +It is also possible to export the |TableReport| in JSON or Markdown format with +:meth:`~skrub.TableReport.json()` :meth:`~skrub.TableReport.markdown()` respectively. + +The generated JSON includes the plots in SVG format, which can be +quite verbose: plots can be disabled by setting ``plot_distributions=False`` +when generating the report. +Similarly, the Markdown string includes information about all columns in the dataframe, +so it can be quite lengthy for dataframes that include many columns. + +.. warning:: + + The Markdown output can be fed to AI agents to obtain insight in the data, + but it is **not** sanitized by the |TableReport|. Therefore, it should not be + used with untrusted data or for dataframes that are too large, as it could lead + to security risks or performance issues. + +A demo of the |TableReport| +~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Pre-computed examples of the |TableReport| are available +`here `_, and you can +try it out on your data `here `_. + +In the **Distributions** tab, it is possible to select columns by clicking on the +checkmark icon: the name of the column is added to the bar on top, so that it may +be copied in a script. + +The TableReport can be used in a notebook cell, or it can be opened in a browser +window using ``TableReport(df).open()``. diff --git a/skrub/_docs/multi_column_operations.rst b/skrub/_docs/multi_column_operations.rst new file mode 100644 index 000000000..fc5e5e7a0 --- /dev/null +++ b/skrub/_docs/multi_column_operations.rst @@ -0,0 +1,17 @@ +.. _user_guide_multi_column_index: + +Multi-column operations +======================== + +Skrub provides various tools to extend the use of single column transformers to +multiple columns. + +.. include:: includes/big_toc_css.rst + +.. toctree:: + :maxdepth: 3 + + modules/multi_column_operations/drop_uninformative + modules/multi_column_operations/selectors + modules/multi_column_operations/type_of_selectors + modules/multi_column_operations/advanced_selectors diff --git a/skrub/_docs/sg_execution_times.rst b/skrub/_docs/sg_execution_times.rst new file mode 100644 index 000000000..33493fba4 --- /dev/null +++ b/skrub/_docs/sg_execution_times.rst @@ -0,0 +1,88 @@ + +:orphan: + +.. _sphx_glr_sg_execution_times: + + +Computation times +================= +**11:40.190** total execution time for 18 files **from all galleries**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_auto_examples_03_joining_0070_join_aggregation.py` (``../examples/03_joining/0070_join_aggregation.py``) + - 02:24.175 + - 528.7 + * - :ref:`sphx_glr_auto_examples_01_encoding_0020_text_with_string_encoders.py` (``../examples/01_encoding/0020_text_with_string_encoders.py``) + - 02:19.025 + - 1152.5 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1120_multiple_tables.py` (``../examples/02_data_ops/1120_multiple_tables.py``) + - 01:23.995 + - 407.4 + * - :ref:`sphx_glr_auto_examples_01_encoding_0010_encodings.py` (``../examples/01_encoding/0010_encodings.py``) + - 00:57.927 + - 368.5 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1131_optuna_choices.py` (``../examples/02_data_ops/1131_optuna_choices.py``) + - 00:34.782 + - 735.6 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1130_choices.py` (``../examples/02_data_ops/1130_choices.py``) + - 00:34.396 + - 352.5 + * - :ref:`sphx_glr_auto_examples_03_joining_0040_fuzzy_joining.py` (``../examples/03_joining/0040_fuzzy_joining.py``) + - 00:31.762 + - 349.4 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1160_pytorch.py` (``../examples/02_data_ops/1160_pytorch.py``) + - 00:27.060 + - 349.9 + * - :ref:`sphx_glr_auto_examples_03_joining_0080_interpolation_join.py` (``../examples/03_joining/0080_interpolation_join.py``) + - 00:24.162 + - 1904.7 + * - :ref:`sphx_glr_auto_examples_03_joining_0060_multiple_key_join.py` (``../examples/03_joining/0060_multiple_key_join.py``) + - 00:23.482 + - 1524.7 + * - :ref:`sphx_glr_auto_tutorials_0000_getting_started.py` (``tutorials/0000_getting_started.py``) + - 00:15.634 + - 360.2 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1140_subsampling.py` (``../examples/02_data_ops/1140_subsampling.py``) + - 00:15.468 + - 355.8 + * - :ref:`sphx_glr_auto_examples_02_data_ops_0100_squashing_scaler.py` (``../examples/02_data_ops/0100_squashing_scaler.py``) + - 00:14.470 + - 349.4 + * - :ref:`sphx_glr_auto_examples_01_encoding_0030_datetime_encoder.py` (``../examples/01_encoding/0030_datetime_encoder.py``) + - 00:14.035 + - 356.4 + * - :ref:`sphx_glr_auto_tutorials_1110_data_ops_intro.py` (``tutorials/1110_data_ops_intro.py``) + - 00:13.458 + - 351.8 + * - :ref:`sphx_glr_auto_examples_0010_apply_to_cols.py` (``../examples/0010_apply_to_cols.py``) + - 00:12.338 + - 415.9 + * - :ref:`sphx_glr_auto_examples_02_data_ops_1150_use_case.py` (``../examples/02_data_ops/1150_use_case.py``) + - 00:07.995 + - 457.2 + * - :ref:`sphx_glr_auto_examples_0050_deduplication.py` (``../examples/0050_deduplication.py``) + - 00:06.024 + - 349.9 diff --git a/skrub/_docs/tutorial_example.rst b/skrub/_docs/tutorial_example.rst new file mode 100644 index 000000000..40189a867 --- /dev/null +++ b/skrub/_docs/tutorial_example.rst @@ -0,0 +1,239 @@ +.. _tutorial_write_example: + +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` + +How to write an example for the gallery +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This tutorial explains to new contributors how to format their examples so that +they are properly rendered in the skrub documentation gallery. + +While examples are written in plain Python code, there are some quirks to be aware of +when writing them, due to the way Sphinx and the sphinx-gallery extension work. +This tutorial explains these quirks and how to work around them. + +Location of the examples +----------------------- + +Once you decide on the subject of your example, start writing the code as a Python +script. Place the script in the ``examples/`` folder of the repository. The example +should be self-contained and runnable as a standalone script. The documentation is +built by executing the code and generating additional content from it. + +The name of the file should start with a number, followed by an underscore, +and then a short description of the example. The number is used to order the examples +in the documentation. For instance, if your example is about using the +|TableVectorizer| class, you might want to name the file ``01_table_vectorizer.py``. + +Note that the ``examples/`` folder is covered by ``pre-commit`` hooks, which run +various checks on your code when you try to commit. These checks may block you from +pushing. + +Dealing with typos in the example +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If your code includes any kind of intentional typo, for example if you are trying +to correct names by replacing a string with a typo with the new one, the +``codespell`` hook will block your commit. To bypass this, update ``pyproject.toml`` +by adding the typo to the ``ignore-word-list`` entry in the ``tool.codespell`` +section. After this, commit the updated ``pyproject.toml`` file using +``git commit --no-verify`` to bypass local checks so that following commits will +ignore the typos. +Note that without updating ``pyproject.toml``, the CI will still reject commits +with typos, as it runs the same hooks that are run locally. + +Writing the example +----------------------- +Your python script should start with a docstring that briefly explains what the example +is about. This docstring can contain multiple paragraphs and will be rendered +as an RST file in the documentation, so you can use RST syntax +in it. + +Importantly, the first line of the docstring should be the title of the example, +not an RST directive (such as ``.. replace::`` or ``.. note::``). Sphinx +adds a reference to the example at the top of the page using the file name as the +title. Adding a directive at the top of the docstring would prevent proper HTML +rendering. + +This is an example of what the beginning of your example may look like: + +.. code-block:: python + + """ + Title of the example + ==================== + + This is a brief description of the example. It can contain multiple paragraphs, + and it can use RST syntax. + + .. note:: + + You can use RST directives in the docstring, such as ``.. note::``, + ``.. warning::``, ``.. seealso::``, etc. + + After the definition of the title, you may also add directives such as + ``.. replace::``, and they will be rendered properly. For example, you can add: + + .. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` + + + """ + +Then, you can start writing the code for the example. The content of your Python script +should be a sequence of code cells, each delimited by a line starting with ``# %%``. +These code cells may contain comments, which will be rendered as rst in the final +documentation. + +After the docstring, write the code for your example as a sequence of code cells, +each delimited by a line starting with ``# %%``. Comments in these cells will be +rendered as RST in the final documentation. + +.. code-block:: python + + # %% + # This is a comment that will be rendered as markdown in the final documentation. + # You can use multiple lines for comments, and you can use RST syntax in them. + + import pandas as pd + from skrub import TableVectorizer + + # %% + # This is another code cell. You can write any python code here. + df = pd.DataFrame({ + "A": [1, 2, 3], + "B": ["a", "b", "c"] + }) + tv = TableVectorizer() + X = tv.fit_transform(df) + print(X) + +Running the example +------------------- + +Once you have written the code for the example (or while writing it), you can run +it to see how it looks in the final documentation. Depending on your setup, you +may need to install some dependencies. Refer to your IDE's documentation for more +information on running interactive Python scripts. For example, VSCode documentation +is available `here `_. + +Once you are happy with your example, you can submit a pull request to the repository, +following the instructions in the :ref:`contributing guide `. + +Adding cross-references +----------------------- + +Adding cross-references to the documentation helps users find more information +about the concepts and functions used in your example. This step is optional, and +you may ask the maintainers for help on which cross-references to add. Good +cross-references include relevant user guide sections, the documentation of the +objects used in the example (like the |TableVectorizer|), or other examples. + +You can add cross-references in the docstring and comments of your example in several ways: + +- You can add references to the objects in the skrub API using the ``:class:`~skrub.ClassName``` + or ``:func:`~skrub.function_name``` directives. +- If your example uses the same objects multiple times, you can define a replacement at the top + of the docstring using the ``.. replace::`` directive, and then use the replacement + instead of the full directive. +- You can also add references to other sections of the documentation using the + ``:ref:`label``` directive, where ``label`` is the label of the section you want to reference. + + +For example, if your example uses the |TableVectorizer| class multiple times, define +a replacement at the top of the docstring. You may also want to add a reference +to the user guide section about the |TableVectorizer| class. This can be done as follows: + +.. code-block:: python + + """ + Title of the example + ==================== + + .. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` + + This example demonstrates how to use the |TableVectorizer| class to vectorize a dataframe. + + See the :ref:`user_guide_building_pipeline_index` guide for more information about the |TableVectorizer| class. + """ + + # %% + import pandas as pd + from skrub import TableVectorizer + + df = pd.DataFrame({ + "A": [1, 2, 3], + "B": ["a", "b", "c"] + }) + tv = TableVectorizer() + X = tv.fit_transform(df) + print(X) + +You may find more information on the cross-references in the +`official Sphinx documentation `_. + + +Generating the new documentation +------------------------------- +Once you have written your example and added any necessary cross-references, you can +generate the new documentation to see how it looks. This can be done in two ways: + +- You can run the commands ``make html`` or ``make html-noplot`` in the ``doc/`` + folder of the repository to generate the HTML documentation for the entire project. +- Alternatively, you can use ``pixi run -e doc build-doc`` or ``pixi run -e doc build-doc-quick`` + from the root folder to generate the documentation. The advantage of using ``pixi`` is that + it automatically sets up a virtual environment with the necessary dependencies, so you + don't need to worry about installing them manually. + +The ``make html`` and ``pixi run -e doc build-doc`` commands generate complete +documentation by executing all example code. The ``-noplot`` (or ``-quick``) +versions skip code execution, making documentation generation much faster. Use +these faster versions to check formatting when you've already tested your example +code locally. + +The CI pipeline will always run the full documentation build, so you can safely +use ``make html-noplot`` or ``pixi run -e doc build-doc-quick`` for local testing. + + +After generating the documentation, open the ``index.html`` file in the ``doc/_build/html/`` +folder with a web browser to review the results. Check that: + +- Section titles are properly formatted. +- Any formatting in docstrings or comments is rendered as intended. For example, + Sphinx uses spaces to delimit lists and code blocks, so if you have them in the + example, make sure that they render correctly. +- Cross-references are working. You can check the logs of the Sphinx + generation to see if there are any broken references. + + +Linking your work to examples already in the documentation +---------------------------------------------------------- +After generating the documentation, you may want to add references to your example +in other relevant parts of the documentation. This helps users find your example +when reading about related topics. + + +This step is done after generating the documentation because you need the final +reference name, which is created dynamically from your file name. For example, +if your file is named ``99_my_example.py``: + +1. The generated files will be in ``doc/auto_examples`` +2. A reference file will be created at ``doc/auto_examples/99_my_example.rst`` +3. The reference label will be ``.. _sphx_glr_auto_examples_99_my_example.py`` + +To link to your example from other documentation pages, use: + +.. code-block:: rst + + :ref:`sphx_glr_auto_examples_99_my_example.py` + + + +Merging your example +----------------------- +Finally, if everything looks good, commit your changes and submit a pull request +to the repository. For more information, see the :ref:`contributing guide `. + + +Your PR will be reviewed by the maintainers, who may suggest changes or improvements. +Once approved, it will be merged into the main branch, and your example will +become part of the official documentation. Thank you! diff --git a/skrub/_docs/tutorials/0000_getting_started.py b/skrub/_docs/tutorials/0000_getting_started.py new file mode 100644 index 000000000..4e7cca8f8 --- /dev/null +++ b/skrub/_docs/tutorials/0000_getting_started.py @@ -0,0 +1,221 @@ +""" +Getting Started with skrub +========================== + +This guide showcases some of the features of skrub. +Much of skrub revolves around simplifying many of the tasks that are involved +in pre-processing raw data into a format that shallow or classic machine-learning +models can understand, that is, numerical data. + +Skrub achieves this by vectorizing, assembling, and encoding tabular data through +the features we present in this example and the following ones. + +.. |TableReport| replace:: :class:`~skrub.TableReport` +.. |Cleaner| replace:: :class:`~skrub.Cleaner` +.. |set_config| replace:: :func:`~skrub.set_config` +.. |tabular_pipeline| replace:: :func:`~skrub.tabular_pipeline` +.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` +.. |Joiner| replace:: :class:`~skrub.Joiner` +.. |SquashingScaler| replace:: :class:`~skrub.SquashingScaler` +.. |DatetimeEncoder| replace:: :class:`~skrub.DatetimeEncoder` +.. |ApplyToCols| replace:: :class:`~skrub.ApplyToCols` +.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` +.. |TextEncoder| replace:: :class:`~skrub.TextEncoder` +""" + +# %% +# Preliminary exploration with the |TableReport| +# ---------------------------------------------- +# We start by loading the "employee salaries". Skrub dataset fetching functions +# return a Bunch object, which contains the paths to the data files. +# We can load the data into a dataframe using pandas. + +import pandas as pd + +from skrub.datasets import fetch_employee_salaries + +file_path = fetch_employee_salaries().path +employees_df = pd.read_csv(file_path) + +# %% +# The target variable is the current annual salary. We pop it from the dataframe +# to keep only the features in ``employees_df``. +salaries = employees_df.pop("current_annual_salary") + +# %% +# Typically, the first step with new data is exploration and parsing. +# To quickly get an overview of a dataframe's contents, use the |TableReport|. + +# %% +from skrub import TableReport + +TableReport(employees_df) + +# %% +# You can use the interactive display above to explore the dataset visually. +# +# .. admonition:: Additional examples +# :collapsible: closed +# +# You can see a few more `example reports`_ online. We also +# provide an experimental online demo_ that allows you to select a CSV or +# parquet file and generate a report directly in your web browser, without +# installing anything. +# +# .. _example reports: https://skrub-data.org/skrub-reports/examples/ +# .. _demo: https://skrub-data.org/skrub-reports/ +# +# From the report above, we see that there are columns with date and time stored +# as ``object`` dtype (cf. "Stats" tab of the report). +# Datatypes not being parsed correctly is a scenario that occurs commonly after +# reading a table. We can use the |Cleaner| to address this. +# In the next section, we show that this transformer does additional cleaning. + +# %% +# Sanitizing data with the |Cleaner| +# ---------------------------------- +# Here, we use the |Cleaner|, a transformer that sanitizes the +# dataframe by parsing nulls and dates, and by dropping "uninformative" columns +# (e.g., columns with too many nulls or that are constant). +# + +from skrub import Cleaner + +employees_df = Cleaner().fit_transform(employees_df) +TableReport(employees_df) + +# %% +# We can see from the "Stats" tab that now the column ``date_first_hired`` has been +# parsed correctly as a Datetime. + +# %% +# Easily building a strong baseline for tabular machine learning +# -------------------------------------------------------------- +# +# The goal of skrub is to ease tabular data preparation for machine learning. +# The |tabular_pipeline| function provides an easy way to build a simple +# but reliable machine learning model that works well on most tabular data. + + +# %% +from sklearn.model_selection import cross_validate + +from skrub import tabular_pipeline + +model = tabular_pipeline("regressor") +model +# %% +results = cross_validate(model, employees_df, salaries) +results["test_score"] + +# %% +# To handle rich tabular data and feed it to a machine learning model, the +# pipeline returned by |tabular_pipeline| preprocesses and encodes +# strings, categories and dates using the |TableVectorizer|. +# See its documentation or :ref:`sphx_glr_auto_examples_0010_encodings.py` for +# more details. An overview of the chosen defaults is available in +# :ref:`user_guide_tabular_pipeline`. + + +# %% +# Encoding any data as numerical features +# --------------------------------------- +# +# Tabular data can contain a variety of datatypes, from numerical to +# datetimes, categories, strings, and text. Encoding features in a meaningful +# way requires significant effort and is a major part of the feature engineering +# process required to properly train machine learning models. +# +# Skrub helps with this by providing various transformers that automatically +# encode different datatypes into ``float32`` features. +# +# For **numerical features**, the |SquashingScaler| applies a robust +# scaling technique that is less sensitive to outliers. Check the +# :ref:`relative example ` +# for more information on the feature. +# +# For **datetime columns**, skrub provides the |DatetimeEncoder| +# which can extract useful features such as year, month, day, as well as additional +# features such as weekday or day of year. Periodic encoding with trigonometric +# or spline features is also available. Refer to the |DatetimeEncoder| +# documentation for more detail. +# + +# %% +import pandas as pd + +data = pd.DataFrame( + { + "event": ["A", "B", "C"], + "date_1": ["2020-01-01", "2020-06-15", "2021-03-22"], + "date_2": ["2020-01-15", "2020-07-01", "2021-04-05"], + } +) +data = Cleaner().fit_transform(data) +TableReport(data) +# %% +# Skrub transformers are applied column-by-column, but it's possible to use +# the |ApplyToCols| meta-transformer to apply a transformer to +# multiple columns at once. Complex column selection is possible using +# :ref:`skrub's column selectors `. + +from skrub import ApplyToCols, DatetimeEncoder + +ApplyToCols( + DatetimeEncoder(add_total_seconds=False), cols=["date_1", "date_2"] +).fit_transform(data) + +# %% +# Finally, when a column contains **categorical or string data**, it can be +# encoded using various encoders provided by skrub. The default encoder is +# the |StringEncoder|, which encodes categories using +# `Latent Semantic Analysis (LSA) `_. +# It is a simple and efficient way to encode categories and works well in +# practice. + +data = pd.DataFrame( + { + "city": ["Paris", "London", "Berlin", "Madrid", "Rome"], + "country": ["France", "UK", "Germany", "Spain", "Italy"], + } +) +TableReport(data) +from skrub import StringEncoder + +StringEncoder(n_components=3).fit_transform(data["city"]) + +# %% +# If your data includes a lot of text, you may want to use the +# |TextEncoder|, +# which uses pre-trained language models retrieved from the HuggingFace hub to +# create meaningful text embeddings. +# See :ref:`user_guide_encoders_index` for more details on all the categorical encoders +# provided by skrub, and :ref:`sphx_glr_auto_examples_0010_encodings.py` for a +# comparison between the different methods. +# + +# %% +# Advanced use cases +# ---------------------- +# If your use case involves more complex data preparation, hyperparameter tuning, +# or model selection, if you want to build a multi-table pipeline that requires +# assembling and preparing multiple tables, or if you want to ensure that the +# data preparation can be reproduced exactly, you can use the skrub Data Ops, +# a powerful framework that provides tools to build complex data processing pipelines. +# See the related :ref:`user guide ` and the +# :ref:`data_ops_examples_ref` +# examples for more details. + +# %% +# Next steps +# ---------- +# +# We have briefly covered pipeline creation, vectorizing, assembling, and encoding +# data. We presented the main functionalities of skrub, but there is much +# more to explore! +# +# Please refer to our :ref:`user_guide` for a more in-depth presentation of +# skrub's concepts, or visit our +# `examples `_ for more +# illustrations of the tools that we provide! +# diff --git a/skrub/_docs/tutorials/1110_data_ops_intro.py b/skrub/_docs/tutorials/1110_data_ops_intro.py new file mode 100644 index 000000000..4f3fd807e --- /dev/null +++ b/skrub/_docs/tutorials/1110_data_ops_intro.py @@ -0,0 +1,210 @@ +""" +Tutorial: Using Data Ops to build a machine-learning pipeline +======================================================================= + +.. currentmodule:: skrub + +.. |fetch_employee_salaries| replace:: :func:`datasets.fetch_employee_salaries` +.. |TableReport| replace:: :class:`TableReport` +.. |var| replace:: :func:`var` +.. |skb.mark_as_X| replace:: :meth:`DataOp.skb.mark_as_X` +.. |skb.mark_as_y| replace:: :meth:`DataOp.skb.mark_as_y` +.. |TableVectorizer| replace:: :class:`TableVectorizer` +.. |ToDatetime| replace:: :class:`ToDatetime` +.. |skb.apply| replace:: :meth:`.skb.apply() ` +.. |HistGradientBoostingRegressor| replace:: + :class:`~sklearn.ensemble.HistGradientBoostingRegressor` +.. |.skb.full_report()| replace:: :meth:`.skb.full_report() ` +.. |choose_float| replace:: :func:`choose_float` +.. |make_randomized_search| replace:: + :meth:`.skb.make_randomized_search ` + +This example shows data how we can use skrub's +:ref:`DataOps ` for building a machine learning pipeline. + +The challenge of preparing data for machine learning is the need to +apply the same data preparation and wrangling operations to new data, for prediction. + +Skrub's DataOps build pipelines that blend data wrangling and machine +learning by recording all the operations involved in pre-processing data +and training models, as well as the state of the transformers and models used to +make predictions. + +.. admonition:: What is a state? + :collapsible: closed + + The state of a transformer or model refers to the internal parameters and + attributes that are learned or set during the fitting process. For example, + in a :class:`~sklearn.preprocessing.StandardScaler`, the state would include + the mean and standard deviation calculated from the training data. + In a pre-processing transformer like |ToDatetime|, the state would include the + inferred datetime format based on the data it was fitted on. + In a machine learning model like |HistGradientBoostingRegressor|, the state + would include the fitted parameters of the model after training on the data. + +The result of building a DataOps plan is a *learner*, an object with an interface +similar to that of a scikit-learn estimator, but which contains all the steps in the +data preparation and model training process, along with the state of all the +transformers and models: this allows to save the learner, load it back later, +and use it to make predictions on new data. + +This example is meant to be an introduction to skrub DataOps, and as such it +will not cover all the features. Further examples in the gallery +:ref:`data_ops_examples_ref` go into more detail on skrub DataOps +for more complex tasks. + + +""" + +# %% +# The data +# --------- +# +# We begin by loading the employee salaries dataset, which is a regression dataset +# that contains information about employees and their current annual salaries. +# By default, the |fetch_employee_salaries| function returns the training set. +# We will load the test set later, to evaluate our model on unseen data. + +import pandas as pd + +from skrub.datasets import fetch_employee_salaries + +training_data = pd.read_csv( + fetch_employee_salaries(split="train").employee_salaries_path +) + +# %% +# We can take a look at the dataset using the |TableReport|. +# This dataset contains numerical, categorical, and datetime features. The column +# ``current_annual_salary`` is the target variable we want to predict. +# + +import skrub + +skrub.TableReport(training_data) +# %% +# Assembling our DataOps plan +# ---------------------------- +# +# Our goal is to predict the ``current_annual_salary`` of employees based on their +# other features. We will use skrub's DataOps to combine both skrub and scikit-learn +# objects into a single DataOps plan, which will allow us to preprocess the data, +# train a model, and tune hyperparameters. +# +# We begin by defining a skrub |var|, which is the entry point for our DataOps plan. + +data_var = skrub.var("data", training_data) + +# %% +# Next, we define the initial features ``X`` and the target variable ``y``. +# We use the |skb.mark_as_X| and |skb.mark_as_y| methods to mark these variables +# in the DataOps plan. This allows skrub to properly split these objects into +# training and validation steps when executing cross-validation or hyperparameter +# tuning. + +X = data_var.drop("current_annual_salary", axis=1).skb.mark_as_X() +y = data_var["current_annual_salary"].skb.mark_as_y() +# %% +# Our first step is to vectorize the features in ``X``. We will use the +# |TableVectorizer| to convert the categorical and numerical features into a +# numerical format that can be used by machine learning algorithms. +# We apply the vectorizer to ``X`` using the |skb.apply| method, which allows us to +# apply any scikit-learn compatible transformer to the skrub variable. + +from skrub import TableVectorizer + +vectorizer = TableVectorizer() + +X_vec = X.skb.apply(vectorizer) +X_vec +# %% +# By clicking on ``Show graph``, we can see the DataOps plan that has been created: +# the plan shows the steps that have been applied to the data so far. +# Now that we have the vectorized features, we can proceed to train a model. +# We use a scikit-learn |HistGradientBoostingRegressor| to predict the target variable. +# We apply the model to the vectorized features using ``.skb.apply``, and pass +# ``y`` as the target variable. +# Note that the resulting ``predictor`` variable shows prediction results on the +# preview subsample, but the model will be properly fitted when we create the learner. + +from sklearn.ensemble import HistGradientBoostingRegressor + +hgb = HistGradientBoostingRegressor() + +predictor = X_vec.skb.apply(hgb, y=y) +predictor + +# %% +# Now that we have built our entire plan, we can explore it in more detail +# with the |.skb.full_report()| method:: +# +# predictor.skb.full_report() +# +# This produces a folder on disk rather than displaying inline in a notebook so +# we do not run it here. But you can +# `see the output here <../../_static/employee_salaries_report/index.html>`_. +# +# This method evaluates each step in +# the plan and shows detailed information about the operations that are being performed. + +# %% +# Turning the DataOps plan into a learner, for later reuse +# --------------------------------------------------------- +# +# Now that we have defined the predictor, we can create a ``learner``, a +# standalone object that contains all the steps in the DataOps plan. We fit the +# learner, so that it can be used to make predictions on new data. + +trained_learner = predictor.skb.make_learner(fitted=True) + +# %% +# A big advantage of the learner is that it can be pickled and saved to disk, +# allowing us to reuse the trained model later without needing to retrain it. +# The learner contains all steps in the DataOps plan, including the fitted +# vectorizer and the trained model. We can save it using Python's ``pickle`` module. +# Here we use ``pickle.dumps`` to serialize the learner object into a byte string. + +import pickle + +saved_model = pickle.dumps(trained_learner) + +# %% +# We can now load the saved model back into memory using ``pickle.loads``. +loaded_model = pickle.loads(saved_model) + +# %% +# Now, we can make predictions on new data using the loaded model, by passing +# a dictionary with the skrub variable names as keys. +# We don't have to create a new variable, as this will be done internally by the +# learner. +# In fact, the ``learner`` is similar to a scikit-learn estimator, but rather +# than taking ``X`` and ``y`` as inputs, it takes a dictionary (the "environment") +# where each key corresponds to the name of a skrub variable in the plan (in this +# case, "data"). +# +# We can now get the test set of the employee salaries dataset: +unseen_data = pd.read_csv(fetch_employee_salaries(split="test").employee_salaries_path) + +# %% +# Then, we can use the loaded model to make predictions on the unseen data by +# passing a dictionary with the variable name as the key. + +predicted_values = loaded_model.predict({"data": unseen_data}) +predicted_values + +# %% +# We can also evaluate the model's performance using the `score` method, which +# uses the scikit-learn scoring function used by the predictor: +loaded_model.score({"data": unseen_data}) + +# %% +# Conclusion +# ---------- +# +# In this example, we have briefly introduced the skrub DataOps and how they can +# be used to build powerful machine learning pipelines. We have shown how to preprocess +# data and train a model. We have also demonstrated how to save and load the trained +# model, and how to make predictions on new data. +# +# However, skrub DataOps are significantly more powerful than what we have shown here. +# For more advanced examples, see :ref:`data_ops_examples_ref`. diff --git a/skrub/_docs/tutorials/GALLERY_HEADER.txt b/skrub/_docs/tutorials/GALLERY_HEADER.txt new file mode 100644 index 000000000..1e107f52e --- /dev/null +++ b/skrub/_docs/tutorials/GALLERY_HEADER.txt @@ -0,0 +1 @@ +examples diff --git a/skrub/_docs/vision.rst b/skrub/_docs/vision.rst new file mode 100644 index 000000000..37fde54d0 --- /dev/null +++ b/skrub/_docs/vision.rst @@ -0,0 +1,64 @@ +=============================== +Vision: Where is skrub heading? +=============================== + +.. currentmodule:: skrub + +Vision Statement +================ + +The goal of skrub is to facilitate machine learning on tables: +`pandas `__ +and `polars `__ dataframes, SQL databases, and more. + +| + +Skrub is high-level, with a philosophy and API matching that of +`scikit-learn `_. It strives to bridge the world +of databases and machine learning, **enabling imperfect assembly and +representation of data when it is noisy**, using the downstream +target to guide assembly when possible (supervised learning for +data assembly). + +In the long term, as skrub is built on higher-level APIs, it will make it +easier for data scientists to use efficient database patterns and +backends. + +Skrub seeks tradeoffs in terms of flexibility: its high-level APIs are by +construction restrictive compared to directly manipulating dataframes. +This is by design, as skrub does not aim to replace tools such as `Pandas +`__, `Ibis `__, +`DuckDB `_. + +To make things simpler, skrub uses defaults that are chosen empirically to +give good machine learning, even though these are sometimes heuristic, as +in the :class:`TableVectorizer`. We keep the benchmarks used to choose the defaults +in a separate `repository `__. + +Roadmap +======= + +In an open-source project, roadmaps can be whishful thinking: things +happen in an iterative way, often guided by the community. + +We however decided to communicate on what we would like to do in the next +6 months to give a better idea of the vision. + +From shorter term to longer term: + +- Better support for time series + +- Data namespaces, lazy data loading, out of core computing using + database engines (e.g., duckdb) + +- Join discovery to work in data lakes where the tables are not in a + clean relational database + +- Automatic feature synthesis in databases, building on the assembling + features + + +Imputation is out of skrub's scope: scikit-learn implements transformers +that perform imputation. Academic work has also shown that imputation is +expensive and often does not improve prediction results +(https://arxiv.org/pdf/2407.19804). From e9f4ff7f78d98b2a606f54bf38cdec6cab40d81a Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 25 Jun 2026 10:50:19 +0200 Subject: [PATCH 25/28] updating examples --- .gitignore | 19 + doc/Makefile | 25 +- doc/conf.py | 27 +- .../type_of_selectors.rst | 1 - examples/0010_apply_to_cols.py | 174 -- examples/0050_deduplication.py | 164 -- examples/0100_squashing_scaler.py | 204 --- examples/01_encoding/0010_encodings.py | 377 ---- .../0020_text_with_string_encoders.py | 346 ---- examples/01_encoding/0030_datetime_encoder.py | 355 ---- examples/01_encoding/GALLERY_HEADER.rst | 2 - examples/02_data_ops/1120_multiple_tables.py | 246 --- examples/02_data_ops/1130_choices.py | 284 --- examples/02_data_ops/1131_optuna_choices.py | 188 -- examples/02_data_ops/1140_subsampling.py | 108 -- examples/02_data_ops/1150_use_case.py | 170 -- examples/02_data_ops/1160_pytorch.py | 218 --- examples/02_data_ops/GALLERY_HEADER.rst | 4 - examples/03_joining/0040_fuzzy_joining.py | 408 ----- examples/03_joining/0060_multiple_key_join.py | 184 -- examples/03_joining/0070_join_aggregation.py | 352 ---- .../03_joining/0080_interpolation_join.py | 214 --- examples/03_joining/GALLERY_HEADER.rst | 2 - examples/GALLERY_HEADER.rst | 2 - pyproject.toml | 4 +- skrub/_docs/CHANGES.rst | 1632 ----------------- skrub/_docs/CONTRIBUTING.rst | 498 ----- skrub/_docs/RELEASE_PROCESS.rst | 157 -- skrub/_docs/_templates/base.rst | 37 - skrub/_docs/_templates/data_op_class.rst | 6 - skrub/_docs/_templates/numpydoc_docstring.rst | 16 - skrub/_docs/includes/big_toc_css.rst | 160 -- skrub/_docs/sg_execution_times.rst | 88 - 33 files changed, 53 insertions(+), 6619 deletions(-) delete mode 100644 examples/0010_apply_to_cols.py delete mode 100644 examples/0050_deduplication.py delete mode 100644 examples/0100_squashing_scaler.py delete mode 100644 examples/01_encoding/0010_encodings.py delete mode 100644 examples/01_encoding/0020_text_with_string_encoders.py delete mode 100644 examples/01_encoding/0030_datetime_encoder.py delete mode 100644 examples/01_encoding/GALLERY_HEADER.rst delete mode 100644 examples/02_data_ops/1120_multiple_tables.py delete mode 100644 examples/02_data_ops/1130_choices.py delete mode 100644 examples/02_data_ops/1131_optuna_choices.py delete mode 100644 examples/02_data_ops/1140_subsampling.py delete mode 100644 examples/02_data_ops/1150_use_case.py delete mode 100644 examples/02_data_ops/1160_pytorch.py delete mode 100644 examples/02_data_ops/GALLERY_HEADER.rst delete mode 100644 examples/03_joining/0040_fuzzy_joining.py delete mode 100644 examples/03_joining/0060_multiple_key_join.py delete mode 100644 examples/03_joining/0070_join_aggregation.py delete mode 100644 examples/03_joining/0080_interpolation_join.py delete mode 100644 examples/03_joining/GALLERY_HEADER.rst delete mode 100644 examples/GALLERY_HEADER.rst delete mode 100644 skrub/_docs/CHANGES.rst delete mode 100644 skrub/_docs/CONTRIBUTING.rst delete mode 100644 skrub/_docs/RELEASE_PROCESS.rst delete mode 100644 skrub/_docs/_templates/base.rst delete mode 100644 skrub/_docs/_templates/data_op_class.rst delete mode 100644 skrub/_docs/_templates/numpydoc_docstring.rst delete mode 100644 skrub/_docs/includes/big_toc_css.rst delete mode 100644 skrub/_docs/sg_execution_times.rst diff --git a/.gitignore b/.gitignore index ddd8cd7d2..fc9537593 100644 --- a/.gitignore +++ b/.gitignore @@ -67,6 +67,25 @@ doc/CHANGES.rst doc/RELEASE_PROCESS.rst doc/CONTRIBUTING.rst doc/sg_execution_times.rst +# RST content files synced from skrub/_docs at build time (conf.py) +doc/about.rst +doc/column_level_featurizing.rst +doc/data_ops.rst +doc/default_wrangling.rst +doc/development.rst +doc/documentation.rst +doc/exploring_a_dataframe.rst +doc/howto.rst +doc/index.rst +doc/install.rst +doc/joining_dataframes.rst +doc/learning_materials.rst +doc/multi_column_operations.rst +doc/tutorial_example.rst +doc/vision.rst +doc/guides/ +doc/modules/ +doc/tutorials/ .DS_Store doc/_templates/demo_table_report_generated.html doc/reference/*.rst diff --git a/doc/Makefile b/doc/Makefile index 2b8dfbcd6..c252f6dc6 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -63,22 +63,15 @@ markdown-noplot: @echo @echo "Markdown build (no plot) finished. The markdown files are in $(BUILDDIR)/markdown." -# Copy the generated markdown docs into the skrub/ package tree so they -# get bundled with the wheel. Run after html, html-noplot, markdown, or -# markdown-noplot. -install-docs: - rm -rf ../skrub/_docs - mkdir -p ../skrub/_docs - mkdir -p ../skrub/_docs/examples - mkdir -p ../skrub/_docs/tutorials - mkdir -p ../skrub/_docs/guides - cp -r $(BUILDDIR)/markdown/guides/* ../skrub/_docs/guides - cp -r $(BUILDDIR)/markdown/modules/* ../skrub/_docs/guides - cp -r $(BUILDDIR)/markdown/auto_examples/* ../skrub/_docs/examples - cp -r $(BUILDDIR)/markdown/auto_tutorials/* ../skrub/_docs/tutorials - cp -r $(BUILDDIR)/markdown/*.md ../skrub/_docs/ - cp -r $(BUILDDIR)/html/llms.txt ../skrub/_docs/ - find ../skrub/_docs -name "*.md" | wc -l | xargs echo "Number of markdown files installed:" +# skrub/_docs is the single source of truth for guide/content RST files. +# They are synced into doc/ automatically at build time by conf.py. +# Use this target to verify the two trees are in sync (no-op if they match). +check-docs-sync: + diff -rq --exclude="*.pyc" ../skrub/_docs/ . \ + --exclude="CHANGES.rst" --exclude="CONTRIBUTING.rst" \ + --exclude="RELEASE_PROCESS.rst" \ + $(addprefix --exclude=,$(notdir $(wildcard ../skrub/_docs/*.rst))) \ + && echo "skrub/_docs and doc/ are in sync" || true # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). diff --git a/doc/conf.py b/doc/conf.py index 5c1b7cba9..9094b0db8 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -50,14 +50,33 @@ from github_link import make_linkcode_resolve from sphinx_gallery.notebook import add_code_cell, add_markdown_cell -# -- Copy files for docs -------------------------------------------------- +# -- Sync documentation source files from skrub/_docs -------------------- # -# We avoid duplicating the information, but we do not use symlinks to be -# able to build the docs on Windows +# skrub/_docs is the single source of truth for all guide/content RST files +# so they are packaged with the wheel. We copy them into doc/ at build time +# rather than using symlinks (to support Windows builds). +# +# CHANGES.rst, CONTRIBUTING.rst and RELEASE_PROCESS.rst are canonical in the +# project root and are NOT stored in skrub/_docs. shutil.copyfile("../RELEASE_PROCESS.rst", "RELEASE_PROCESS.rst") shutil.copyfile("../CHANGES.rst", "CHANGES.rst") shutil.copyfile("../CONTRIBUTING.rst", "CONTRIBUTING.rst") +_docs_src = Path("../skrub/_docs") + +# Copy top-level RST content files +_skip_toplevel = {"CHANGES.rst", "CONTRIBUTING.rst", "RELEASE_PROCESS.rst"} +for _rst_file in _docs_src.glob("*.rst"): + if _rst_file.name not in _skip_toplevel: + shutil.copyfile(_rst_file, _rst_file.name) + +# Copy content subdirectories (guides, modules) +for _subdir in ["guides", "modules"]: + shutil.copytree(_docs_src / _subdir, _subdir, dirs_exist_ok=True) + +# Copy tutorials source files for sphinx-gallery +shutil.copytree(_docs_src / "tutorials", "tutorials", dirs_exist_ok=True) + # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. @@ -496,7 +515,7 @@ def call_garbage_collector(gallery_conf, fname): # See https://sphinx-gallery.github.io/stable/configuration.html#link-to-documentation # noqa }, "filename_pattern": ".*", - "examples_dirs": ["../examples", "tutorials"], + "examples_dirs": ["../skrub/_docs/examples", "tutorials"], "gallery_dirs": ["auto_examples", "auto_tutorials"], "within_subsection_order": FileNameSortKey, "download_all_examples": False, diff --git a/doc/modules/multi_column_operations/type_of_selectors.rst b/doc/modules/multi_column_operations/type_of_selectors.rst index 75df623a6..87d598187 100644 --- a/doc/modules/multi_column_operations/type_of_selectors.rst +++ b/doc/modules/multi_column_operations/type_of_selectors.rst @@ -79,7 +79,6 @@ Selectors based on column data types - :func:`~skrub.selectors.any_date`: Select columns with date or datetime data types - :func:`~skrub.selectors.categorical`: Select columns with categorical data types - :func:`~skrub.selectors.string`: Select columns with string data types -- :func:`~skrub.selectors.object`: Select columns with the ``object`` (pandas) or ``pl.Object`` (polars) dtype - :func:`~skrub.selectors.boolean`: Select columns with boolean data types Selectors based on column content and properties diff --git a/examples/0010_apply_to_cols.py b/examples/0010_apply_to_cols.py deleted file mode 100644 index 2e45f4dda..000000000 --- a/examples/0010_apply_to_cols.py +++ /dev/null @@ -1,174 +0,0 @@ -""" -Hands-On with Column Selection and Transformers -=============================================== - -In previous examples, we saw how skrub provides powerful abstractions like -:class:`~skrub.TableVectorizer` and :func:`~skrub.tabular_pipeline` to create pipelines. - -In this new example, we show how to create more flexible pipelines by selecting -and transforming dataframe columns using arbitrary logic. - -.. |ApplyToCols| replace:: :class:`~skrub.ApplyToCols` -.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` -.. |SelectCols| replace:: :class:`~skrub.SelectCols` -.. |DropCols| replace:: :class:`~skrub.DropCols` -.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` -.. |OrdinalEncoder| replace:: :class:`~sklearn.preprocessing.OrdinalEncoder` -.. |PCA| replace:: :class:`~sklearn.decomposition.PCA` -.. |Pipeline| replace:: :class:`~sklearn.pipeline.Pipeline` -.. |ColumnTransformer| replace:: :class:`~sklearn.compose.ColumnTransformer` - -""" - -# %% -# We begin with loading a dataset with heterogeneous datatypes, and replacing Pandas's -# display with the TableReport display via :func:`skrub.patch_display`. -import pandas as pd - -import skrub -from skrub.datasets import fetch_employee_salaries - -skrub.patch_display() -file_path = fetch_employee_salaries().path -data = pd.read_csv(file_path) -X = data.drop(columns="current_annual_salary") -y = data["current_annual_salary"] -X - -# %% -# Our goal is now to apply a |StringEncoder| to two columns of our -# choosing: ``division`` and ``employee_position_title``. -# -# We can achieve this using |ApplyToCols|, whose job is to apply a -# transformer to multiple columns independently, and let unmatched columns through -# without changes. -# This can be seen as a handy drop-in replacement of the -# |ColumnTransformer|. -# -# Since we selected two columns and set the number of components to ``30`` each, -# |ApplyToCols| will create ``2*30`` embedding columns in the dataframe -# ``Xt``, which we prefix with ``lsa_``. -from skrub import ApplyToCols, StringEncoder - -apply_string_encoder = ApplyToCols( - StringEncoder(n_components=30), - cols=["division", "employee_position_title"], - rename_columns="lsa_{}", -) -Xt = apply_string_encoder.fit_transform(X) -Xt - -# %% -# The |ApplyToCols| class can detect automatically whether the transformer is a -# ``SingleColumnTransformer`` (i.e., it can only be applied to one column at a time) -# or not, and apply it accordingly. The |StringEncoder| is a ``SingleColumnTransformer`` -# and thus applied to each column independently. - -# %% -# The |ApplyToCols| class can also be used with transformers that -# can be applied to multiple columns at once, such as the |PCA|. -# Here, we want to use PCA to reduce the number of dimensions of the new ``lsa_`` -# columns. -# -# To select columns without hardcoding their names, we introduce -# :ref:`selectors`, which allow for flexible matching pattern -# and composable logic. -# -# The regex selector below will match all columns prefixed with ``"lsa"``, and pass them -# to |ApplyToCols| which will assemble these columns into a dataframe -# and finally pass it to the PCA -# -# Note that |ApplyToCols| will automatically detect that PCA is not a -# ``SingleColumnTransformer`` -# and apply it to the whole sub-dataframe of columns chosen by the selector at once. - -from sklearn.decomposition import PCA - -from skrub import selectors as s - -apply_pca = ApplyToCols(PCA(n_components=8), cols=s.regex("lsa")) -Xt = apply_pca.fit_transform(Xt) -Xt - -# %% -# These two selectors are scikit-learn transformers and can be chained together within -# a |Pipeline|. -from sklearn.pipeline import make_pipeline - -model = make_pipeline( - apply_string_encoder, - apply_pca, -).fit_transform(X) - -# %% -# .. admonition:: Under the hood of |ApplyToCols| -# :collapsible: closed -# -# |ApplyToCols| is implemented using the ``ApplyToEachCol`` and ``ApplyToSubFrame`` -# classes. -# The former applies a transformer to each column independently, while the latter -# applies a transformer to a sub-dataframe. -# Normally, users don't need to worry about these two classes, but they can be useful -# when more control is needed. - -# %% -# Note that selectors also come in handy in a pipeline to select or drop columns, using -# |SelectCols| and |DropCols|. -from sklearn.preprocessing import StandardScaler - -from skrub import SelectCols - -# Select only numerical columns -pipeline = make_pipeline( - SelectCols(cols=s.numeric()), - StandardScaler(), -).set_output(transform="pandas") -pipeline.fit_transform(Xt) - -# %% -# Let's run through one more example to showcase the expressiveness of the selectors. -# Suppose we want to apply an |OrdinalEncoder| on -# categorical columns with low cardinality (e.g., fewer than ``40`` unique values). -# -# We define a column filter using skrub selectors with a lambda function. Note that -# the same effect can be obtained directly by using -# :func:`~skrub.selectors.cardinality_below`. -from sklearn.preprocessing import OrdinalEncoder - -low_cardinality = s.filter(lambda col: col.nunique() < 40) -ApplyToCols(OrdinalEncoder(), cols=s.string() & low_cardinality).fit_transform(X) - -# %% -# Notice how we composed the selector with :func:`~skrub.selectors.string()` -# using a logical operator. This resulting selector matches string -# columns with cardinality below ``40``. -# -# We can also define the opposite selector ``high_cardinality`` using the negation -# operator ``~`` and apply a |StringEncoder| to vectorize those -# columns. -from sklearn.ensemble import HistGradientBoostingRegressor - -high_cardinality = ~low_cardinality -pipeline = make_pipeline( - ApplyToCols( - OrdinalEncoder(), - cols=s.string() & low_cardinality, - ), - ApplyToCols( - StringEncoder(), - cols=s.string() & high_cardinality, - ), - HistGradientBoostingRegressor(), -).fit(X, y) -pipeline - -# %% -# Interestingly, the pipeline above is similar to the datatype dispatching performed by -# |TableVectorizer|, also used in :func:`~skrub.tabular_pipeline`. -# -# Click on the dropdown arrows next to the datatype to see the columns are mapped to -# the different transformers in |TableVectorizer|. -from skrub import tabular_pipeline - -tabular_pipeline("regressor").fit(X, y) -# %% diff --git a/examples/0050_deduplication.py b/examples/0050_deduplication.py deleted file mode 100644 index f877c6190..000000000 --- a/examples/0050_deduplication.py +++ /dev/null @@ -1,164 +0,0 @@ -""" -.. _examples_deduplication: - -=================================== -Deduplicating misspelled categories -=================================== - -Real-world datasets often come with misspellings, for instance -in manually inputted categorical variables. -Such misspellings break data analysis steps that require -exact matching, such as a ``GROUP BY`` operation. - -Merging multiple variants of the same category is known as -*deduplication*. It is implemented in skrub with the |deduplicate| function. - -Deduplication relies on *unsupervised learning*. It finds structures in -the data without providing a-priori known and explicit labels/categories. -Specifically, measuring the distance between strings can be used to -find clusters of strings that are similar to each other (e.g. differ only -by a misspelling) and hence, flag and regroup potentially -misspelled category names in an unsupervised manner. - - -.. |deduplicate| replace:: - :func:`~skrub.deduplicate` - -.. |Gap| replace:: - :class:`~skrub.GapEncoder` - -.. |MinHash| replace:: - :class:`~skrub.MinHashEncoder` -""" - -############################################################################### -# A typical use case -# ------------------ -# -# Let's take an example: -# as a data scientist, your job is to analyze the data from a hospital ward. -# In the data, we notice that in most cases, the doctor prescribes -# one of three following medications: -# "Contrivan", "Genericon" or "Zipholan". -# -# However, data entry is manual and - either because the doctor's -# handwriting was hard to decipher, or due to mistakes during input - -# there are multiple spelling mistakes in the dataset. -# -# Let's generate this example dataset: - -import numpy as np -import pandas as pd - -from skrub.datasets import make_deduplication_data - -duplicated_names = make_deduplication_data( - examples=["Contrivan", "Genericon", "Zipholan"], # our three medication names - entries_per_example=[500, 100, 1500], # their respective number of occurrences - prob_mistake_per_letter=0.05, # 5% probability of typo per letter - random_state=42, # set seed for reproducibility -) - -duplicated_names[:5] - -############################################################################### -# We then extract the unique medication names in the data and -# visualize how often they appear: - -import matplotlib.pyplot as plt - -unique_examples, counts = np.unique(duplicated_names, return_counts=True) - -plt.figure(figsize=(10, 15)) -plt.barh(unique_examples, counts) -plt.ylabel("Medication name") -plt.xlabel("Count") -plt.show() - -############################################################################### -# We clearly see the structure of the data: -# the three original medications ("Contrivan", "Genericon" and "Zipholan") -# are the most common ones, but there are many spelling mistakes or -# slight variations of the original names. -# -# The idea behind |deduplicate| is to use the fact that -# the string distance of misspelled medications will be -# closest to their original (most frequent) medication name -# - and therefore form clusters. - -############################################################################### -# Deduplication: suggest corrections of misspelled names -# ------------------------------------------------------ -# -# The |deduplicate| function uses clustering based on -# string similarities to group duplicated names. -# -# Let's deduplicate our data: - -from skrub import deduplicate - -deduplicated_data = deduplicate(duplicated_names) - -deduplicated_data[:5] - -############################################################################### -# And that's it! We now have the deduplicated data. -# -# .. topic:: Note: -# -# The number of clusters will need some adjustment depending on the data. -# If no fixed number of clusters is given, |deduplicate| tries to set it -# automatically via the -# `silhouette score `_. - -############################################################################### -# We can visualize the distribution of categories in the deduplicated data: - -deduplicated_unique_examples, deduplicated_counts = np.unique( - deduplicated_data, return_counts=True -) -deduplicated_series = pd.Series(deduplicated_counts, index=deduplicated_unique_examples) - -plt.figure(figsize=(10, 5)) -plt.barh(deduplicated_unique_examples, deduplicated_counts) -plt.xlabel("Count") -plt.ylabel("Medication name") -plt.show() - -############################################################################### -# Here, the silhouette score finds the ideal number of -# clusters (3) and groups the spelling mistakes. -# -# In practice, the translation/deduplication will often be imperfect -# and require some tweaks. -# In this case, we can construct and update a translation table based on the -# data returned by |deduplicate|. - -# create a table that maps original to corrected categories -translation_table = pd.Series(deduplicated_data, index=duplicated_names) - -# remove duplicates in the original data -translation_table = translation_table[~translation_table.index.duplicated(keep="first")] - -translation_table.head() - -############################################################################### -# In this table, we have the category name on the left, -# and the cluster it was translated to on the right. -# If we want to adapt the translation table, we can -# modify it manually. - - -############################################################################### -# Conclusion -# ---------- -# -# In this example, we have seen how to use the |deduplicate| function to -# automatically detect and correct misspelled category names. -# -# Note that deduplication is especially useful when we either -# know our ground truth (e.g. the original medication names), -# or when the similarity across strings does not -# carry useful information for our machine learning task. -# Otherwise, we prefer using encoding methods such as |Gap| -# or |MinHash|. diff --git a/examples/0100_squashing_scaler.py b/examples/0100_squashing_scaler.py deleted file mode 100644 index ba6f90110..000000000 --- a/examples/0100_squashing_scaler.py +++ /dev/null @@ -1,204 +0,0 @@ -""" -SquashingScaler: Robust numerical preprocessing for neural networks -=================================================================== - -The following example illustrates the use of the :class:`~skrub.SquashingScaler`, a -transformer that can rescale and squash numerical features to a range that works well -with neural networks and perhaps also other related models. Its basic idea is to -rescale the features based on quantile statistics (to be robust to outliers), and then -perform a smooth squashing function to limit the outputs to a pre-defined range. -This transform has been found to even work well when applied to one-hot encoded -features. - -We first generate some synthetic data with outliers to show how different scalers -transform the data, then we show how the choice of the scaler affects the prediction -performance of a simple neural network. - -.. |SquashingScaler| replace:: :class:`~skrub.SquashingScaler` -.. |RobustScaler| replace:: :class:`~sklearn.preprocessing.RobustScaler` -.. |StandardScaler| replace:: :class:`~sklearn.preprocessing.StandardScaler` -.. |QuantileTransformer| replace:: :class:`~sklearn.preprocessing.QuantileTransformer` - -""" - -# %% -# Plotting the effect of different scalers -# ---------------------------------------- -# -# First, let's import the |SquashingScaler|, as well as the usual scikit-learn -# |StandardScaler| and |RobustScaler|. - -# %% -import numpy as np -from sklearn.preprocessing import QuantileTransformer, RobustScaler, StandardScaler - -from skrub import SquashingScaler - -np.random.seed(0) # for reproducibility - -# %% -# We then generate some random values sampling from a uniform distribution in the -# range ``[0, 1]``: note that this will produce values that are always positive. -# We then add some outliers in random positions in the array. -# Subtracting 50 allows to have some negative outliers in the data. - -values = np.random.rand(100, 1) -n_outliers = 15 -outlier_indices = np.random.choice(values.shape[0], size=n_outliers, replace=False) -values[outlier_indices] = np.random.rand(n_outliers, 1) * 100 - 50 - -# %% -# We then create one of each scaler and use them to scale the data independently. - -# %% -squash_scaler = SquashingScaler() -squash_scaled = squash_scaler.fit_transform(values) - -robust_scaler = RobustScaler() -robust_scaled = robust_scaler.fit_transform(values) - -standard_scaler = StandardScaler() -standard_scaled = standard_scaler.fit_transform(values) - -quantile_transformer = QuantileTransformer(n_quantiles=100) -quantile_scaled = quantile_transformer.fit_transform(values) - - -# %% -# To better show the effect of scaling, we create two plots, where we display the -# data points after sorting them in ascending order: in this way, all outliers -# are close to each other and with the proper sign. -# We create two subplots because the scale of the outliers is much larger than that -# of the inliers, which means that any detail in the inlier would be hidden. - -# %% -import matplotlib.pyplot as plt - -x = np.arange(values.shape[0]) - -fig, axs = plt.subplots(1, 2, layout="constrained", figsize=(10, 5)) - -ax = axs[0] -ax.plot(x, sorted(values), label="Original Values", linewidth=2.5) -ax.plot(x, sorted(squash_scaled), label="SquashingScaler") -ax.plot(x, sorted(robust_scaled), label="RobustScaler", linestyle="--") -ax.plot(x, sorted(standard_scaled), label="StandardScaler") -ax.plot(x, sorted(quantile_scaled), label="QuantileTransformer") - -# Add a horizontal band in [-4, +4] -ax.axhspan(-4, 4, color="gray", alpha=0.15) -ax.set(title="Original data", xlim=[0, values.shape[0]], xlabel="Percentile") -ax.legend() - -ax = axs[1] -ax.plot(x, sorted(values), label="Original Values", linewidth=2.5) -ax.plot(x, sorted(squash_scaled), label="SquashingScaler") -ax.plot(x, sorted(robust_scaled), label="RobustScaler", linestyle="--") -ax.plot(x, sorted(standard_scaled), label="StandardScaler") -ax.plot(x, sorted(quantile_scaled), label="QuantileTransformer") - -ax.set(ylim=[-4, 4]) -ax.set(title="In range [-4, 4]", xlim=[0, values.shape[0]], xlabel="Percentile") - -# Highlight the bounds of the SquashingScaler -ax.axhline(y=3, alpha=0.2) -ax.axhline(y=-3, alpha=0.2) - -fig.suptitle( - "Comparison of different scalers on sorted data with outliers", fontsize=20 -) -fig.supylabel("Value") - -# %% -# The figure on the left immediately shows how the scale of the data may be completely -# off because of a minority of outliers, with the RobustScaler following the behavior -# of the original by retaining the larger scale of the outliers. On the other hand, -# both the SquashingScaler and the StandardScaler remain roughly in the ``[-4, 4]`` -# range (highlighted in grey in the left figure). -# -# In the right figure we can then spot how the presence of outliers has completely -# flattened the curve produced by the StandardScaler, forcing the inliers to be -# very close to 0. The RobustScaler and the SquashingScaler instead follow the original -# data much more closely, after centering it on 0. -# -# Finally, the SquashingScaler performs a smooth clipping of outliers, constraining -# all values to be in the range ``[-max_absolute_value, max_absolute_value]``, -# where ``max_absolute_value`` is a parameter specified by the user (3 by default). - -# %% -# Comparing numerical pre-processing methods on a neural network -# -------------------------------------------------------------- -# -# In the second part of the example, we want to fit a neural network to predict -# employee salaries. -# The dataset contains numerical features, categorical features, text features, -# and dates. -# These features are first converted to numerical features using -# :class:`~skrub.TableVectorizer`. Since the encoded features are not normalized, -# we apply a numerical transformation to them. -# -# Finally, we fit a simple neural network and compare the R2 scores obtained with -# different numerical transformations. -# -# While we use a simple :class:`~sklearn.neural_network.MLPRegressor` here for -# simplicity, we generally recommend using better neural network implementations -# or tree-based models whenever low test errors are desired. - -# %% -# We test the :class:`~skrub.SquashingScaler` against the -# :class:`~sklearn.preprocessing.StandardScaler` and the -# :class:`~sklearn.preprocessing.QuantileTransformer` from scikit-learn. We put -# each of these together in a pipeline with a TableVectorizer and a simple MLPRegressor. -# In the end, we print the R2 scores of each fold's validation set in a three-fold -# cross-validation. - -import warnings - -import numpy as np -import pandas as pd -from sklearn.compose import TransformedTargetRegressor -from sklearn.exceptions import ConvergenceWarning -from sklearn.model_selection import cross_validate -from sklearn.neural_network import MLPRegressor -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import QuantileTransformer, StandardScaler - -from skrub import DatetimeEncoder, SquashingScaler, TableVectorizer -from skrub.datasets import fetch_employee_salaries - -np.random.seed(0) -file_path = fetch_employee_salaries().path -data = pd.read_csv(file_path) -X = data.drop(columns="current_annual_salary") -y = data["current_annual_salary"] - -for num_transformer in [ - StandardScaler(), - QuantileTransformer(output_distribution="normal", random_state=0), - SquashingScaler(), -]: - pipeline = make_pipeline( - TableVectorizer(datetime=DatetimeEncoder(periodic_encoding="circular")), - num_transformer, - TransformedTargetRegressor( - # We use lbfgs for faster convergence - MLPRegressor(solver="lbfgs", max_iter=100), - transformer=StandardScaler(), - ), - ) - with warnings.catch_warnings(): - # Ignore warnings about the MLPRegressor not converging - warnings.simplefilter("ignore", category=ConvergenceWarning) - scores = cross_validate(pipeline, X, y, cv=3, scoring="r2") - - print( - f"Cross-validation R2 scores for {num_transformer.__class__.__name__}" - f" (higher is better):\n{scores['test_score']}\n" - ) - -# %% -# On the employee salaries dataset, the |SquashingScaler| performs -# better than |StandardScaler| and |QuantileTransformer| on all -# cross-validation folds. - -# %% diff --git a/examples/01_encoding/0010_encodings.py b/examples/01_encoding/0010_encodings.py deleted file mode 100644 index 71a666ab2..000000000 --- a/examples/01_encoding/0010_encodings.py +++ /dev/null @@ -1,377 +0,0 @@ -""" -.. _example_encodings: - -===================================================================== -Encoding: from a dataframe to a numerical matrix for machine learning -===================================================================== - -This example shows how to transform a rich dataframe with columns of various types -into a numerical matrix on which machine-learning algorithms can be applied. -We study the case of predicting wages using the -`employee salaries `_ dataset. - -.. |TableVectorizer| replace:: - :class:`~skrub.TableVectorizer` - -.. |Pipeline| replace:: - :class:`~sklearn.pipeline.Pipeline` - -.. |OneHotEncoder| replace:: - :class:`~sklearn.preprocessing.OneHotEncoder` - -.. |GapEncoder| replace:: - :class:`~skrub.GapEncoder` - -.. |MinHashEncoder| replace:: - :class:`~skrub.MinHashEncoder` - -.. |DatetimeEncoder| replace:: - :class:`~skrub.DatetimeEncoder` - -.. |HGBR| replace:: - :class:`~sklearn.ensemble.HistGradientBoostingRegressor` - -.. |RandomForestRegressor| replace:: - :class:`~sklearn.ensemble.RandomForestRegressor` - -.. |permutation importances| replace:: - :func:`~sklearn.inspection.permutation_importance` -""" - -############################################################################### -# Easy learning on a dataframe -# ---------------------------- -# -# Let's first retrieve the dataset, using one of the downloaders from the -# :mod:`skrub.datasets` module. As all the downloaders, -# :func:`~skrub.datasets.fetch_employee_salaries` returns a dataset with a ``path`` -# field pointing to the dataframe file, which contains both the features and the -# target. We load the dataframe from the path using pandas. -# ``X`` is a dataframe which contains the -# features (aka design matrix, explanatory variables, independent variables). -# ``y`` is a column (pandas Series) which contains the target (aka dependent, response -# variable) that we want to learn to predict from ``X``. In this case ``y`` is the -# annual salary, found in column "current_annual_salary". - -import pandas as pd - -from skrub.datasets import fetch_employee_salaries - -file_path = fetch_employee_salaries().path -employees = pd.read_csv(file_path) -X = employees.drop(columns="current_annual_salary") -y = employees["current_annual_salary"] - -############################################################################### -# Most machine-learning algorithms work with arrays of numbers. The -# challenge here is that the ``employees`` dataframe is a heterogeneous -# set of columns: some are numerical (``'year_first_hired'``), some dates -# (``'date_first_hired'``), some have a few categorical entries -# (``'gender'``), some many (``'employee_position_title'``). Therefore -# our table needs to be "vectorized": processed to extract numeric -# features. -# -# ``skrub`` provides an easy way to build a simple but reliable -# machine-learning model which includes this step, working well on most -# tabular data. - -from sklearn.model_selection import cross_validate - -from skrub import tabular_pipeline - -model = tabular_pipeline("regressor") -results = cross_validate(model, X, y) -results["test_score"] - -# %% -# The estimator returned by :obj:`tabular_pipeline` combines 2 steps: -# -# - a |TableVectorizer| to preprocess the dataframe and vectorize the features -# - a supervised learner (by default a |HGBR|) -model - -# %% -# In the rest of this example, we focus on the first step and explore the -# capabilities of skrub's |TableVectorizer|. -# -# | - -# %% -# More details on encoding tabular data -# ------------------------------------- - -from skrub import TableVectorizer - -vectorizer = TableVectorizer() -vectorized_X = vectorizer.fit_transform(X) -vectorized_X - -############################################################################### -# From our 8 columns, the |TableVectorizer| has extracted 143 numerical -# features. Most of them are one-hot encoded representations of the categorical -# features. For example, we can see that 3 columns ``'gender_F'``, ``'gender_M'``, -# ``'gender_nan'`` were created to encode the ``'gender'`` column. - -############################################################################### -# By performing appropriate transformations on our complex data, the |TableVectorizer| -# produced numeric features that we can use for machine-learning: - -from sklearn.ensemble import HistGradientBoostingRegressor - -HistGradientBoostingRegressor().fit(vectorized_X, y) - -############################################################################### -# The |TableVectorizer| bridges the gap between tabular data and machine-learning -# pipelines. It allows us to apply a machine-learning estimator to our dataframe without -# manual data wrangling and feature extraction. -# - -############################################################################### -# Inspecting the TableVectorizer -# ------------------------------ -# -# The |TableVectorizer| distinguishes between 4 basic kinds of columns (more may be -# added in the future). -# For each kind, it applies a different transformation, which we can configure. The -# kinds of columns and the default transformation for each of them are: -# -# - numeric columns: simply casting to floating-point -# - datetime columns: extracting features such as year, day, hour with the -# |DatetimeEncoder| -# - low-cardinality categorical columns: one-hot encoding -# - high-cardinality categorical columns: a simple and effective text representation -# pipeline provided by the |GapEncoder| - -vectorizer - -############################################################################### -# We can inspect which transformation was chosen for each column and retrieve the -# fitted transformer. ``vectorizer.kind_to_columns_`` provides an overview of how the -# vectorizer categorized columns in our input: - -vectorizer.kind_to_columns_ - -############################################################################### -# The reverse mapping is given by: - -vectorizer.column_to_kind_ - -############################################################################### -# ``vectorizer.transformers_`` gives us a dictionary which maps column names to the -# corresponding transformer. - -vectorizer.transformers_["date_first_hired"] - -############################################################################### -# We can also see which features in the vectorizer's output were derived from a given -# input column. - -vectorizer.input_to_outputs_["date_first_hired"] - -############################################################################### - -vectorized_X[vectorizer.input_to_outputs_["date_first_hired"]] - -############################################################################### -# Finally, we can go in the opposite direction: given a column in the input, find out -# from which input column it was derived. - -vectorizer.output_to_input_["department_BOA"] - - -############################################################################### -# Dataframe preprocessing -# ~~~~~~~~~~~~~~~~~~~~~~~ -# -# Note that ``"date_first_hired"`` has been recognized and processed as a datetime -# column. - -vectorizer.column_to_kind_["date_first_hired"] - -############################################################################### -# But looking closer at our original dataframe, it was encoded as a string. - -X["date_first_hired"] - -############################################################################### -# Note the ``dtype: object`` in the output above. -# Before applying the transformers we specify, the |TableVectorizer| performs a few -# preprocessing steps. -# -# For example, strings commonly used to represent missing values such as ``"N/A"`` are -# replaced with actual ``null``. As we saw above, columns containing strings that -# represent dates (e.g. ``'2024-05-15'``) are detected and converted to proper -# datetimes. -# -# We can inspect the list of steps that were applied to a given column: - -vectorizer.all_processing_steps_["date_first_hired"] - -############################################################################### -# These preprocessing steps depend on the column: - -vectorizer.all_processing_steps_["department"] - -############################################################################### - - -############################################################################### -# A simple Pipeline for tabular data -# ---------------------------------- -# -# The |TableVectorizer| outputs data that can be understood by a scikit-learn -# estimator. Therefore we can easily build a 2-step scikit-learn ``Pipeline`` -# that we can fit, test or cross-validate and that works well on tabular data. - -import numpy as np -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.model_selection import cross_validate -from sklearn.pipeline import make_pipeline - -pipeline = make_pipeline(TableVectorizer(), HistGradientBoostingRegressor()) - -results = cross_validate(pipeline, X, y) -scores = results["test_score"] -print(f"R2 score: mean: {np.mean(scores):.3f}; std: {np.std(scores):.3f}") -print(f"mean fit time: {np.mean(results['fit_time']):.3f} seconds") - -############################################################################### -# Specializing the TableVectorizer for HistGradientBoosting -# --------------------------------------------------------- -# -# The encoders used by default by the |TableVectorizer| are safe choices for a wide -# range of downstream estimators. If we know we want to use it with a |HGBR| (or -# classifier) model, we can make some different choices that are only well-suited for -# tree-based models but can yield a faster pipeline. -# We make 2 changes. -# -# The |HGBR| has built-in support for categorical features, so we do not need to one-hot -# encode them. -# We do need to tell it which features should be treated as categorical with the -# ``categorical_features`` parameter. In recent versions of scikit-learn, we can set -# ``categorical_features='from_dtype'``, and it will treat all columns in the input that -# have a ``Categorical`` dtype as such. Therefore we change the encoder for -# low-cardinality columns: instead of ``OneHotEncoder``, we use skrub's -# ``ToCategorical``. This transformer will simply ensure our columns have an actual -# ``Categorical`` dtype (as opposed to string for example), so that they can be -# recognized by the |HGBR|. -# -# The second change replaces the |GapEncoder| with a |MinHashEncoder|. -# The |GapEncoder| is a topic model. -# It produces interpretable embeddings in a vector space where distances are meaningful, -# which is great for interpretation and necessary for some downstream supervised -# learners such as linear models. However fitting the topic model is costly in -# computation time and memory. The |MinHashEncoder| produces features that are not easy -# to interpret, but that decision trees can efficiently use to test for the occurrence -# of particular character n-grams (more details are provided in its documentation). -# Therefore it can be a faster and very effective alternative, when the supervised -# learner is built on top of decision trees, which is the case for the |HGBR|. -# -# The resulting pipeline is identical to the one produced by default by -# :obj:`tabular_pipeline`. - -from skrub import MinHashEncoder, ToCategorical - -vectorizer = TableVectorizer( - low_cardinality=ToCategorical(), high_cardinality=MinHashEncoder() -) -pipeline = make_pipeline( - vectorizer, HistGradientBoostingRegressor(categorical_features="from_dtype") -) - -results = cross_validate(pipeline, X, y) -scores = results["test_score"] -print(f"R2 score: mean: {np.mean(scores):.3f}; std: {np.std(scores):.3f}") -print(f"mean fit time: {np.mean(results['fit_time']):.3f} seconds") - -############################################################################### -# We can see that this new pipeline achieves a similar score but is fitted much faster. -# This is mostly due to replacing |GapEncoder| with |MinHashEncoder| (however this makes -# the features less interpretable). - -############################################################################### -# Feature importances in the statistical model -# -------------------------------------------- -# -# As we just saw, we can fit a |MinHashEncoder| faster than a |GapEncoder|. However, the -# |GapEncoder| has a crucial advantage: each dimension of its output space is associated -# with a topic which can be inspected and interpreted. -# In this section, after training a regressor, we will plot the feature importances. -# -# .. topic:: Note: -# -# To minimize computation time, we use the feature importances computed by the -# |RandomForestRegressor|, but you should prefer |permutation importances| -# instead (which are less subject to biases). -# -# First, we train another scikit-learn regressor, the |RandomForestRegressor|: - -from sklearn.ensemble import RandomForestRegressor - -vectorizer = TableVectorizer() # now using the default GapEncoder -regressor = RandomForestRegressor(n_estimators=50, max_depth=20, random_state=0) - -pipeline = make_pipeline(vectorizer, regressor) -pipeline.fit(X, y) - -############################################################################### -# We are retrieving the feature importances: - -avg_importances = regressor.feature_importances_ -std_importances = np.std( - [tree.feature_importances_ for tree in regressor.estimators_], axis=0 -) -indices = np.argsort(avg_importances)[::-1] - -############################################################################### -# And plotting the results: - -import matplotlib.pyplot as plt - -top_indices = indices[:20] -labels = vectorizer.get_feature_names_out()[top_indices] - -plt.figure(figsize=(12, 9)) -plt.barh( - y=labels, - width=avg_importances[top_indices], - xerr=std_importances[top_indices], - ecolor="k", - color="b", - alpha=0.5, -) -plt.yticks(fontsize=15) -plt.title("Feature importances") -plt.tight_layout(pad=1) -plt.show() - -############################################################################### -# The |GapEncoder| creates feature names that show the first 3 most important words in -# the topic associated with each feature. As we can see in the plot above, this helps -# inspecting the model. If we had used a |MinHashEncoder| instead, the features would be -# much less helpful, with names such as ``employee_position_title_0``, -# ``employee_position_title_1``, etc. - -############################################################################### -# We can see that features such the time elapsed since being hired, having a full-time -# employment, and the position, seem to be the most informative for prediction. However, -# feature importances must not be over-interpreted -- they capture statistical -# associations `rather than causal effects -# `_. Moreover, the -# fast feature importance method used here suffers from biases favouring features with -# larger cardinality, as illustrated in a scikit-learn `example -# `_. -# In general we should prefer |permutation importances|, but it is a slower method. - -############################################################################### -# Conclusion -# ---------- -# -# In this example, we motivated the need for a simple machine learning -# pipeline, which we built using the |TableVectorizer| and a -# |HGBR|. -# -# We saw that by default, it works well on a heterogeneous dataset. -# -# To better understand our dataset, and without much effort, we were also able -# to plot the feature importances. diff --git a/examples/01_encoding/0020_text_with_string_encoders.py b/examples/01_encoding/0020_text_with_string_encoders.py deleted file mode 100644 index 2d530a5cd..000000000 --- a/examples/01_encoding/0020_text_with_string_encoders.py +++ /dev/null @@ -1,346 +0,0 @@ -""" -.. _example_string_encoders: - -===================================================== -Various string encoders: a sentiment analysis example -===================================================== - -In this example, we explore the performance of string and categorical encoders -available in skrub. - -.. |GapEncoder| replace:: - :class:`~skrub.GapEncoder` - -.. |MinHashEncoder| replace:: - :class:`~skrub.MinHashEncoder` - -.. |TextEncoder| replace:: - :class:`~skrub.TextEncoder` - -.. |StringEncoder| replace:: - :class:`~skrub.StringEncoder` - -.. |TableReport| replace:: - :class:`~skrub.TableReport` - -.. |TableVectorizer| replace:: - :class:`~skrub.TableVectorizer` - -.. |pipeline| replace:: - :class:`~sklearn.pipeline.Pipeline` - -.. |HistGradientBoostingClassifier| replace:: - :class:`~sklearn.ensemble.HistGradientBoostingClassifier` - -.. |RandomizedSearchCV| replace:: - :class:`~sklearn.model_selection.RandomizedSearchCV` - -.. |GridSearchCV| replace:: - :class:`~sklearn.model_selection.GridSearchCV` -""" - -# %% -# The Toxicity dataset -# -------------------- -# We focus on the toxicity dataset, a corpus of 1,000 tweets, evenly balanced -# between the binary labels "Toxic" and "Not Toxic". -# Our goal is to classify each entry between these two labels, using only the -# text of the tweets as features. -import pandas as pd - -from skrub.datasets import fetch_toxicity - -# %% -# We load the dataset from the path using pandas. -file_path = fetch_toxicity().path - -X = pd.read_csv(file_path) - -# %% -# When it comes to displaying large chunks of text, the |TableReport| is especially -# useful! Click on any cell below to expand and read the tweet in full. -from skrub import TableReport - -TableReport(X) - -# %% -# We prepare the target variable by mapping the binary labels "Toxic" and "Not Toxic" -# to 1 and 0, respectively. The target is reused throughout the example. - -y = X.pop("is_toxic").map({"Toxic": 1, "Not Toxic": 0}) - -# %% -# GapEncoder -# ^^^^^^^^^^ -# First, let's vectorize our text column using the |GapEncoder|, one of the -# `high cardinality categorical encoders `_ -# provided by skrub. -# As introduced in the :ref:`previous example`, the |GapEncoder| -# performs matrix factorization for topic modeling. It builds latent topics by -# capturing combinations of substrings that frequently co-occur, and encoded vectors -# correspond to topic activations. -# -# To interpret these latent topics, we select for each of them a few labels from -# the input data with the highest activations. In the example below we select 3 labels -# to summarize each topic. -from skrub import GapEncoder - -gap = GapEncoder(n_components=30) -X_trans = gap.fit_transform(X["text"]) -# Add the original text as a first column -X_trans.insert(0, "text", X["text"]) -TableReport(X_trans) - -# %% -# We can use a heatmap to highlight the highest activations, making them more visible -# for comparison against the original text and vectors above. - -import numpy as np -from matplotlib import pyplot as plt - - -def plot_gap_feature_importance(X_trans): - x_samples = X_trans.pop("text") - - # We slightly format the topics and labels for them to fit on the plot. - topic_labels = [x.replace("text: ", "") for x in X_trans.columns] - labels = x_samples.str[:50].values + "..." - - # We clip large outliers to make activations more visible. - X_trans = np.clip(X_trans, a_min=None, a_max=200) - - plt.figure(figsize=(10, 10), dpi=200) - plt.imshow(X_trans.T) - - plt.yticks( - range(len(topic_labels)), - labels=topic_labels, - ha="right", - size=12, - ) - plt.xticks(range(len(labels)), labels=labels, size=12, rotation=50, ha="right") - - plt.colorbar().set_label(label="Topic activations", size=13) - plt.ylabel("Latent topics", size=14) - plt.xlabel("Data entries", size=14) - plt.tight_layout() - plt.show() - - -plot_gap_feature_importance(X_trans.head()) - -# %% -# Now that we have an understanding of the vectors produced by the |GapEncoder|, -# let's evaluate its performance in toxicity classification. The |GapEncoder| excels -# at handling categorical columns with high cardinality, but here the column consists -# of free-form text. Sentences are generally longer, with more unique ngrams than -# high cardinality categories. -# -# To benchmark the performance of the |GapEncoder| against the toxicity dataset, -# we integrate it into a |TableVectorizer|, as introduced in the -# :ref:`previous example`, -# and create a |pipeline| by appending a |HistGradientBoostingClassifier|, which -# consumes the vectors produced by the |GapEncoder|. -# -# We set ``n_components`` to 30; however, to achieve the best performance, we would -# need to find the optimal value for this hyperparameter using either |GridSearchCV| -# or |RandomizedSearchCV|. We skip this part to keep the computation time for this -# small example. -# -# Recall that the ROC AUC is a metric that quantifies the ranking power of estimators, -# where a random estimator scores 0.5, and an oracle —providing perfect predictions— -# scores 1. -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.model_selection import cross_validate -from sklearn.pipeline import make_pipeline - -from skrub import TableVectorizer - - -def plot_box_results(named_results): - fig, ax = plt.subplots() - names, scores = zip( - *[(name, result["test_score"]) for name, result in named_results] - ) - ax.boxplot(scores) - ax.set_xticks(range(1, len(names) + 1), labels=list(names), size=12) - ax.set_ylabel("ROC AUC", size=14) - plt.title( - "AUC distribution across folds (higher is better)", - size=14, - ) - plt.show() - - -results = [] - -# %% -# Now we can evaluate the performance of the |GapEncoder| in toxicity classification. - -gap_pipe = make_pipeline( - TableVectorizer(high_cardinality=GapEncoder(n_components=30)), - HistGradientBoostingClassifier(), -) -gap_results = cross_validate(gap_pipe, X, y, scoring="roc_auc") -results.append(("GapEncoder", gap_results)) - -plot_box_results(results) - -# %% -# MinHashEncoder -# ^^^^^^^^^^^^^^ -# We now compare these results with the |MinHashEncoder|, which is faster -# and produces vectors better suited for tree-based estimators like -# |HistGradientBoostingClassifier|. To do this, we can simply replace -# the |GapEncoder| with the |MinHashEncoder| in the previous pipeline -# using ``set_params()``. - -from skrub import MinHashEncoder - -minhash_pipe = make_pipeline( - TableVectorizer(high_cardinality=MinHashEncoder(n_components=30)), - HistGradientBoostingClassifier(), -) -minhash_results = cross_validate(minhash_pipe, X, y, scoring="roc_auc") -results.append(("MinHashEncoder", minhash_results)) - -plot_box_results(results) - -# %% -# Remarkably, the vectors produced by the |MinHashEncoder| offer less predictive -# power than those from the |GapEncoder| on this dataset. -# -# TextEncoder -# ^^^^^^^^^^^ -# Let's now shift our focus to pre-trained deep learning encoders. Our previous -# encoders are syntactic models that we trained directly on the toxicity dataset. -# To generate more powerful vector representations for free-form text and diverse -# entries, we can instead use semantic models, such as BERT, which have been trained -# on very large datasets. -# -# |TextEncoder| enables you to integrate any Sentence Transformer model from the -# Hugging Face Hub (or from your local disk) into your |pipeline| to transform a text -# column in a dataframe. By default, |TextEncoder| uses the e5-small-v2 model. -from skrub import TextEncoder - -text_encoder = TextEncoder( - "sentence-transformers/paraphrase-albert-small-v2", - device="cpu", -) - -text_encoder_pipe = make_pipeline( - TableVectorizer(high_cardinality=text_encoder), - HistGradientBoostingClassifier(), -) -text_encoder_results = cross_validate(text_encoder_pipe, X, y, scoring="roc_auc") -results.append(("TextEncoder", text_encoder_results)) - -plot_box_results(results) - -# %% -# StringEncoder -# ^^^^^^^^^^^^^ -# |TextEncoder| embeddings are very strong, but they are also quite expensive to -# use. A simpler, faster alternative for encoding strings is the |StringEncoder|, -# which works by first performing a tf-idf (computing vectors of rescaled word -# counts of the text `wiki `_), and then -# following it with TruncatedSVD to reduce the number of dimensions to, in this -# case, 30. -from skrub import StringEncoder - -string_encoder = StringEncoder(ngram_range=(3, 4), analyzer="char_wb", random_state=0) - -string_encoder_pipe = make_pipeline( - TableVectorizer(high_cardinality=string_encoder), - HistGradientBoostingClassifier(), -) - -string_encoder_results = cross_validate(string_encoder_pipe, X, y, scoring="roc_auc") -results.append(("StringEncoder", string_encoder_results)) - -plot_box_results(results) - - -# %% -# The performance of the |TextEncoder| is significantly stronger than that of -# the syntactic encoders, which is expected. But how long does it take to load -# and vectorize text on a CPU using a Sentence Transformer model? Below, we display -# the tradeoff between predictive accuracy and training time. Note that since we are -# not training the Sentence Transformer model, the "fitting time" refers to the -# time taken for vectorization. - - -def plot_performance_tradeoff(results): - fig, ax = plt.subplots(figsize=(5, 4), dpi=200) - markers = ["s", "o", "^", "x"] - for idx, (name, result) in enumerate(results): - ax.scatter( - result["fit_time"], - result["test_score"], - label=name, - marker=markers[idx], - ) - mean_fit_time = np.mean(result["fit_time"]) - mean_score = np.mean(result["test_score"]) - ax.scatter( - mean_fit_time, - mean_score, - color="k", - marker=markers[idx], - ) - std_fit_time = np.std(result["fit_time"]) - std_score = np.std(result["test_score"]) - ax.errorbar( - x=mean_fit_time, - y=mean_score, - yerr=std_score, - fmt="none", - c="k", - capsize=2, - ) - ax.errorbar( - x=mean_fit_time, - y=mean_score, - xerr=std_fit_time, - fmt="none", - c="k", - capsize=2, - ) - ax.set_xscale("log") - - ax.set_xlabel("Time to fit (seconds)") - ax.set_ylabel("ROC AUC") - ax.set_title("Prediction performance / training time trade-off") - - ax.annotate( - "Best time / \nperformance trade-off", - xy=(0.05, 0.95), - xycoords="axes fraction", - xytext=(0.2, 0.8), - textcoords="axes fraction", - arrowprops=dict(arrowstyle="->", lw=1.5, mutation_scale=15), - ) - ax.legend(bbox_to_anchor=(1.02, 0.3)) - plt.show() - - -plot_performance_tradeoff(results) - -# %% -# The black points represent the average time to fit and AUC for each vectorizer, -# and the width of the bars represents one standard deviation. -# -# The green outlier dot on the right side of the plot corresponds to the first time -# the Sentence Transformers model was downloaded and loaded into memory. -# During the subsequent cross-validation iterations, the model is simply copied, -# which reduces computation time for the remaining folds. -# -# Interestingly, |StringEncoder| has a performance remarkably similar to that of -# |GapEncoder|, while being significantly faster. -# -# Conclusion -# ---------- -# In conclusion, |TextEncoder| provides powerful vectorization for text, but at -# the cost of longer computation times and the need for additional dependencies, -# such as torch. |StringEncoder| represents a simpler alternative that can provide -# good performance at a fraction of the cost of more complex methods. diff --git a/examples/01_encoding/0030_datetime_encoder.py b/examples/01_encoding/0030_datetime_encoder.py deleted file mode 100644 index 1b339ac51..000000000 --- a/examples/01_encoding/0030_datetime_encoder.py +++ /dev/null @@ -1,355 +0,0 @@ -""" -.. _example_datetime_encoder : - -=================================================== -Handling datetime features with the DatetimeEncoder -=================================================== - -In this example, we illustrate how to better integrate datetime features -in machine learning models with the |DatetimeEncoder|. - -This encoder breaks down passed datetime features into relevant numerical -features, such as the month, the day of the week, the hour of the day, etc. - -It is used by default in the |TableVectorizer|. - - -.. |DatetimeEncoder| replace:: - :class:`~skrub.DatetimeEncoder` - -.. |TableVectorizer| replace:: - :class:`~skrub.TableVectorizer` - -.. |OneHotEncoder| replace:: - :class:`~sklearn.preprocessing.OneHotEncoder` - -.. |TimeSeriesSplit| replace:: - :class:`~sklearn.model_selection.TimeSeriesSplit` - -.. |ColumnTransformer| replace:: - :class:`~sklearn.compose.ColumnTransformer` - -.. |make_column_transformer| replace:: - :class:`~sklearn.compose.make_column_transformer` - -.. |RidgeCV| replace:: - :class:`~sklearn.linear_model.RidgeCV` - -.. |SimpleImputer| replace:: - :class:`~sklearn.impute.SimpleImputer` - -.. |StandardScaler| replace:: - :class:`~sklearn.preprocessing.StandardScaler` - -.. |ToDatetime| replace:: - :class:`~skrub.ToDatetime` -""" - -# %% -# A problem with relevant datetime features -# ----------------------------------------- -# -# We will use a dataset of bike sharing demand in 2011 and 2012. -# In this setting, we want to predict the number of bike rentals, based -# on the date, time and weather conditions. - -from pprint import pprint - -import pandas as pd - -from skrub import datasets - -file_path = datasets.fetch_bike_sharing().path -data = pd.read_csv(file_path) - -# Extract our input data (X) and the target column (y) -y = data["cnt"] -X = data[["date", "holiday", "temp", "hum", "windspeed", "weathersit"]] - -X - -# %% -y - -############################################################################### -# We convert the dataframe's ``"date"`` column using |ToDatetime|. - -from skrub import ToDatetime - -date = ToDatetime().fit_transform(X["date"]) - -print("original dtype:", X["date"].dtypes, "\n\nconverted dtype:", date.dtypes) - -############################################################################### -# Encoding the features -# ..................... -# -# We now encode this column with a |DatetimeEncoder|. -# -# During the instantiation of the |DatetimeEncoder|, we specify that we want -# don't want to extract features with a resolution finer than hours. This is -# because we don't want to extract minutes, seconds and lower units, as they -# are unimportant here. - -from skrub import DatetimeEncoder - -# DatetimeEncoder has "hour" as default resolution -date_enc = DatetimeEncoder().fit_transform(date) - -print(date, "\n\nHas been encoded as:\n\n", date_enc) - -############################################################################### -# We see that the encoder is working as expected: the column has -# been replaced by features extracting the month, day, hour, day of the -# week and total seconds since Epoch information. - -############################################################################### -# One-liner with the TableVectorizer -# .................................. -# -# As mentioned earlier, the |TableVectorizer| makes use of the -# |DatetimeEncoder| by default. Note that ``X["date"]`` is still -# a string, but will be automatically transformed into a datetime in the -# |TableVectorizer|. - -from skrub import TableVectorizer - -table_vec = TableVectorizer().fit(X) -pprint(table_vec.get_feature_names_out()) - -############################################################################### -# If we want to customize the |DatetimeEncoder| inside the |TableVectorizer|, -# we can replace its default parameter with a new, custom instance. -# -# Here, for example, we want it to extract the day of the week: - -# use the ``datetime`` argument to use a custom DatetimeEncoder in the TableVectorizer -table_vec_weekday = TableVectorizer(datetime=DatetimeEncoder(add_weekday=True)).fit(X) -pprint(table_vec_weekday.get_feature_names_out()) - -############################################################################### -# .. note: -# For more information on how to customize the |TableVectorizer|, see -# :ref:`sphx_glr_auto_examples_0010_dirty_categories.py`. -# -# Inspecting the |TableVectorizer| further, we can check that the -# |DatetimeEncoder| is used on the correct column(s). -pprint(table_vec_weekday.transformers_) - -############################################################################### -# -# Feature engineering for linear models -# .................................................................... -# -# The |DatetimeEncoder| can generate additional periodic features. These are -# particularly useful for linear models. This is controlled by the -# ``periodic encoding`` parameter which can be either ``circular`` or ``spline``, -# for trigonometric functions or B-Splines respectively. In this example, we use -# ``spline``. -# We can also add the day in the year with the parameter ``add_day_of_year``. - -table_vec_periodic = TableVectorizer( - datetime=DatetimeEncoder( - add_weekday=True, periodic_encoding="spline", add_day_of_year=True - ) -).fit(X) - -############################################################################### -# Prediction with datetime features -# --------------------------------- -# -# For prediction tasks, we recommend using the |TableVectorizer| inside a -# pipeline, combined with a model that can use the features extracted by the -# |DatetimeEncoder|. -# Here we'll use a |RidgeCV| model as our learner. We also fill null values with -# |SimpleImputer| and then rescale numeric features with |StandardScaler|. -# To test the effect of different datetime encodings on the linear model, we train -# three separate pipelines. - -from sklearn.impute import SimpleImputer -from sklearn.linear_model import RidgeCV -from sklearn.pipeline import make_pipeline -from sklearn.preprocessing import StandardScaler - -# Base pipeline with default DatetimeEncoder parameters -pipeline = make_pipeline(table_vec, StandardScaler(), SimpleImputer(), RidgeCV()) -# Datetime encoder with weekday feature -pipeline_weekday = make_pipeline( - table_vec_weekday, StandardScaler(), SimpleImputer(), RidgeCV() -) -# Datetime encoder with periodic features -pipeline_periodic = make_pipeline( - table_vec_periodic, StandardScaler(), SimpleImputer(), RidgeCV() -) - -############################################################################### -# Evaluating the model -# .................... -# -# When using date and time features, we often care about predicting the future. -# In this case, we have to be careful when evaluating our model, because -# the standard settings of the cross-validation do not respect time ordering. -# -# Instead, we can use the |TimeSeriesSplit|, -# which ensures that the test set is always in the future. -from sklearn.model_selection import TimeSeriesSplit, cross_val_score - -score_base = cross_val_score( - pipeline, - X, - y, - scoring="neg_root_mean_squared_error", - cv=TimeSeriesSplit(n_splits=5), -) - -score_weekday = cross_val_score( - pipeline_weekday, - X, - y, - scoring="neg_root_mean_squared_error", - cv=TimeSeriesSplit(n_splits=5), -) - -score_periodic = cross_val_score( - pipeline_periodic, - X, - y, - scoring="neg_root_mean_squared_error", - cv=TimeSeriesSplit(n_splits=5), -) - -print(f"Base transformer - Mean RMSE : {-score_base.mean():.2f}") -print(f"Transformer with weekday - Mean RMSE : {-score_weekday.mean():.2f}") -print(f"Transformer with periodic features - Mean RMSE : {-score_periodic.mean():.2f}") - -############################################################################### -# As expected for linear models, introducing the periodic features improved -# the RMSE by a noticeable amount. - -############################################################################### -# Plotting the prediction -# ....................... -# -# The mean squared error is not obvious to interpret, so we visually -# compare the prediction of our model with the actual values. -# To do so, we will divide our dataset into a train and a test set: -# we use 2011 data to predict what happened in 2012. -import matplotlib.dates as mdates -import matplotlib.pyplot as plt - -mask_train = X["date"] < "2012-01-01" -X_train, X_test = X.loc[mask_train], X.loc[~mask_train] -y_train, y_test = y.loc[mask_train], y.loc[~mask_train] - -pipeline.fit(X_train, y_train) -y_pred = pipeline.predict(X_test) - -pipeline_weekday.fit(X_train, y_train) -y_pred_weekday = pipeline_weekday.predict(X_test) - -pipeline_periodic.fit(X_train, y_train) -y_pred_periodic = pipeline_periodic.predict(X_test) - -X_plot = pd.to_datetime(X.tail(96)["date"]).values -X_test_plot = pd.to_datetime(X_test.tail(96)["date"]).values - -fig, ax = plt.subplots(figsize=(12, 3)) -fig.suptitle("Predictions with linear models") -ax.plot( - X_plot, - y.tail(96).values, - "x-", - alpha=0.2, - label="Actual demand", - color="black", -) -ax.plot( - X_test_plot, - y_pred[-96:], - "x-", - label="DatetimeEncoder() + RidgeCV prediction", -) -ax.plot( - X_test_plot, - y_pred_periodic[-96:], - "x-", - label='DatetimeEncoder(periodic_encoding="spline") + RidgeCV prediction', -) - - -ax.xaxis.set_major_locator(mdates.DayLocator()) -ax.xaxis.set_minor_locator( - mdates.HourLocator( - [0, 6, 12, 18], - ) -) - -# Major formatter: format date as "YYYY-MM-DD" -ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d")) -# # Minor formatter: format time as "HH:MM" -ax.xaxis.set_minor_formatter(mdates.DateFormatter("%H:%M")) - -ax.tick_params(axis="x", labelsize=7, labelrotation=75) -ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m-%d")) -_ = fig.legend(loc="upper left") -plt.tight_layout() -plt.show() -# %% -############################################################################### -# As we can see, the base RidgeCV model struggles to learn the the pattern well -# enough, while the model that is trained on the additional periodic features -# follows the actual demand more accurately. - - -############################################################################### -# Feature importances -# ------------------- -# -# Using the |DatetimeEncoder| allows us to better understand how the date -# impacts the bike sharing demand. To this aim, we can use the function -# :func:`~sklearn.inspection.permutation_importance` to shuffle the features -# created by the |DatetimeEncoder| and measure their importance by observing -# how the model changes its prediction. - - -############################################################################### -from sklearn.inspection import permutation_importance - -# In this case, we don't use the whole pipeline, because we want to compute the -# importance of the features created by the DatetimeEncoder -X_test_transform = pipeline_periodic[:-1].transform(X_test) -result = permutation_importance( - pipeline_periodic[-1], X_test_transform, y_test, n_repeats=10, random_state=0 -) - -result = pd.DataFrame( - dict( - feature_names=pipeline_periodic[0].all_outputs_, - std=result.importances_std, - importances=result.importances_mean, - ) -).sort_values("importances", ascending=True) - -result.plot.barh( - y="importances", - x="feature_names", - title="Feature Importances", - xerr="std", - figsize=(12, 9), -) -plt.tight_layout() -plt.show() - -# %% -# We can clearly see that some of the hour splines (``date_hour_spline_18``, -# ``date_hour_spline_9``) are more important than other features, likely due to -# the fact that they match rush hours in the day. Other features, such as the -# temperature, the month, and the humidity are more important than others. -# -# Conclusion -# ---------- -# -# In this example, we saw how to use the |DatetimeEncoder| to create -# features from a datetime column. -# Also check out the |TableVectorizer|, which automatically recognizes -# and transforms datetime columns by default. diff --git a/examples/01_encoding/GALLERY_HEADER.rst b/examples/01_encoding/GALLERY_HEADER.rst deleted file mode 100644 index d79d44f0a..000000000 --- a/examples/01_encoding/GALLERY_HEADER.rst +++ /dev/null @@ -1,2 +0,0 @@ -Encoding features -================= diff --git a/examples/02_data_ops/1120_multiple_tables.py b/examples/02_data_ops/1120_multiple_tables.py deleted file mode 100644 index cb035b11f..000000000 --- a/examples/02_data_ops/1120_multiple_tables.py +++ /dev/null @@ -1,246 +0,0 @@ -""" -Multiples tables: building machine learning pipelines with DataOps -================================================================== - -In this example, we show how to build a DataOps plan to handle -pre-processing, validation and hyperparameter tuning of a dataset with **multiple -tables**. - -We consider the credit fraud dataset, which contains two tables: one for -baskets (orders) and one for products. The goal is to predict whether a basket -(a single order that has been placed with the website) is fraudulent or not, -based on the products it contains. - -.. currentmodule:: skrub - -.. |choose_from| replace:: :func:`skrub.choose_from` -.. |choose_int| replace:: :func:`skrub.choose_int` -.. |choose_float| replace:: :func:`skrub.choose_float` -.. |MinHashEncoder| replace:: :class:`~skrub.MinHashEncoder` -.. |StringEncoder| replace:: :class:`~skrub.StringEncoder` -.. |TableVectorizer| replace:: :class:`~skrub.TableVectorizer` -.. |var| replace:: :func:`skrub.var` -.. |TableReport| replace:: :class:`~skrub.TableReport` -.. |HistGradientBoostingClassifier| replace:: - :class:`~sklearn.ensemble.HistGradientBoostingClassifier` -.. |make_randomized_search| replace:: :func:`~skrub.DataOp.skb.make_randomized_search` -.. |RocCurveDisplay| replace:: :class:`~sklearn.metrics.RocCurveDisplay` - - -""" - -# %% -# The credit fraud dataset -# ------------------------ -# -# We fetch the credit fraud dataset using ``fetch_credit_fraud``. This dataset -# contains two tables: ``baskets`` and ``products``. We load the training split -# of the dataset to train the model. At the end of the example, we will load -# the test split to evaluate the model on unseen data. - -# %% -import pandas as pd - -import skrub -import skrub.datasets - -# Small display detail: open the graphs by default in the visualizations shown -# in the rest of this notebook. -skrub.set_config(data_ops_open_graph_dropdown=True) - -dataset = skrub.datasets.fetch_credit_fraud(split="train") - -# %% -# We define two skrub variables that store the content of the two csv -# files. These variables will be used as inputs to the DataOps plan we will build. -# Later, when we want to apply the resulting model to new data, we will need to -# provide dataframes to the same variables, but with the content of the test split -# of the dataset instead. -baskets = skrub.var("baskets", pd.read_csv(dataset.baskets_path)) -products = skrub.var("products", pd.read_csv(dataset.products_path)) - -# %% -# Now we can use the |TableReport| provided by the Data Ops to inspect the two tables. -# The ``baskets`` table contains the list of basket IDs, and a fraud flag indicating -# whether the basket is fraudulent or not. -baskets -# %% -# We mark the "ID" column of the ``baskets`` table as ``X``, and the -# ``"fraud_flag"`` column as ``y``. This allows the Data Ops to track the indices -# of the variables when splitting for cross-validation. -# so that DataOps can use their indices for train-test splitting and cross-validation. -basket_ids = baskets[["ID"]].skb.mark_as_X() -fraud_flags = baskets["fraud_flag"].skb.mark_as_y() -# %% -# The ``products`` table contains information about the products that have been -# purchased, and the basket they belong to. A basket contains at least one product. -# Products can be associated with the corresponding basket through the "basket_ID" -# column. - -# %% -products -# %% -# A data-processing challenge -# ---------------------------- -# The general structure of the DataOps plan we want to build looks like this: -# -# .. image:: ../../_static/credit_fraud_diagram.svg -# :width: 300 -# -# We want to fit a |HistGradientBoostingClassifier| to predict the fraud -# flag (y). However, since the features for each basket are stored in -# the products table, we need to extract these features, aggregate them -# at the basket level, and merge the result with the basket data. -# -# .. admonition:: Why building a pipeline for this is hard -# :collapsible: closed -# -# We can use the |TableVectorizer| to vectorize the products, but we -# then need to aggregate the resulting vectors to obtain a single row per basket. -# Using a scikit-learn Pipeline is tricky because the |TableVectorizer| would be -# fitted on a table with a different number of rows than the target y (the baskets -# table), which scikit-learn does not allow. -# -# While we could fit the |TableVectorizer| manually, this would forfeit -# scikit-learn’s tooling for managing transformations, storing fitted estimators, -# splitting data, cross-validation, and hyper-parameter tuning. -# We would also have to handle the aggregation and join ourselves, likely with -# error-prone Pandas code. -# -# Fortunately, skrub DataOps provide a powerful alternative for building flexible -# plans that address these problems. - -# %% -# Building a multi-table DataOps plan -# ------------------------------------ -# Since our DataOps expect dataframes for products, baskets and fraud -# flags, we manipulate those objects as we would manipulate pandas dataframes. -# For instance, we filter products to keep only those that match one of the -# baskets in the ``baskets`` table, and then add a column containing the total -# amount for each kind of product in a basket: -# %% -kept_products = products[products["basket_ID"].isin(basket_ids["ID"])] -products_with_total = kept_products.assign( - total_price=kept_products["Nbr_of_prod_purchas"] * kept_products["cash_price"] -) -products_with_total - -# %% -# We then build a skrub |TableVectorizer| with different choices of -# the type of encoder for high-cardinality categorical or string columns, and -# the number of components it uses. -# -# With skrub, there’s no need to specify a separate grid of hyperparameters outside -# the pipeline. -# Instead, within a DataOps plan, we can directly replace a parameter’s value using -# one of skrub’s ``choose_*`` functions, which define the range of values to consider -# during hyperparameter selection. In this example, we use |choose_int| to select -# the number of components for the encoder and |choose_from| to select the type -# of encoder. - -# %% -n = skrub.choose_int(5, 15, name="n_components") -encoder = skrub.choose_from( - { - "MinHash": skrub.MinHashEncoder(n_components=n), - "LSA": skrub.StringEncoder(n_components=n), - }, - name="encoder", -) -vectorizer = skrub.TableVectorizer(high_cardinality=encoder) - -# %% -# We can restrict the vectorizer to a subset of columns: in our case, we want to -# vectorize all columns except the ``"basket_ID"`` column, which is not a -# feature but a link to the basket it belongs to. - -# %% -vectorized_products = products_with_total.skb.apply( - vectorizer, exclude_cols="basket_ID" -) - -# %% -# We then aggregate the vectorized products by basket ID, and then merge the result -# with the baskets table. - -# %% -aggregated_products = vectorized_products.groupby("basket_ID").agg("mean").reset_index() -augmented_baskets = basket_ids.merge( - aggregated_products, left_on="ID", right_on="basket_ID" -).drop(columns=["ID", "basket_ID"]) - -# %% -# Finally, we add a supervised estimator, and use |choose_float| to -# add the learning rate as a hyperparameter to tune. - -# %% -from sklearn.ensemble import HistGradientBoostingClassifier - -hgb = HistGradientBoostingClassifier( - learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="learning_rate") -) -predictions = augmented_baskets.skb.apply(hgb, y=fraud_flags) -predictions - -# %% -# And our DataOps plan is complete! -# -# We can now use |make_randomized_search| to perform hyperparameter -# tuning and find the best hyperparameters for our model. Below, we display the -# hyperparameter combinations that define our search space. - -# %% -print(predictions.skb.describe_param_grid()) - -# %% -# |make_randomized_search| returns a :class:`~skrub.ParamSearch` object, which contains -# our search result and some plotting logic. -search = predictions.skb.make_randomized_search( - scoring="roc_auc", n_iter=8, n_jobs=4, random_state=0, fitted=True -) -search.results_ - -# %% -# We can also display the results of the search in a parallel coordinates plot: -search.plot_results() - -# %% -# It seems here that using the LSA as an encoder brings better test scores, -# but at the expense of training and scoring time. -# -# We can get the best performing :class:`~skrub.SkrubLearner` via -# ``best_learner_``, and use it for inference on new data. -# We load the test split of the credit fraud dataset, and apply the best learner to -# it to obtain predictions. - -new_data = skrub.datasets.fetch_credit_fraud(split="test") - -new_baskets = pd.read_csv(new_data.baskets_path) -new_products = pd.read_csv(new_data.products_path) - -probabilities = search.best_learner_.predict_proba( - {"baskets": new_baskets, "products": new_products} -) -# %% -# We can evaluate the performance of our model by plotting the ROC curve and -# calculating the AUC score. -# We can use the |RocCurveDisplay| from scikit-learn to plot the ROC curve. - -import matplotlib.pyplot as plt -from sklearn.metrics import RocCurveDisplay - -RocCurveDisplay.from_predictions(new_baskets["fraud_flag"], probabilities[:, 1]) -plt.show() -# %% -# Conclusion -# ---------- -# -# In this example, we have shown how to build a multi-table machine learning -# pipeline with skrub DataOps. We have seen how DataOps allow us to use familiar -# Pandas operations to manipulate dataframes, and how we can build a DataOps plan -# that works with multiple tables and performs hyperparameter tuning on the -# resulting pipeline. -# -# If you want to learn more about tuning hyperparameters using skrub DataOps, see -# the :ref:`Tuning Pipelines example ` for an -# in-depth tutorial. diff --git a/examples/02_data_ops/1130_choices.py b/examples/02_data_ops/1130_choices.py deleted file mode 100644 index 550b4981e..000000000 --- a/examples/02_data_ops/1130_choices.py +++ /dev/null @@ -1,284 +0,0 @@ -""" - -.. currentmodule:: skrub - -.. _example_tuning_pipelines: - -Hyperparameter tuning with DataOps -================================== - -A machine-learning pipeline typically contains values or choices which -may influence its prediction performance, such as hyperparameters (e.g., the -regularization parameter ``alpha`` of a :class:`~sklearn.linear_model.RidgeClassifier`, -the ``learning_rate`` of a :class:`~sklearn.ensemble.HistGradientBoostingClassifier`), -which estimator to use (e.g., ``RidgeClassifier`` or -``HistGradientBoostingClassifier``), -or which steps to include (e.g., should we join a table to bring additional information -or not). - -We want to tune these choices by trying several options and keeping those that -give the best performance on a validation set. - -Skrub :ref:`DataOps ` provide a convenient way to specify -the range of possible values by inserting them directly in place of the actual -value. For example, we can write: -""" - -# %% -from sklearn.linear_model import RidgeClassifier - -import skrub - -RidgeClassifier(alpha=skrub.choose_from([0.1, 1.0, 10.0], name="α")) - -# %% -# instead of: - -RidgeClassifier(alpha=1.0) - -# %% -# Skrub then inspects our DataOps plan to discover all the places where we used objects -# like :func:`~skrub.choose_from()` and builds a grid of hyperparameters for us. -# -# We will illustrate hyperparameter tuning on the "toxicity" dataset. This -# dataset contains 1,000 texts and the task is to predict if they are -# flagged as being toxic or not. -# -# We start from a very simple pipeline without any hyperparameters. - -# %% -import pandas as pd -from sklearn.ensemble import HistGradientBoostingClassifier - -import skrub -import skrub.datasets - -file_path = skrub.datasets.fetch_toxicity().path -data = pd.read_csv(file_path) - -# This dataset is sorted -- all toxic tweets appear first, so we shuffle it -data = data.sample(frac=1.0, random_state=1) - -texts = data[["text"]] -labels = data["is_toxic"] - -# %% -# We mark the ``texts`` column as the input variable and the ``labels`` column as -# the target variable. -# -# See `the previous example <1110_data_ops_intro.html>`_ -# for a more detailed explanation -# of :func:`skrub.X` and :func:`skrub.y`. -# -# We then encode the text with a :class:`~skrub.MinHashEncoder` and fit a -# :class:`~sklearn.ensemble.HistGradientBoostingClassifier` on the resulting features. - -# %% -X = skrub.X(texts) -X - -# %% -y = skrub.y(labels) -y - -# %% -pred = X.skb.apply(skrub.MinHashEncoder()).skb.apply( - HistGradientBoostingClassifier(), y=y -) -pred.skb.cross_validate(n_jobs=4)["test_score"] - -# %% -# In this example, we will focus on the ``n_components`` of the -# ``MinHashEncoder`` and the ``learning_rate`` of the ``HistGradientBoostingClassifier`` -# to illustrate the choices objects. -# -# When we use a scikit-learn hyperparameter-tuner like -# :class:`~sklearn.model_selection.GridSearchCV` or -# :class:`~sklearn.model_selection.RandomizedSearchCV`, we need to specify a grid of -# hyperparameters separately from the estimator, with something similar to -# ``GridSearchCV(my_pipeline, param_grid={"encoder__n_components: [5, 10, 20]"})``. -# -# Instead, within a skrub DataOps plan we can use -# ``skrub.choose_from(...)`` directly where the actual value -# would normally go. Skrub then takes care of constructing the -# :class:`~sklearn.model_selection.GridSearchCV`'s parameter grid for us. -# -# Note that :func:`skrub.choose_float()` and :func:`skrub.choose_int()` can be given a -# ``log`` argument to sample in log scale, and that it is possible to specify the -# number of steps with the ``n_steps`` argument. - -# %% -X, y = skrub.X(texts), skrub.y(labels) - -encoder = skrub.MinHashEncoder( - n_components=skrub.choose_int(5, 15, n_steps=5, name="N components") -) -classifier = HistGradientBoostingClassifier( - learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="lr") -) -pred = X.skb.apply(encoder).skb.apply(classifier, y=y) - -# %% -# From here, the ``pred`` DataOp can be used to perform hyperparameter search with -# ``.skb.make_grid_search()`` or ``.skb.make_randomized_search()``. They accept -# the same arguments as their scikit-learn counterparts (e.g., ``scoring``, ``cv``, -# ``n_jobs``). Also, like ``.skb.make_learner()``, they accept a ``fitted`` -# argument: if ``fitted=True``, the search is fitted on the data we provided -# when initializing our pipeline's variables. - -search = pred.skb.make_randomized_search( - n_iter=8, n_jobs=4, random_state=1, fitted=True -) -search.results_ - -# %% -# If the plotly library is installed, we can visualize the results of the -# hyperparameter search with :func:`~skrub.ParamSearch.plot_results`. -# In the plot below, each line represents a combination of hyperparameters (in -# this case, only ``N components`` and ``learning rate``), and each column of -# points represents either a hyperparameter or the score of a given -# combination of hyperparameters. -# -# The color of the line represents the score of the combination of hyperparameters. -# The plot is interactive, and you can select only a subset of the -# hyperparameters to visualize by dragging the mouse over each column to select -# the desired range. -# -# This is particularly useful when there are many combinations of hyperparameters, -# and we want to understand which hyperparameters have the largest -# impact on the score. - -search.plot_results() -# %% -# Finally, we can retrieve the best learner from the search results, and save it -# to disk. This learner will contain the best hyperparameter configuration -# found during the search, and can be used to make predictions on new data. - -import pickle - -best_learner = search.best_learner_ -saved_model = pickle.dumps(best_learner) - -# %% -# Default choice values -# --------------------- -# -# The goal of using the different ``choose_*`` functions is to tune choices on -# validation metrics with randomized or grid search. However, even when our -# expression contains such choices we can still use it without tuning, for -# example in previews or to get a quick first result before spending the -# computation time to run the search. When we use :meth:`.skb.make_learner() -# `, we get a pipeline that does not perform any tuning -# and uses those default values. This default pipeline is used for -# :meth:`.skb.eval() `. -# -# We can control what should be the default value for each choice. For -# :func:`choose_int`, :func:`choose_float` and :func:`choose_bool`, we can use -# the ``default`` parameter. For :func:`choose_from`, the default is the first -# item from the list or dict of outcomes we provide. For :func:`optional`, we -# can pass ``default=None`` to force the default to be the alternative -# outcome, ``None``. -# -# When we do not set an explicit default, skrub picks one for depending on the -# kind of choice, as detailed in :ref:`this table` in the -# User Guide. - -# %% -# As mentioned we can control the default value: - -# %% -skrub.choose_float(1.0, 100.0, default=12.0).default() - -# %% -# Choices can appear in many places -# --------------------------------- -# -# Choices are not limited to selecting estimator hyperparameters. They can also be -# used to choose between different estimators, or in place of any value used in -# our pipeline. -# -# For example, here we pass a choice to pandas DataFrame's ``assign`` method. -# We want to add a feature that captures the length of the text, but we are not -# sure if it is better to count length in characters or in words. We do not -# want to add both because it would be redundant. We can add a column to the -# dataframe, which will be chosen among the length in characters or the length -# in words: - -# %% -X, y = skrub.X(texts), skrub.y(labels) - -X.assign( - length=skrub.choose_from( - {"words": X["text"].str.count(r"\b\w+\b"), "chars": X["text"].str.len()}, - name="length", - ) -) - -# %% -# ``choose_from`` can be given a dictionary if we want to provide -# names for the individual outcomes, or a list, when names are not needed: -# ``choose_from([1, 100], name='N')``, -# ``choose_from({'small': 1, 'big': 100}, name='N')``. -# -# Choices can be nested arbitrarily. For example, here we want to choose -# between 2 possible encoder types: the ``MinHashEncoder`` or the -# ``StringEncoder``. Each of the possible outcomes contains a choice itself: -# the number of components. - -# %% -X, y = skrub.X(texts), skrub.y(labels) - -n_components = skrub.choose_int(5, 15, name="N components") - -encoder = skrub.choose_from( - { - "minhash": skrub.MinHashEncoder(n_components=n_components), - "lse": skrub.StringEncoder(n_components=n_components), - }, - name="encoder", -) -X.skb.apply(encoder, cols="text") - -# %% -# In a similar vein, we might want to choose between a HistGradientBoostingClassifier -# and a Ridge classifier, each with its own set of hyperparameters. -# We can then define a choice for the classifier and a choice for the -# hyperparameters of each classifier. - -# %% -from sklearn.linear_model import RidgeClassifier - -hgb = HistGradientBoostingClassifier( - learning_rate=skrub.choose_float(0.01, 0.9, log=True, name="lr") -) -ridge = RidgeClassifier(alpha=skrub.choose_float(0.01, 100, log=True, name="α")) -classifier = skrub.choose_from({"hgb": hgb, "ridge": ridge}, name="classifier") -pred = X.skb.apply(encoder).skb.apply(classifier, y=y) -print(pred.skb.describe_param_grid()) - -# %% -search = pred.skb.make_randomized_search( - n_iter=16, n_jobs=4, random_state=1, fitted=True -) -search.plot_results() - -# %% -# Now that we have a more complex plan, we can draw more conclusions from the -# parallel coordinate plot. For example, we can see that the -# ``HistGradientBoostingClassifier`` -# performs better than the ``RidgeClassifier`` in most cases, that the ``StringEncoder`` -# outperforms the ``MinHashEncoder``, and that the choice of the additional ``length`` -# feature does not have a significant impact on the score. - -# %% -# In this example, we've seen how to use skrub's ``choose_from`` objects to tune -# hyperparameters, choose optional configurations, and nest choices. We then -# examined how different choices affect the plan and prediction scores. -# -# There is more to learn about skrub choices than what is covered here. -# In particular, choices are not limited to choosing estimators and -# their hyperparameters: they can be used anywhere DataOps are used, -# such as the argument of a :func:`deferred` function, or the argument of -# other DataOps' methods or operators. Additionally, choices can be -# inter-dependent. Find more information in the :ref:`user guide -# `. diff --git a/examples/02_data_ops/1131_optuna_choices.py b/examples/02_data_ops/1131_optuna_choices.py deleted file mode 100644 index 6d61f9674..000000000 --- a/examples/02_data_ops/1131_optuna_choices.py +++ /dev/null @@ -1,188 +0,0 @@ -""" -.. currentmodule:: skrub -.. _example_optuna_choices: - -Tuning DataOps with Optuna -========================== - -This example shows how to use `Optuna -`_ to tune the hyperparameters of a -skrub :class:`DataOp`. As seen in the previous example, skrub DataOps can contain -"choices", objects created with :func:`choose_from`, :func:`choose_int`, -:func:`choose_float`, etc. and we can use hyperparameter search techniques to -pick the best outcome for each choice. Performing this search with Optuna -allows us to benefit from its many features, such as state-of-the-art search -strategies, monitoring and visualization, stopping and resuming searches, and -parallel or distributed computation. - -In order to use Optuna with skrub, the package must be installed first. -This can be done with pip: - -.. code-block:: bash - - pip install optuna - -""" - -# %% -# A simple regressor and example data. -# ------------------------------------ -# -# We will fit a regressor containing a few choices on a toy dataset. We -# try 2 regressors: extra trees and ridge. They both have hyperparameters that -# we want to tune. - -# %% -from sklearn.ensemble import ExtraTreesRegressor -from sklearn.linear_model import Ridge - -import skrub - -extra_tree = ExtraTreesRegressor( - min_samples_leaf=skrub.choose_int(1, 32, log=True, name="min_samples_leaf"), -) -ridge = Ridge(alpha=skrub.choose_float(0.01, 10.0, log=True, name="α")) - -regressor = skrub.choose_from( - {"extra_tree": extra_tree, "ridge": ridge}, name="regressor" -) -data = skrub.var("data") -X = data.drop(columns="MedHouseVal", errors="ignore").skb.mark_as_X() -y = data["MedHouseVal"].skb.mark_as_y() -pred = X.skb.apply(regressor, y=y) -print(pred.skb.describe_param_grid()) - -# %% -# Load data for the example - -# %% -import pandas as pd -from sklearn.model_selection import KFold - -# (We subsample the dataset by half to make the example run faster) -file_path = skrub.datasets.fetch_california_housing().path -df = pd.read_csv(file_path).sample(10_000, random_state=0) - -# The environment we will use to fit the learners created by our DataOp. -env = {"data": df} -cv = KFold(n_splits=4, shuffle=True, random_state=0) - -# %% -# Selecting the best hyperparameters with Optuna. -# ----------------------------------------------- -# -# The simplest way to use Optuna is to pass ``backend='optuna'`` to -# :meth:`DataOp.skb.make_randomized_search()`. It is used very similarly as -# with the default backend -# (:class:`sklearn.model_selection.RandomizedSearchCV`). Additional -# parameters are available to control the Optuna sampler, storage and study -# name, and timeout. -# Note that in order to persist the study and resume it later, the ``storage`` -# parameter must be set to a valid database URL (e.g., a SQLite file). Refer to -# the User Guide for an example. - -# %% -search = pred.skb.make_randomized_search( - backend="optuna", cv=cv, n_iter=10, random_state=10 -) -search.fit(env) -search.results_ - -# %% -# The usual ``results_``, ``detailed_results_`` and ``plot_results()`` are -# still available. - -# %% -search.plot_results() - -# %% -# The Optuna :class:`Study ` that was used to run the -# hyperparameter search is available in the attribute ``study_``: - -# %% -search.study_ - -# %% -search.study_.best_params - -# %% -# This allows us to use Optuna's reporting capabilities provided in -# `optuna.visualization -# `_ or -# `optuna-dashboard -# `_. - -# %% -import optuna - -optuna.visualization.plot_slice(search.study_, params=["0:min_samples_leaf"]) - -# %% -# Using Optuna directly for more advanced use cases -# ------------------------------------------------- -# -# Often we may want more control over the use of Optuna, or to access -# functionality not available through :meth:`DataOp.skb.make_randomized_search` -# such as the ask-and-tell interface, trial pruning, callbacks, -# multi-objective optimization, etc. . -# -# Directly using Optuna ourselves is also easy, as we will show now. What makes -# this possible is that we can pass an Optuna Trial to -# :meth:`DataOp.skb.make_learner` in which case the parameters suggested by the -# trial are used to create the learner. -# -# We revisit the example above, following the typical Optuna workflow. -# -# The :class:`optuna.Study ` runs the hyperparameter -# search. -# -# Its method :meth:`optimize ` is given an -# ``objective`` function. The ``objective`` must accept a -# :class:`~optuna.trial.Trial` object (which is produced by the study and picks -# the parameters for a given evaluation of the objective) and return the value -# to maximize (or minimize). -# -# To use Optuna with a :class:`DataOp`, we just need to pass the Trial object -# to :meth:`DataOp.skb.make_learner`. This creates a :class:`SkrubLearner` -# initialized with the parameters picked by the optuna Trial. -# -# We can then cross-validate the SkrubLearner, or score it however we prefer, -# and return the score so that the optuna Study can take it into account. -# -# Here we return a single score (R²), but multi-objective -# optimization is also possible. Please refer to the Optuna documentation for -# more information. - - -# %% - - -def objective(trial): - learner = pred.skb.make_learner(choose=trial) - cv_results = skrub.cross_validate(learner, environment=env, cv=cv) - return cv_results["test_score"].mean() - - -study = optuna.create_study(direction="maximize") -study.optimize(objective, n_trials=10) -study.best_params - -# %% -# We can also use Optuna's visualization capabilities to inspect the study: -optuna.visualization.plot_optimization_history(study) - -# %% -# Now we build a learner with the best hyperparameters and fit it on the full -# dataset: - -# %% -best_learner = pred.skb.make_learner(choose=study.best_trial) - -# This would achieve the same result: -# best_learner = pred.skb.make_learner() -# best_learner.set_params(**study.best_params) - -best_learner.fit(env) -print(best_learner.describe_params()) - -# %% diff --git a/examples/02_data_ops/1140_subsampling.py b/examples/02_data_ops/1140_subsampling.py deleted file mode 100644 index f06013c5c..000000000 --- a/examples/02_data_ops/1140_subsampling.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -.. _example_subsampling: - -Subsampling for faster development -================================== - -Here we show how to use :meth:`.skb.subsample() ` to speed up -interactive construction of a skrub DataOps plan by computing previews on a subsampled -version of the original data. - -.. currentmodule:: skrub - -""" - -# %% - -import pandas as pd - -import skrub -import skrub.datasets - -file_path = skrub.datasets.fetch_employee_salaries().path -dataset = pd.read_csv(file_path) - -full_data = skrub.var("data", dataset) -full_data - -# %% -# We are working with a dataset of over 9K rows. As we build up our plan, -# we see previews of the intermediate results so we can check that it behaves -# as expected. However, if some estimators are slow, fitting them and -# computing results on the whole data can slow us down. -# -# Lightweight construction of the DataOps plan on a subsample -# ---------------------------------------------------------- -# -# We can tell skrub to subsample the data when computing the previews with -# :meth:`.skb.subsample() `. - -# %% -data = full_data.skb.subsample(n=100) -data - -# %% -# The rest of the plan will now use only 100 points for its previews. -# -# .. topic:: Subsampling only applies to previews by default -# -# By default subsampling is applied *only for previews*: the results -# shown when we display the plan, and the output of calling -# :meth:`.skb.preview() `. For other methods such as -# :meth:`.skb.get_learner() ` or -# :meth:`.skb.cross_validate() `, *no subsampling is -# done by default*. We can explicitly ask for it with ``keep_subsampling=True`` -# as we will see below. Even when ``keep_subsampling=True``, subsampling is -# not applied to the ``predict`` method. -# -# To continue building our plan, we now define X and y: - -# %% -employees = data.drop( - columns="current_annual_salary", - errors="ignore", -).skb.mark_as_X() - -salaries = data["current_annual_salary"].skb.mark_as_y() - -# %% -# And finally we apply a TableVectorizer then gradient boosting: - -# %% -from sklearn.ensemble import HistGradientBoostingRegressor - -predictions = employees.skb.apply(skrub.TableVectorizer()).skb.apply( - HistGradientBoostingRegressor(), y=salaries -) - -# %% -# -# All the lines above run very fast, including fitting the predictor above. -# -# When we display our ``predictions`` DataOp, we see that the preview is -# computed on a subsample: the result column has only 100 entries. - -# %% -predictions - -# %% -# We can also turn on subsampling for other DataOps methods, such as -# :meth:`.skb.cross_validate() `. Here we run the -# cross-validation on the small subsample of 100 rows we configured. With such -# a small subsample the scores will be very low but this might help us quickly -# detect errors in our cross-validation scheme. - -# %% -predictions.skb.cross_validate(keep_subsampling=True) - -# %% -# Evaluating the DataOps plan on the full data -# ------------------------------------------- -# By default, when we do not explicitly ask for ``keep_subsampling=True``, no -# subsampling takes place. -# -# Here we run the cross-validation **on the full data**. -# Note the longer ``fit_time`` and much better ``test_score``. - -# %% -predictions.skb.cross_validate() diff --git a/examples/02_data_ops/1150_use_case.py b/examples/02_data_ops/1150_use_case.py deleted file mode 100644 index 09c9f75f3..000000000 --- a/examples/02_data_ops/1150_use_case.py +++ /dev/null @@ -1,170 +0,0 @@ -""" -Use case: developing locally and deploying to production -======================================================= -""" - -# %% -# As a team of data scientists, we are tasked with a project to predict whether an email -# is potentially malicious (i.e., spam or phishing). We develop and test our models -# locally, either in a Jupyter notebook or within a Python script. Once we are satisfied -# with the model's performance, we move on to deploying it. -# -# In this use case, every time the email provider receives a new email, they want to -# verify whether it is spam before displaying it in the recipient's inbox. To achieve -# this, they plan to integrate a machine learning model within a microservice. This -# microservice will accept an email's data as a JSON payload and return a score between -# 0 and 1, indicating the likelihood that the email is spam. -# -# To avoid rewriting the entire data pipeline when moving from model validation to -# production deployment, which is both error-prone and inefficient, we prefer to load an -# object that encapsulates the same processing pipeline used during model development. -# This is where the :class:`~skrub.SkrubLearner` can help. -# -# Adopting this workflow also has the benefit of forcing us to clearly define the type -# of data that will be available at the input of the microservice. It helps ensure we -# build models that rely only on information accessible at this specific point in the -# product pipeline. For example, since we want to detect spam before the email reaches -# the recipient's inbox, we cannot use features that are only available after the -# recipient opens the email. -# -# Since this example is focused on the pipeline construction itself, we won't look at -# our model performance. - -# %% -# Generating the training data -# ---------------------------- -# In this section, we define a few functions that help us with generating the -# training data in dictionary form. We are going to generate a fully random data set. -import random -import string -import uuid -from datetime import datetime, timedelta - -import numpy as np - - -def generate_id(): - return str(uuid.uuid4()) - - -def generate_email(): - length = random.randint(5, 10) - username = "".join(random.choice(string.ascii_lowercase) for _ in range(length)) - domain = ["google", "yahoo", "whatever"] - tld = ["fr", "en", "com", "net"] - return f"{username}@{random.choice(domain)}.{random.choice(tld)}" - - -def generate_datetime(): - random_seconds = random.randint(0, int(timedelta(days=2).total_seconds())) - random_datetime = datetime.now() - timedelta(seconds=random_seconds) - return random_datetime - - -def generate_text(min_str_length, max_str_length): - random_length = random.randint(min_str_length, max_str_length) - random_text = "".join( - random.choice(string.ascii_letters + string.digits + string.punctuation) - for _ in range(random_length) - ) - return random_text - - -# %% -# We generate 1000 training samples and store them in a list of dictionaries: - -n_samples = 1000 - -# %% -# In this use case, the emails to be tested when the model is put in production -# are not contained in a dataframe, but in a JSON. As a result, our training data -# should also be contained in a list of dictionaries. - -X = [ - { - "id": generate_id(), - "sender": generate_email(), - "title": generate_text(max_str_length=10, min_str_length=2), - "content": generate_text(max_str_length=100, min_str_length=10), - "date": generate_datetime(), - "cc_emails": [generate_email() for _ in range(random.randint(0, 5))], - } - for _ in range(n_samples) -] - - -# generate array of 1 and 0 to represent the target variable -y = np.random.binomial(n=1, p=0.9, size=n_samples) - -# %% -# Building the DataOps plan -# ------------------------- -# Let's start our DataOps plan by indicating what the features and the target -# variables are. -import skrub - -X = skrub.X(X) -y = skrub.y(y) - -# %% -# The variable X is currently a list of dictionaries, which estimators cannot -# handle directly. Let's convert it to a pandas DataFrame using -# :func:`~skrub.DataOp.skb.apply_func`. -import pandas as pd - -df = X.skb.apply_func(pd.DataFrame) - -# %% -# For this example, we will use a strong baseline, with skrub's -# :func:`~skrub.tabular_pipeline()`. -tab_pipeline = skrub.tabular_pipeline("classification") - -# We can now apply the predictive model to the data. -# The DataOps plan is ready after applying the model to the data. -predictions = df.skb.apply(tab_pipeline, y=y) - -# We can then explore the full plan: -predictions.skb.draw_graph() - -# %% -# To end the explorative work, we need to build the learner, fit it, and save it to a -# file. -# Passing ``fitted=True`` to the :func:`~skrub.DataOp.skb.make_learner` -# function makes it so that the learner is fitted on the data that has been passed to -# the variables of the DataOps plan. -import joblib - -with open("learner.pkl", "wb") as f: - learner = predictions.skb.make_learner(fitted=True) - joblib.dump(learner, f) - -# %% -# Production phase -# ---------------- -# -# In our microservice, we receive a payload in JSON format. -X_input = { - "id": generate_id(), - "sender": generate_email(), - "title": generate_text(max_str_length=10, min_str_length=2), - "content": generate_text(max_str_length=100, min_str_length=10), - "date": generate_datetime(), - "cc_emails": [generate_email() for _ in range(random.randint(0, 5))], -} - -# We just have to load the learner and use it to predict the score for this input. -with open("learner.pkl", "rb") as f: - loaded_learner = joblib.load(f) -# ``X_input`` must be passed as a list so that it can be parsed correctly as a dataframe -# by Pandas. -prediction = loaded_learner.predict({"X": [X_input]}) -prediction - -# %% -# Conclusion -# ---------- -# -# Thanks to the skrub DataOps and learner, we ensure that all the transformations -# and preprocessing done during model development are exactly the same as those done in -# production. This makes deployment straightforward and reduces the risk of errors -# when moving from development to production environments. diff --git a/examples/02_data_ops/1160_pytorch.py b/examples/02_data_ops/1160_pytorch.py deleted file mode 100644 index 9f26ab54f..000000000 --- a/examples/02_data_ops/1160_pytorch.py +++ /dev/null @@ -1,218 +0,0 @@ -""" -Using PyTorch (via skorch) in DataOps -====================================== - -This example shows how to wrap a PyTorch model with skorch and plug it into a -skrub DataOps plan. - -.. note:: - This example requires the optional dependencies ``torch`` and ``skorch``. - -The main goal here is to show the *integration pattern*: - -- **PyTorch** defines the model (an ``nn.Module``) -- **skorch** wraps it as a scikit-learn compatible estimator -- **skrub DataOps** builds a plan and can tune skorch (and therefore PyTorch) - hyperparameters using the skrub choices. -""" - -# %% -# Loading the data -# ================= -# -# We use scikit-learn's digits dataset because it is small and ships with -# scikit-learn. Each sample is an 8x8 grayscale image of a -# handwritten digit, encoded as 64 pixel intensity values and displays a -# number from 0 to 9. -from sklearn.datasets import load_digits - -digits = load_digits() -X, y = digits.data, digits.target -print(f"Dataset shape: {X.shape}") -print(f"Number of classes: {len(set(y))}") - -# %% -# Start of the DataOps plan -# ========================== -# -# We start the DataOps plan by creating the skrub variables X and y. -import skrub - -X = skrub.X(X) -y = skrub.y(y) - -# %% -# Data preprocessing -# ================== -# -# We start by normalizing the pixel values to [0, 1] by first -# computing the global max value and then dividing the pixel values -# by this max value. Importantly, we freeze the max value (scaling factor) -# after fitting so that the same rescaling is applied later when we use our -# dataop for prediction on new (test) data. -# -# A convolutional network expects images with shape (N, C, H, W) where: -# -# - N: number of samples -# - C: number of color channels (1 for grayscale) -# - H, W: image height and width -# -# So we reshape the images to (N, 1, 8, 8) for the CNN. The -1 means the first -# dimension (N) is inferred automatically from the array size. -# -# The advantage of using DataOps is that the preprocessing steps are tracked -# in the plan and will be automatically applied during prediction. - -max_value = X.max().skb.freeze_after_fit() -X_scaled = X / max_value -X_reshaped = X_scaled.reshape(-1, 1, 8, 8).astype("float32") -X_reshaped.skb.draw_graph() - - -# %% -# Building a NN Classifier -# ========================= -# -# We'll build a tiny CNN using PyTorch and wrap it with skorch to make it -# scikit-learn compatible. The architecture uses a single convolution + pooling -# stage and a small MLP head. The architectural choices below are meant to be: -# -# - **standard**: 3x3 convolutions and 2x2 max-pooling are very common -# - **small**: the dataset and images are tiny, so we keep the model tiny too -# -# If you want more background on CNN building blocks and how convolution/pooling -# changes tensor shapes, see the CS231n notes: -# https://cs231n.github.io/convolutional-networks/ -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim - - -class TinyCNN(nn.Module): - def __init__(self, conv_channels: int = 8, hidden_units: int = 32): - super().__init__() - self.conv_channels = conv_channels - self.hidden_units = hidden_units - - # 2-level CNN with 2x2 max-pooling - self.conv1 = nn.Conv2d( - in_channels=1, out_channels=conv_channels, kernel_size=3, padding=1 - ) - self.conv2 = nn.Conv2d(conv_channels, conv_channels, kernel_size=3, padding=1) - self.pool = nn.MaxPool2d(kernel_size=2) - - # input shape = (8,8) -> conv1: (8,8) -> conv2: (8,8) -> pool: (4,4) - image_shape_after_conv = 4 * 4 - - # MLP head - self.fc1 = nn.Linear(conv_channels * image_shape_after_conv, hidden_units) - self.dropout = nn.Dropout(p=0.25) # Regularization to avoid overfitting - self.fc2 = nn.Linear(hidden_units, 10) # 10 digit classes (0..9) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = self.pool(F.relu(self.conv2(x))) - x = x.flatten(start_dim=1) - x = self.dropout(F.relu(self.fc1(x))) - return self.fc2(x) - - -# %% -# Skorch provides scikit-learn compatible wrappers around torch training loops. -# That makes the torch model usable by skrub DataOps (and scikit-learn tools in -# general). -# -# We use :func:`skrub.choose_from()` to define hyperparameters that the DataOps -# grid search will tune: conv_channels, hidden_units, and max_epochs. -# The other parameters are set to common choices for this task and training data size. - -from skorch import NeuralNetClassifier - -device = "cpu" # use "cuda" or "mps" if available - -net = NeuralNetClassifier( - module=TinyCNN, - # These choices are intentionally small so the example runs quickly. - module__conv_channels=skrub.choose_from([8, 16], name="conv_channels"), - module__hidden_units=skrub.choose_from([8, 16, 32], name="hidden_units"), - max_epochs=skrub.choose_from([10, 15], name="max_epochs"), - optimizer__lr=0.01, - optimizer=optim.Adam, - criterion=nn.CrossEntropyLoss, - device=device, - train_split=None, # We'll use skrub's grid search for validation - verbose=0, -) - - -# %% -# Tuning the model's hyperparameters with DataOps -# =============================================== -# -# We integrate the model into the DataOps plan. First, we -# convert the target labels to integers for the loss computation -# and apply the model to the preprocessed X and y. - -y_int = y.astype("int64") -predictor = X_reshaped.skb.apply(net, y=y_int) -predictor.skb.draw_graph() - -# %% -# Finally, we use 4-fold cross-validation for the hyperparameter -# tuning on our DataOps plan. - -from sklearn.model_selection import KFold - -cv = KFold(n_splits=4, shuffle=True, random_state=42) -search = predictor.skb.make_grid_search( - cv=cv, - fitted=True, - n_jobs=-1, -) -print("\nSearch results:") -print(search.results_.to_string(index=False)) - -# %% -# Let's take a better look at the well-performing models by looking -# at the parallel coordinates plot. We filter to models with -# score >= 0.94 to focus on the top-performing configurations. - -fig = search.plot_results(min_score=0.94) -fig - -# %% -# Interpreting the results -# ======================== -# -# Looking at the search results, we can observe several patterns: -# -# - **Model capacity matters**: Larger configurations with ``conv_channels=16`` -# and ``hidden_units=32`` tend to perform best. Smaller models with -# ``conv_channels=8`` and/or ``hidden_units=8`` perform significantly worse, -# indicating that the task benefits from increased model capacity. -# - **More epochs generally help**: Configurations with ``max_epochs=15`` tend to -# perform slightly better than those with ``max_epochs=10``, though the gains -# are modest compared to architectural changes. - -# %% -# Conclusion -# ========== -# -# In this example, we've shown how to use **PyTorch** and **skorch** within -# skrub DataOps. The key steps were: -# -# 1. Define a PyTorch ``nn.Module`` (our ``TinyCNN``) -# 2. Wrap it with skorch's ``NeuralNetClassifier`` to make it scikit-learn compatible -# 3. Use :func:`skrub.choose_from()` to specify hyperparameters for tuning -# 4. Integrate it into a DataOps plan and use grid search to find the best configuration -# -# This pattern lets you leverage PyTorch's flexibility for model definition while -# benefiting from skrub's hyperparameter tuning and data preprocessing capabilities. -# -# .. seealso:: -# -# * :ref:`example_tuning_pipelines`: Learn more about using -# ``skrub.choose_from()`` and other choice objects to tune hyperparameters -# in DataOps plans. -# * :ref:`example_optuna_choices`: Discover how to use Optuna as a backend -# for more sophisticated hyperparameter search strategies with skrub DataOps. diff --git a/examples/02_data_ops/GALLERY_HEADER.rst b/examples/02_data_ops/GALLERY_HEADER.rst deleted file mode 100644 index 865b2bc81..000000000 --- a/examples/02_data_ops/GALLERY_HEADER.rst +++ /dev/null @@ -1,4 +0,0 @@ -.. _data_ops_examples_ref: - -Skrub DataOps -================= diff --git a/examples/03_joining/0040_fuzzy_joining.py b/examples/03_joining/0040_fuzzy_joining.py deleted file mode 100644 index 38874b3a4..000000000 --- a/examples/03_joining/0040_fuzzy_joining.py +++ /dev/null @@ -1,408 +0,0 @@ -""" -.. _example_fuzzy_joining: - -Fuzzy joining dirty tables with the Joiner -========================================== - -Here we show how to combine data from different sources, -with a vocabulary not well normalized. - -Joining is difficult: one entry on one side does not have -an exact match on the other side. - -The |fj| function enables to join tables without cleaning the data by -accounting for the label variations. - -To illustrate, we will join data from the -`2022 World Happiness Report `_, with tables -provided in `the World Bank open data platform `_ -in order to create a first prediction model. - -Moreover, the |joiner| is a scikit-learn Transformer that makes it easy to -use such fuzzy joining multiple tables to bring in information in a -machine-learning pipeline. In particular, it enables tuning parameters of -|fj| to find the matches that maximize prediction accuracy. - - -.. |fj| replace:: :func:`~skrub.fuzzy_join` - -.. |joiner| replace:: :func:`~skrub.Joiner` -""" - -############################################################################### -# Data Importing and preprocessing -# -------------------------------- -# -# We import the happiness score table first: -import pandas as pd - -from skrub import datasets - -happiness_data = datasets.fetch_country_happiness() -df = pd.read_csv(happiness_data.happiness_report_path) - -############################################################################### -# Let's look at the table: -df.head(3) - -############################################################################### -# This is a table that contains the happiness index of a country along with -# some of the possible explanatory factors: GDP per capita, Social support, -# Generosity etc. -# - -############################################################################### -# For the sake of this example, we only keep the country names and our -# variable of interest: the 'Happiness score'. -df = df[["Country", "Happiness score"]] - -############################################################################### -# Additional tables from other sources -# ------------------------------------ -# -# Now, we need to include explanatory factors from other sources, to -# complete our covariates (X table). -# -# Interesting tables can be found on `the World Bank open data platform -# `_, which are also available in the dataset -# We extract the table containing GDP per capita by country: - -gdp_per_capita = pd.read_csv(happiness_data.GDP_per_capita_path) -gdp_per_capita.head(3) - -############################################################################### -# Then another table, with life expectancy by country: -life_exp = pd.read_csv(happiness_data.life_expectancy_path) -life_exp.head(3) - -############################################################################### -# And a table with legal rights strength by country: -legal_rights = pd.read_csv(happiness_data.legal_rights_index_path) -legal_rights.head(3) - -############################################################################### -# A correspondence problem -# ------------------------ -# -# Alas, the entries for countries do not perfectly match between our -# original table (df), and those that we downloaded from the worldbank -# (gdp_per_capita): - -df.sort_values(by="Country").tail(7) - -############################################################################### -gdp_per_capita.sort_values(by="Country Name").tail(7) - -############################################################################### -# We can see that Yemen is written "Yemen*" on one side, and -# "Yemen, Rep." on the other. -# -# We also have entries that probably do not have correspondences: "World" -# on one side, whereas the other table only has country-level data. - -############################################################################### -# Joining tables with imperfect correspondence -# -------------------------------------------- -# -# We will now join our initial table, df, with the 3 additional ones that -# we have extracted. -# - -############################################################################### -# .. _example_fuzzy_join: -# -# 1. Joining GDP per capita table -# ............................... -# -# To join them with skrub, we only need to do the following: -from skrub import fuzzy_join - -augmented_df = fuzzy_join( - df, # our table to join - gdp_per_capita, # the table to join with - left_on="Country", # the first join key column - right_on="Country Name", # the second join key column - add_match_info=True, -) - -augmented_df.tail(20) - -# We merged the first World Bank table to our initial one. - -############################################################################### -# .. topic:: Note: -# -# We set the ``add_match_info`` parameter to `True` to show distances -# between the rows that have been matched, that we will use later to show -# what are the worst matches. - -############################################################################### -# -# We see that our |fj| successfully identified the countries, -# even though some country names differ between tables. -# -# For instance, "Egypt" and "Egypt, Arab Rep." are correctly matched, as are -# "Lesotho*" and "Lesotho". -# -# .. topic:: Note: -# -# This would all be missed out if we were using other methods such as -# `pandas.merge `_, -# which can only find exact matches. -# In this case, to reach the best result, we would have to `manually` clean -# the data (e.g. remove the * after country name) and look -# for matching patterns in every observation. -# -# Let's do some more inspection of the merging done. - -############################################################################### -# Let's print the worst matches, which will give -# us an overview of the situation: - -augmented_df.sort_values("skrub_Joiner_rescaled_distance").tail(10) - -############################################################################### -# We see that some matches were unsuccessful -# (e.g "Palestinian Territories*" and "Palau"), -# because there is simply no match in the two tables. - -############################################################################### -# In this case, it is better to use the threshold parameter (``max_dist``) -# so as to include only precise-enough matches: -# -augmented_df = fuzzy_join( - df, - gdp_per_capita, - left_on="Country", - right_on="Country Name", - max_dist=0.9, - add_match_info=True, -) -augmented_df.sort_values("skrub_Joiner_rescaled_distance", ascending=False).head() - -############################################################################### -# Matches that are not available (or precise enough) are marked as ``NaN``. -# We will remove them using the ``drop_unmatched`` parameter: - -augmented_df = fuzzy_join( - df, - gdp_per_capita, - left_on="Country", - right_on="Country Name", - drop_unmatched=True, - max_dist=0.9, - add_match_info=True, -) - -augmented_df.drop(columns=["Country Name"], inplace=True) - -############################################################################### -# We can finally plot and look at the link between GDP per capital -# and happiness: -import matplotlib.pyplot as plt -import seaborn as sns - -sns.set_context("notebook") - -plt.figure(figsize=(4, 3)) -ax = sns.regplot( - data=augmented_df, - x="GDP per capita (current US$)", - y="Happiness score", - lowess=True, -) -ax.set_ylabel("Happiness index") -ax.set_title("Is a higher GDP per capita linked to happiness?") -plt.tight_layout() -plt.show() - -############################################################################### -# It seems that the happiest countries are those -# having a high GDP per capita. -# However, unhappy countries do not have only low levels -# of GDP per capita. We have to search for other patterns. - -############################################################################### -# 2. Joining life expectancy table -# ................................ -# -# Now let's include other information that may be relevant, such as in the -# life_exp table: -augmented_df = fuzzy_join( - augmented_df, - life_exp, - left_on="Country", - right_on="Country Name", - max_dist=0.9, - add_match_info=True, -) - -augmented_df.drop(columns=["Country Name"], inplace=True) - -augmented_df.head(3) - -############################################################################### -# Let's plot this relation: -plt.figure(figsize=(4, 3)) -fig = sns.regplot( - data=augmented_df, - x="Life expectancy at birth, total (years)", - y="Happiness score", - lowess=True, -) -fig.set_ylabel("Happiness index") -fig.set_title("Is a higher life expectancy linked to happiness?") -plt.tight_layout() -plt.show() - -############################################################################### -# It seems the answer is yes! -# Countries with higher life expectancy are also happier. - - -############################################################################### -# 3. Joining legal rights strength table -# ...................................... -# -# And the table with a measure of legal rights strength in the country: -augmented_df = fuzzy_join( - augmented_df, - legal_rights, - left_on="Country", - right_on="Country Name", - max_dist=0.9, - add_match_info=True, -) - -augmented_df.drop(columns=["Country Name"], inplace=True) - -augmented_df.head(3) - -############################################################################### -# Let's take a look at their correspondence in a figure: -plt.figure(figsize=(4, 3)) -fig = sns.regplot( - data=augmented_df, - x="Strength of legal rights index (0=weak to 12=strong)", - y="Happiness score", - lowess=True, -) -fig.set_ylabel("Happiness index") -fig.set_title("Does a country's legal rights strength lead to happiness?") -plt.tight_layout() -plt.show() - -############################################################################### -# From this plot, it is not clear that this measure of legal strength -# is linked to happiness. - -############################################################################### -# Great! Our joined table has become bigger and full of useful information. -# And now we are ready to apply a first machine learning model to it! - -############################################################################### -# Prediction model -# ---------------- -# -# We now separate our covariates (X), from the target (or exogenous) -# variables: y. -y = augmented_df["Happiness score"] -X = augmented_df.drop(["Happiness score", "Country"], axis=1) - -################################################################### -# Let us now define the model that will be used to predict the happiness score: - -from sklearn.ensemble import HistGradientBoostingRegressor -from sklearn.model_selection import KFold - -hgdb = HistGradientBoostingRegressor(random_state=0) -cv = KFold(n_splits=5, shuffle=True, random_state=0) - -################################################################# -# To evaluate our model, we will apply a `5-fold cross-validation`. -# We evaluate our model using the `R2` score. -# -# Let's finally assess the results of our models: -from sklearn.model_selection import cross_validate - -cv_results_t = cross_validate(hgdb, X, y, cv=cv, scoring="r2") - -cv_r2_t = cv_results_t["test_score"] - -print(f"Mean R² score is {cv_r2_t.mean():.2f} +- {cv_r2_t.std():.2f}") - -################################################################# -# We have a satisfying first result: an R² of 0.63! -# -# Data cleaning varies from dataset to dataset: there are as -# many ways to clean a table as there are errors. The |fj| -# method is generalizable across all datasets. -# -# Data transformation is also often very costly in both time and resources. -# |fj| is fast and easy-to-use. -# -# Now up to you, try improving our model by adding information into it and -# beating our result! - -####################################################################### -# Using the |joiner| to fuzzy join multiple tables -# ------------------------------------------------- -# A convenient way to merge different tables from the World Bank -# to `X` in a scikit-learn Pipeline and tune the parameters is to use the |joiner|. -# -# The |joiner| is a transformer that can fuzzy-join a table on -# a main table. - -####################################################################### -# .. _example_joiner: -# -# Instantiating the transformer -# ............................. - -y = df["Happiness score"] -df = df.drop("Happiness score", axis=1) - -from sklearn.pipeline import make_pipeline - -from skrub import Joiner, SelectCols - -# We create a selector that we will insert at the end of our pipeline, to -# select the relevant columns before fitting the regressor -selector = SelectCols( - [ - "GDP per capita (current US$) gdp", - "Life expectancy at birth, total (years) life_exp", - "Strength of legal rights index (0=weak to 12=strong) legal_rights", - ] -) - -# And we can now put together the pipeline -pipeline = make_pipeline( - Joiner(gdp_per_capita, main_key="Country", aux_key="Country Name", suffix=" gdp"), - Joiner(life_exp, main_key="Country", aux_key="Country Name", suffix=" life_exp"), - Joiner( - legal_rights, main_key="Country", aux_key="Country Name", suffix=" legal_rights" - ), - selector, - HistGradientBoostingRegressor(), -) - - -########################################################################## -# And the best part is that we are now able to evaluate the parameters of the |fj|. -# For instance, the ``match_score`` was manually picked and can now be -# introduced into a grid search: - -from sklearn.model_selection import GridSearchCV - -# We will test 2 possible values of max_dist: -params = { - "joiner-1__max_dist": [0.1, 0.9], - "joiner-2__max_dist": [0.1, 0.9], - "joiner-3__max_dist": [0.1, 0.9], -} - -grid = GridSearchCV(pipeline, param_grid=params, cv=cv) -grid.fit(df, y) - -print("Best parameters:", grid.best_params_) diff --git a/examples/03_joining/0060_multiple_key_join.py b/examples/03_joining/0060_multiple_key_join.py deleted file mode 100644 index 4ed5b1767..000000000 --- a/examples/03_joining/0060_multiple_key_join.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -.. _example_multiple_key_join: - -Spatial join for flight data: Joining across multiple columns -============================================================= - -Joining tables may be difficult if one entry on one side does not have -an exact match on the other side. - -This problem becomes even more complex when multiple columns -are significant for the join. For instance, this is the case -for **spatial joins** on two columns, typically -longitude and latitude. - -|joiner| is a scikit-learn compatible transformer that enables -performing joins across multiple keys, -independently of the data type (numerical, string or mixed). - -The following example uses US domestic flights data -to illustrate how space and time information from a -pool of tables are combined for machine learning. - -.. |fj| replace:: :func:`~skrub.fuzzy_join` - -.. |joiner| replace:: :func:`~skrub.Joiner` - -.. |Pipeline| replace:: - :class:`~sklearn.pipeline.Pipeline` -""" - -############################################################################### -# Flight-delays data -# ------------------ -# The goal is to predict flight delays. -# We have a pool of tables that we will use to improve our prediction. -# -# The following tables are at our disposal: - -############################################################################### -# The main table: flights dataset -# ............................... -# - The `flights` dataset. It contains all US flights date, origin -# and destination airports and flight time. -# Here, we consider only flights from 2008. - -import pandas as pd - -from skrub.datasets import fetch_flight_delays - -dataset = fetch_flight_delays() -seed = 1 -flights = pd.read_csv(dataset.flights_path) - -# Sampling for faster computation. -flights = flights.sample(5_000, random_state=seed, ignore_index=True) -flights.head() - -############################################################################### -# Let us see the arrival delay of the flights in the dataset: -import matplotlib.pyplot as plt -import seaborn as sns - -sns.set_theme(style="ticks") - -ax = sns.histplot(data=flights, x="ArrDelay") -ax.set_yscale("log") -plt.show() - -############################################################################ -# Interesting, most delays are relatively short (<100 min), but there -# are some very long ones. - -############################################################################ -# Airport data: an auxiliary table from the same database -# ....................................................... -# - The ``airports`` dataset, with information such as their name -# and location (longitude, latitude). - -airports = pd.read_csv(dataset.airports_path) -airports.head() - -######################################################################## -# Weather data: auxiliary tables from external sources -# .................................................... -# - The ``weather`` table. Weather details by measurement station. -# Both tables are from the Global Historical Climatology Network. -# Here, we consider only weather measurements from 2008. - -weather = pd.read_csv(dataset.weather_path) -# Sampling for faster computation. -weather = weather.sample(10_000, random_state=seed, ignore_index=True) -weather.head() - -######################################################################## -# - The ``stations`` dataset. Provides location of all the weather -# measurement stations in the US. - -stations = pd.read_csv(dataset.stations_path) -stations.head() - -############################################################################### -# Joining: feature augmentation across tables -# ------------------------------------------- -# First we join the stations with weather on the ID (exact join): - -aux = pd.merge(stations, weather, on="ID") -aux.head() - -############################################################################### -# Then we join this table with the airports so that we get all auxiliary -# tables into one. - -from skrub import Joiner - -joiner = Joiner(airports, aux_key=["lat", "long"], main_key=["LATITUDE", "LONGITUDE"]) - -aux_augmented = joiner.fit_transform(aux) - -aux_augmented.head() - -############################################################################### -# Joining airports with flights data: -# Let's instantiate another multiple key joiner on the date and the airport: - -joiner = Joiner( - aux_augmented, - aux_key=["YEAR/MONTH/DAY", "iata"], - main_key=["Year_Month_DayofMonth", "Origin"], -) - -flights.drop(columns=["TailNum", "FlightNum"]) - -############################################################################### -# Training data is then passed through a |Pipeline|: -# -# - We will combine all the information from our pool of tables into "flights", -# our main table. -# - We will use this main table to model the prediction of flight delay. -# - -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.pipeline import make_pipeline - -from skrub import TableVectorizer - -tv = TableVectorizer() -hgb = HistGradientBoostingClassifier() - -pipeline_hgb = make_pipeline(joiner, tv, hgb) - -############################################################################### -# We isolate our target variable and remove useless ID variables: - -y = flights["ArrDelay"] -X = flights.drop(columns=["ArrDelay"]) - -############################################################################### -# We want to frame this as a classification problem: -# suppose that your company is obliged to reimburse the ticket -# price if the flight is delayed. -# -# We have a binary classification problem: -# the flight was delayed (1) or not (0). - -y = (y > 0).astype(int) -y.value_counts() - -############################################################################### -# The results: - -from sklearn.model_selection import train_test_split - -X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed) -pipeline_hgb.fit(X_train, y_train).score(X_test, y_test) - -############################################################################### -# Conclusion -# ---------- -# -# In this example, we have combined multiple tables with complex joins -# on imprecise and multiple-key correspondences. -# This is made easy by skrub's |Joiner| transformer. -# -# Our final cross-validated accuracy score is 0.55. diff --git a/examples/03_joining/0070_join_aggregation.py b/examples/03_joining/0070_join_aggregation.py deleted file mode 100644 index 27426f2b7..000000000 --- a/examples/03_joining/0070_join_aggregation.py +++ /dev/null @@ -1,352 +0,0 @@ -""" -AggJoiner on a credit fraud dataset -=================================== - -Many problems involve tables whose entities have a one-to-many relationship. -To simplify aggregate-then-join operations for machine learning, we can include -the |AggJoiner| in our pipeline. - - -In this example, we are tackling a fraudulent loan detection use case. -Because fraud is rare, this dataset is extremely imbalanced, with a prevalence of around -1.4%. - -The data consists of two distinct entities: e-commerce "baskets", and "products". -Baskets can be tagged fraudulent (1) or not (0), and are essentially a list of products -of variable size. Each basket is linked to at least one products, e.g. basket 1 can have -product 1 and 2. - -.. image:: ../../_static/08_example_data.png - :width: 450 px - -| - -Our aim is to predict which baskets are fraudulent. - -The products dataframe can be joined on the baskets dataframe using the ``basket_ID`` -column. - -Each product has several attributes: - -- a category (marked by the column ``"item"``), -- a model (``"model"``), -- a brand (``"make"``), -- a merchant code (``"goods_code"``), -- a price per unit (``"cash_price"``), -- a quantity selected in the basket (``"Nbr_of_prod_purchas"``) - -.. |AggJoiner| replace:: - :class:`~skrub.AggJoiner` - -.. |Joiner| replace:: - :class:`~skrub.Joiner` - -.. |DropCols| replace:: - :class:`~skrub.DropCols` - -.. |TableVectorizer| replace:: - :class:`~skrub.TableVectorizer` - -.. |TableReport| replace:: - :class:`~skrub.TableReport` - -.. |MinHashEncoder| replace:: - :class:`~skrub.MinHashEncoder` - -.. |TargetEncoder| replace:: - :class:`~sklearn.preprocessing.TargetEncoder` - -.. |make_pipeline| replace:: - :func:`~sklearn.pipeline.make_pipeline` - -.. |Pipeline| replace:: - :class:`~sklearn.pipeline.Pipeline` - -.. |HGBC| replace:: - :class:`~sklearn.ensemble.HistGradientBoostingClassifier` - -.. |OrdinalEncoder| replace:: - :class:`~sklearn.preprocessing.OrdinalEncoder` - -.. |TunedThresholdClassifierCV| replace:: - :class:`~sklearn.model_selection.TunedThresholdClassifierCV` - -.. |CalibrationDisplay| replace:: - :class:`~sklearn.calibration.CalibrationDisplay` - -.. |pandas.melt| replace:: - :func:`~pandas.melt` - -""" - -# %% -import pandas as pd - -from skrub import TableReport -from skrub.datasets import fetch_credit_fraud - -bunch = fetch_credit_fraud() -products = pd.read_csv(bunch.products_path) -baskets = pd.read_csv(bunch.baskets_path) - -TableReport(products) - -# %% -TableReport(baskets) - -# %% -# Naive aggregation -# ----------------- -# -# Let's explore a naive solution first. -# -# .. note:: -# -# Click :ref:`here` to skip this section and see the AggJoiner -# in action! -# -# -# The first idea that comes to mind to merge these two tables is to aggregate the -# products attributes into lists, using their basket IDs. -products_grouped = products.groupby("basket_ID").agg(list) -TableReport(products_grouped) - -# %% -# Then, we can expand all lists into columns, as if we were "flattening" the dataframe. -# We end up with a products dataframe ready to be joined on the baskets dataframe, using -# ``"basket_ID"`` as the join key. - -products_flatten = [] -for col in products_grouped.columns: - cols = [f"{col}{idx}" for idx in range(24)] - products_flatten.append(pd.DataFrame(products_grouped[col].to_list(), columns=cols)) -products_flatten = pd.concat(products_flatten, axis=1) -products_flatten.insert(0, "basket_ID", products_grouped.index) -TableReport(products_flatten) - -# %% -# Look at the "Stats" section of the |TableReport| above. Does anything strike you? -# -# Not only did we create 144 columns, but most of these columns are filled with NaN, -# which is very inefficient for learning! -# -# This is because each basket contains a variable number of products, up to 24, and we -# created one column for each product attribute, for each position (up to 24) in -# the dataframe. -# -# Moreover, if we wanted to replace text columns with encodings, we would create -# :math:`d \times 24 \times 2` columns (encoding of dimensionality :math:`d`, for -# 24 products, for the ``"item"`` and ``"make"`` columns), which would explode the -# memory usage. -# -# .. _agg-joiner-anchor: -# -# AggJoiner -# --------- -# Let's now see how the |AggJoiner| can help us solve this. We begin with splitting our -# basket dataset in a training and testing set. -from sklearn.model_selection import train_test_split - -X, y = baskets[["ID"]], baskets["fraud_flag"] -X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.1) -X_train.shape, y_train.shape - -# %% -# Before aggregating our product dataframe, we need to vectorize our categorical -# columns. To do so, we use: -# -# - |MinHashEncoder| on "item" and "model" columns, because they both expose typos -# and text similarities. -# - |OrdinalEncoder| on "make" and "goods_code" columns, because they consist in -# orthogonal categories. -# -# We bring this logic into a |TableVectorizer| to vectorize these columns in a -# single step. -# See `this example `_ -# for more details about these encoding choices. -from sklearn.preprocessing import OrdinalEncoder - -from skrub import MinHashEncoder, TableVectorizer - -vectorizer = TableVectorizer( - high_cardinality=MinHashEncoder(), # encode ["item", "model"] - specific_transformers=[ - (OrdinalEncoder(), ["make", "goods_code"]), - ], -) -products_transformed = vectorizer.fit_transform(products) -TableReport(products_transformed) - -# %% -# Our objective is now to aggregate this vectorized product dataframe by -# ``"basket_ID"``, then to merge it on the baskets dataframe, still on -# the ``"basket_ID"``. -# -# .. image:: ../../_static/08_example_aggjoiner.png -# :width: 900 -# -# | -# -# |AggJoiner| can help us achieve exactly this. We need to pass the product dataframe as -# an auxiliary table argument to |AggJoiner| in ``__init__``. The ``aux_key`` argument -# represent both the columns used to groupby on, and the columns used to join on. -# -# The basket dataframe is our main table, and we indicate the columns to join on with -# ``main_key``. Note that we pass the main table during ``fit``, and we discuss the -# limitations of this design in the conclusion at the bottom of this notebook. -# -# The minimum ("min") is the most appropriate operation to aggregate encodings from -# |MinHashEncoder|, for reasons that are out of the scope of this notebook. -# -from skrub import AggJoiner -from skrub import selectors as s - -# Skrub selectors allow us to select columns using regexes, which reduces -# the boilerplate. -minhash_cols_query = s.glob("item*") | s.glob("model*") -minhash_cols = s.select(products_transformed, minhash_cols_query).columns - -agg_joiner = AggJoiner( - aux_table=products_transformed, - aux_key="basket_ID", - main_key="ID", - cols=minhash_cols, - operations=["min"], -) -baskets_products = agg_joiner.fit_transform(baskets) -TableReport(baskets_products) - -# %% -# Now that we understand how to use the |AggJoiner|, we can now assemble our pipeline by -# chaining two |AggJoiner| together: -# -# - the first one to deal with the |MinHashEncoder| vectors as we just saw -# - the second one to deal with the all the other columns -# -# For the second |AggJoiner|, we use the mean, standard deviation, minimum and maximum -# operations to extract a representative summary of each distribution. -# -# |DropCols| is another skrub transformer which removes the "ID" column, which doesn't -# bring any information after the joining operation. -from scipy.stats import loguniform, randint -from sklearn.ensemble import HistGradientBoostingClassifier -from sklearn.pipeline import make_pipeline - -from skrub import DropCols - -model = make_pipeline( - AggJoiner( - aux_table=products_transformed, - aux_key="basket_ID", - main_key="ID", - cols=minhash_cols, - operations=["min"], - ), - AggJoiner( - aux_table=products_transformed, - aux_key="basket_ID", - main_key="ID", - cols=["make", "goods_code", "cash_price", "Nbr_of_prod_purchas"], - operations=["sum", "mean", "std", "min", "max"], - ), - DropCols(["ID"]), - HistGradientBoostingClassifier(), -) -model - -# %% -# We tune the hyper-parameters of the |HGBC| model using ``RandomizedSearchCV``. -# By default, the |HGBC| applies early stopping when there are at least 10_000 -# samples so we don't need to explicitly tune the number of trees (``max_iter``). -# Therefore we set this at a very high level of 1_000. We increase -# ``n_iter_no_change`` to make sure early stopping does not kick in too early. -from time import time - -from sklearn.model_selection import RandomizedSearchCV - -param_distributions = dict( - histgradientboostingclassifier__learning_rate=loguniform(1e-2, 5e-1), - histgradientboostingclassifier__min_samples_leaf=randint(2, 64), - histgradientboostingclassifier__max_leaf_nodes=[None, 10, 30, 60, 90], - histgradientboostingclassifier__n_iter_no_change=[50], - histgradientboostingclassifier__max_iter=[1000], -) - -tic = time() -search = RandomizedSearchCV( - model, - param_distributions, - scoring="neg_log_loss", - refit=False, - n_iter=10, - cv=3, - verbose=1, -).fit(X_train, y_train) -print(f"This operation took {time() - tic:.1f}s") -# %% -# The best hyper parameters are: - -pd.Series(search.best_params_) - -# %% -# To benchmark our performance, we plot the log loss of our model on the test set -# against the log loss of a dummy model that always output the observed probability of -# the two classes. -# -# As this dataset is extremely imbalanced, this dummy model should be a good baseline. -# -# The vertical bar represents one standard deviation around the mean of the cross -# validation log-loss. -import seaborn as sns -from matplotlib import pyplot as plt -from sklearn.dummy import DummyClassifier -from sklearn.metrics import log_loss - -results = search.cv_results_ -best_idx = search.best_index_ -log_loss_model_mean = -results["mean_test_score"][best_idx] -log_loss_model_std = results["std_test_score"][best_idx] - -dummy = DummyClassifier(strategy="prior").fit(X_train, y_train) -y_proba_dummy = dummy.predict_proba(X_test) -log_loss_dummy = log_loss(y_true=y_test, y_pred=y_proba_dummy) - -fig, ax = plt.subplots() -ax.bar( - height=[log_loss_model_mean, log_loss_dummy], - x=["AggJoiner model", "Dummy"], - color=["C0", "C4"], -) -for container in ax.containers: - ax.bar_label(container, padding=4) - -ax.vlines( - x="AggJoiner model", - ymin=log_loss_model_mean - log_loss_model_std, - ymax=log_loss_model_mean + log_loss_model_std, - linestyle="-", - linewidth=1, - color="k", -) -sns.despine() -ax.set_title("Log loss (lower is better)") - -# %% -# Conclusion -# ---------- -# With |AggJoiner|, you can bring the aggregation and joining operations within a -# sklearn pipeline, and train models more efficiently. -# -# One known limitation of both the |AggJoiner| and |Joiner| is that the auxiliary data -# to join is passed during the ``__init__`` method instead of the ``fit`` method, and -# is therefore fixed once the model has been trained. -# This limitation causes two main issues: -# -# 1. **Bigger model serialization:** Since the dataset has to be pickled along with -# the model, it can result in a massive file size on disk. -# -# 2. **Inflexibility with new, unseen data in a production environment:** To use new -# auxiliary data, you would need to replace the auxiliary table in the |AggJoiner| that -# was used during ``fit`` with the updated data, which is a rather hacky approach. -# -# These limitations will be addressed later in skrub. diff --git a/examples/03_joining/0080_interpolation_join.py b/examples/03_joining/0080_interpolation_join.py deleted file mode 100644 index aec4a7c56..000000000 --- a/examples/03_joining/0080_interpolation_join.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -Interpolation join: infer missing rows when joining two tables -============================================================== - -We illustrate the :class:`~skrub.InterpolationJoiner`, which is a type of join where -values from the second table are inferred with machine-learning, rather than looked up -in the table. It is useful when exact matches are not available but we have rows that -are close enough to make an educated guess -- in this sense it is a generalization of a -:func:`~skrub.fuzzy_join`. - -The :class:`~skrub.InterpolationJoiner` is therefore a transformer that adds the outputs -of one or more machine-learning models as new columns to the table it operates on. - -In this example we want our transformer to add weather data (temperature, rain, etc.) to -the table it operates on. We have a table containing information about commercial -flights, and we want to add information about the weather at the time and place where -each flight took off. This could be useful to predict delays -- flights are often -delayed by bad weather. - -We have a table of weather data containing, at many weather stations, measurements such -as temperature, rain and snow at many time points. Unfortunately, our weather stations -are not inside the airports, and the measurements are not timed according to the flight -schedule. Therefore, a simple equi-join would not yield any matching pair of rows from -our two tables. Instead, we use the :class:`~skrub.InterpolationJoiner` to *infer* the -temperature at the airport at take-off time. We train supervised -machine-learning models using the weather table, then query them with the times -and locations in the flights table. - -""" - -###################################################################### -# Load weather data -# ----------------- -# We join the table containing the measurements to the table that contains the weather -# stations’ latitude and longitude. We subsample these large tables for the example to -# run faster. - -import pandas as pd - -from skrub.datasets import fetch_flight_delays - -dataset = fetch_flight_delays() -weather = pd.read_csv(dataset.weather_path) -weather = weather.sample(100_000, random_state=0, ignore_index=True) -stations = pd.read_csv(dataset.stations_path) -weather = stations.merge(weather, on="ID")[ - ["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY", "TMAX", "PRCP", "SNOW"] -] -weather["YEAR/MONTH/DAY"] = pd.to_datetime(weather["YEAR/MONTH/DAY"]) - -###################################################################### -# The ``'TMAX'`` is in tenths of degree Celsius -- a ``'TMAX'`` of 297 means the maximum -# temperature that day was 29.7℃. We convert it to degrees for readability - -weather["TMAX"] /= 10 - -###################################################################### -# InterpolationJoiner with a ground truth: joining the weather table on itself -# ---------------------------------------------------------------------------- -# As a first simple example, we apply the :class:`~skrub.InterpolationJoiner` in a -# situation where the ground truth is known. We split the weather table in half and join -# the second half on the first half. Thus, the values from the right side table of the -# join are inferred, whereas the corresponding columns from the left side contain the -# ground truth and we can compare them. - -n_main = weather.shape[0] // 2 -main_table = weather.iloc[:n_main] -main_table.head() - -###################################################################### -aux_table = weather.iloc[n_main:] -aux_table.head() - - -###################################################################### -# Joining the tables -# ------------------ -# Now we join our two tables and check how well the :class:`~skrub.InterpolationJoiner` -# can reconstruct the matching rows that are missing from the right side table. To avoid -# clashes in the column names, we use the ``suffix`` parameter to append ``"predicted"`` -# to the right side table column names. - -from skrub import InterpolationJoiner - -joiner = InterpolationJoiner( - aux_table, - key=["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY"], - suffix="_predicted", -).fit(main_table) -join = joiner.transform(main_table) -join.head() - -###################################################################### -# Comparing the estimated values to the ground truth -# -------------------------------------------------- - -from matplotlib import pyplot as plt - -join = join.sample(2000, random_state=0, ignore_index=True) -fig, axes = plt.subplots( - 3, - 1, - figsize=(5, 9), - gridspec_kw={"height_ratios": [1.0, 0.5, 0.5]}, - layout="compressed", -) -for ax, col in zip(axes.ravel(), ["TMAX", "PRCP", "SNOW"]): - ax.scatter( - join[col].values, - join[f"{col}_predicted"].values, - alpha=0.1, - ) - ax.set_aspect(1) - ax.set_xlabel(f"true {col}") - ax.set_ylabel(f"predicted {col}") -plt.show() - -###################################################################### -# We see that in this case the interpolation join works well for the temperature, but -# not precipitation nor snow. So we will only add the temperature to our flights table. - -aux_table = aux_table.drop(["PRCP", "SNOW"], axis=1) - -###################################################################### -# Loading the flights table -# ------------------------- -# We load the flights table and join it to the airports table using the flights’ -# ``'Origin'`` which refers to the departure airport’s IATA code. We use only a subset -# to speed up the example. - -flights = pd.read_csv(dataset.flights_path) -flights["Year_Month_DayofMonth"] = pd.to_datetime(flights["Year_Month_DayofMonth"]) -flights = flights[["Year_Month_DayofMonth", "Origin", "ArrDelay"]] -flights = flights.sample(20_000, random_state=0, ignore_index=True) -airports = pd.read_csv(dataset.airports_path)[ - ["iata", "airport", "state", "lat", "long"] -] -flights = flights.merge(airports, left_on="Origin", right_on="iata") -# printing the first row is more readable than the head() when we have many columns -flights.iloc[0] - -###################################################################### -# Joining the flights and weather data -# ------------------------------------ -# As before, we initialize our join transformer with the weather table. Then, we use it -# to transform the flights table -- it adds a ``'TMAX'`` column containing the predicted -# maximum daily temperature. -# - -joiner = InterpolationJoiner( - aux_table, - main_key=["lat", "long", "Year_Month_DayofMonth"], - aux_key=["LATITUDE", "LONGITUDE", "YEAR/MONTH/DAY"], -) -join = joiner.fit_transform(flights) -join.head() - -###################################################################### -# Sanity checks -# ------------- -# This time we do not have a ground truth for the temperatures. -# We can perform a few basic sanity checks. - -state_temperatures = join.groupby("state")["TMAX"].mean().sort_values() - -###################################################################### -# States with the lowest average predicted temperatures: Alaska, Montana, North Dakota, -# Washington, Minnesota. -state_temperatures.head() - -###################################################################### -# States with the highest predicted temperatures: Puerto Rico, Virgin Islands, Hawaii, -# Florida, Louisiana. -state_temperatures.tail() - -###################################################################### -# Higher latitudes (farther up north) are colder -- the airports in this dataset are in -# the United States. -fig, ax = plt.subplots() -ax.scatter(join["lat"], join["TMAX"]) -ax.set_xlabel("Latitude (higher is farther north)") -ax.set_ylabel("TMAX") -plt.show() - -###################################################################### -# Winter months are colder than spring -- in the north hemisphere January is colder than -# April -# - -import seaborn as sns - -join["month"] = join["Year_Month_DayofMonth"].dt.strftime("%m %B") -plt.figure(layout="constrained") -sns.barplot(data=join.sort_values(by="month"), y="month", x="TMAX") -plt.show() - -###################################################################### -# Of course these checks do not guarantee that the inferred values in our ``join`` -# table’s ``'TMAX'`` column are accurate. But at least the -# :class:`~skrub.InterpolationJoiner` seems to have learned a few reasonable trends from -# its training table. - - -###################################################################### -# Conclusion -# ---------- -# We have seen how to fit an :class:`~skrub.InterpolationJoiner` transformer: we give it -# a table (the weather data) and a set of matching columns (here date, latitude, -# longitude) and it learns to predict the other columns’ values (such as the max daily -# temperature). Then, it transforms tables by *predicting* values that a matching row -# would contain, rather than by searching for an actual match. It is a generalization of -# the :func:`~skrub.fuzzy_join`, as :func:`~skrub.fuzzy_join` is the same thing as an -# :class:`~skrub.InterpolationJoiner` where the estimators are 1-nearest-neighbor -# estimators. diff --git a/examples/03_joining/GALLERY_HEADER.rst b/examples/03_joining/GALLERY_HEADER.rst deleted file mode 100644 index 3b3de2857..000000000 --- a/examples/03_joining/GALLERY_HEADER.rst +++ /dev/null @@ -1,2 +0,0 @@ -Joining tables with imperfect data -================================== diff --git a/examples/GALLERY_HEADER.rst b/examples/GALLERY_HEADER.rst deleted file mode 100644 index bac945d55..000000000 --- a/examples/GALLERY_HEADER.rst +++ /dev/null @@ -1,2 +0,0 @@ -Examples -======== diff --git a/pyproject.toml b/pyproject.toml index 903efa466..f3b04a79e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -227,8 +227,8 @@ python = "~=3.11.0" python = "~=3.14.0" [tool.pixi.feature.doc.tasks] -build-doc = { cmd = "make html && make install-docs", cwd = "doc" } -build-doc-quick = { cmd = "make html-noplot && make install-docs", cwd = "doc" } +build-doc = { cmd = "make html", cwd = "doc" } +build-doc-quick = { cmd = "make html-noplot", cwd = "doc" } clean-doc = { cmd = "make clean", cwd = "doc" } linkcheck = { cmd = "make linkcheck", cwd = "doc" } linkcheck-quick = { cmd = "make linkcheck-noplot", cwd = "doc" } diff --git a/skrub/_docs/CHANGES.rst b/skrub/_docs/CHANGES.rst deleted file mode 100644 index 0ba11a7bf..000000000 --- a/skrub/_docs/CHANGES.rst +++ /dev/null @@ -1,1632 +0,0 @@ -.. _changes: - -=============== -Release history -=============== - -.. currentmodule:: skrub - -Ongoing development -=================== - -New Features ------------- -- New methods :meth:`SkrubLearner.get_named_params` and - :meth:`SkrubLearner.set_named_params` allow getting and setting the outcomes for - choices contained in the DataOp, keyed by choice name. It provides a more - robust way of transferring selected hyperparameters from one DataOp to a - different one than :meth:`SkrubLearner.get_params` and - :meth:`SkrubLearner.set_params`. - :pr:`2090` by :user:`Jérôme Dockès `. -- A parameter ``becomes_default`` has been added to :func:`var`. It allows - indicating that the provided preview ``value`` should also be treated as a - default value for this variable in all contexts (for example in a - SkrubLearner's method like ``fit`` or ``predict``). - :pr:`2082` by :user:`Jérôme Dockès `. -- It is now possible to attach new preview values to the variables in a DataOp - with :meth:`DataOp.skb.set_data`. :pr:`2081` by - :user:`Jérôme Dockès `. -- :class:`DataOp` objects have a new attribute :attr:`DataOp.skb.id` which - provides an alternative for referring to a node, in the environment passed to - :meth:`DataOp.skb.eval`, :meth:`SkrubLearner.predict`, etc., or in - :meth:`DataOp.skb.find` or :meth:`SkrubLearner.truncated_after`. :pr:`2062` by - :user:`Jérôme Dockès `. -- The :class:`DropSimilar` transformer has been added, for removing columns in a - dataframe that present high correlation with other columns. :pr:`2023` by - :user:`Eloi Massoulié `. -- :class:`ToFloat32` now allows users to specify ``decimal`` and ``thousand`` - separators to parse numerical columns that use formatting different from the default - formatting used in Python, such as ``1'234,5``. - Additionally, negative numbers indicated with parentheses can be converted to the - regular numeric format (``(432)`` becomes ``-432``). :pr:`1772` by :user:`Gabriela - Gómez Jiménez `. -- :meth:`TableReport.json` now includes histogram data for numeric and datetime - columns (the bin count and edges, and numbers of low and high outliers). Now - ``json()`` contains all the information shown in the report html rendering, - including the plots. :pr:`2164` by :user:`Jérôme Dockès `. - -Changes -------- -- Grouped Examples into subject-specific sections. :pr:`2102` by - :user:`Maureen Githaiga `. -- :meth:`choose_from` now transparently converts `outcomes` to a list when it is - another type of sequence. :pr:`2100` by :user:`aidbar `. -- An unnecessary warning that was raised when passing a numpy array to the - TableVectorizer has been removed. :pr:`1908` by - :user:`Sandrine Henry `. -- Improving the association tab error message when only one column is present - :pr:`2094` by :user:`Alicja Kosak `. -- Added support for numpy arrays in :meth:`DataOp.skb.concat`. - :pr:`2096` by :user:`Ayesha Siddiqua `. -- The :class:`TableReport` can now be exported in markdown format with ``.markdown``. - :pr:`2048` by :user:`Riccardo Cappuzzo `. -- The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1. - :pr:`2159` by :user:`Riccardo Cappuzzo `. -- The package build has been updated and improved to reduce its size and include the - user guide and examples with the package, so that it is now possible to access - it directly from the wheel rather than having to rely on the online docs. - :pr:`2173` by :user:`Riccardo Cappuzzo `. - -Bugfixes --------- -- A bug in how the :class:`TableVectorizer` and :class:`Cleaner` treated columns - duration columns in pandas and polars has been fixed. Now, both classes convert - durations to the total number of seconds (with fractional part). This is done - by the new transformer :class:`DurationToFloat`. :pr:`2069` by - :user:`Riccardo Cappuzzo `. -- An error that could arise when running ``TableReport`` on dataframes containing - double dollar (``$$``) signs has been fixed. - :pr:`2154` by :user:`Katerina Michenina `, - :user:`CecilyTS `, :user:`Eve Rabin `. - -Deprecations ------------- - -- The parameter ``order_by`` of :class:`TableReport` is deprecated. Passing - ``order_by`` now emits a :class:`DeprecationWarning` - :pr:`2101` by :user:`Heidi Koivisto `. - - -Release 0.9.0 -============= - -New Features ------------- -- It is now possible to pass additional (dynamically computed) arguments to the - scorers used by :class:`DataOp` objects for validation, hyperparameter search - etc. For example, sample weights. This is achieved by passing the scorers and - their arguments to :meth:`DataOp.skb.with_scoring`. :pr:`1995` by - :user:`Jérôme Dockès `. -- The diagrams displayed in notebooks for :class:`SkrubLearner`, - :class:`ParamSearch` and :class:`OptunaParamSearch` have been improved and now - display the :class:`DataOp` they contain. :pr:`2024` by :user:`Jérôme Dockès - `. -- The method :meth:`DataOp.skb.find` can find a node by name (or by a callable - predicate) in a DataOp. The method :meth:`DataOp.skb.find_X_y` finds the nodes - marked with :meth:`DataOp.skb.mark_as_X` and :meth:`DataOp.skb.mark_as_y`, and - the ``cv`` splitter and ``split_kwargs`` passed to - :meth:`DataOp.skb.mark_as_X`, if they exist. :pr:`2041` - by :user:`Jérôme Dockès `. -- :func:`selectors.has_dtype` has been added, allowing users to select columns - by passing the dtype objects they want to match. :pr:`2027` by - :user:`kudos07 `. -- A new dataframe generator, :func:`datasets.toy_cities`, has been added for - use cases on dataframes with variable sizes and variable correlation between - columns. :pr:`2042` by :user:`Eloi Massoulié `. -- A new selector function, :func:`selectors.drop`, has been added to drop columns - from a dataframe using a selector. It mirrors the behavior of :func:`selectors.select`. - :pr:`2108` by :user:`Mary Njoroge `. - -Changes -------- -- :class:`TableReport` now accepts ``plot_distributions`` and - ``compute_associations`` parameters (``True``, ``False``, or ``"auto"``) - to explicitly control whether distribution plots and pairwise associations - are computed. The threshold parameters controlling the maximum number of - columns for which these are computed have been renamed to - ``plots_threshold`` and ``associations_threshold`` for clarity. - :pr:`1907` by :user:`JulietteBgl `. -- The row indices of training and testing samples are now also included in the - dictionaries produced by :meth:`DataOp.skb.iter_cv_splits`. :pr:`2012` by - :user:`Jérôme Dockès `. -- The :class:`Cleaner` now exposes a ``parse_numbers`` boolean parameter to - control whether numeric-looking strings (e.g., ``["1", "2", "3"]``) are parsed - to ``float32``, and a ``cast_to_float`` parameter to downcast numeric - columns to ``float32``. - :pr:`1910` by :user:`Varshith-yadaV `. -- :func:`~datasets.fetch_toxicity` now returns a shuffled version of the dataset by default. - :pr:`1892` by :user:`Riccardo Cappuzzo `. -- Added a ``metric`` parameter to :func:`fuzzy_join` and :class:`Joiner` to configure - the nearest-neighbor distance used for matching. The metric can be any value - supported by :class:`~sklearn.neighbors.NearestNeighbors` (see its docstring). - :pr:`1861` by :user:`Saba Siddique `. -- :class:`ApplyToCols` now accepts an ``exclude_cols`` parameter, making it - possible to transform the columns selected by ``cols`` except for an - explicit subset, mirroring :meth:`DataOp.skb.apply`. - :pr:`2039` by :user:`Saba Siddique `. -- In python versions >= 3.11, :class:`ApplyToCols` now produces better error - tracebacks when the wrapped transformer fails, . :pr:`1979` by :user:`Jérôme - Dockès `. -- The parameter ``how`` of :meth:`DataOp.skb.apply` is replaced by a simpler - Boolean parameter ``no_wrap``. :pr:`2049` by :user:`Jérôme Dockès - `. -- The ``exclude_cols`` of :meth:`DataOp.skb.apply` can now be a DataOp. - :pr:`2050` by :user:`Jérôme Dockès `. -- Skrub estimators now correctly show links to the documentation in the HTML - representation that is generated for notebooks. :pr:`2036` by :user:`Riccardo - Cappuzzo `. - -Bugfixes --------- -- An error that could arise when calling ``score`` on a ``SkrubLearner`` that - contains an inner transformer that has a ``score`` method has been fixed. - :pr:`2052` by :user:`Jérôme Dockès `. - -Deprecations ------------- -- The parameter ``numeric_dtype`` in the :class:`Cleaner` has been deprecated in - favor of ``cast_to_float`` in :pr:`1910`. -- The parameter ``drop_if_unique`` of :class:`Cleaner` and :class:`DropUninformative` - has been deprecated. :pr:`2040` by :user:`Riccardo Cappuzzo `. -- The parameters ``max_plot_columns`` and ``max_association_columns`` of the - :class:`TableReport` have been deprecated in favor of ``plot_distributions`` - and ``compute_associations``. :pr:`1907`. - -Release 0.8.0 -============= - -New Features ------------- -- The ``eager_data_ops`` :ref:`configuration - ` option has been added. When set to - False, no previews are computed and validation is deferred until the DataOp is - actually used (e.g. with ``.skb.eval()``) rather than as soon as it is - defined. This can make the definition of complex DataOps with many nodes - faster (the overhead it removes typically becomes noticeable only in DataOps - with 50-100 nodes or more). Moreover, the evaluation of large DataOps has also - become faster. :pr:`1890` by :user:`Jérôme Dockès `. -- The reports produced by :meth:`DataOp.skb.full_report` and - :meth:`SkrubLearner.report` now also display the values provided in the - environment. :pr:`1920` by :user:`Jérôme Dockès `. -- :class:`SkrubLearner`, :class:`ParamSearch` and :class:`OptunaParamSearch` expose - some more attributes for inspection by scikit-learn: ``__sklearn_tags__``, - ``classes_``, ``_estimator_type``. :pr:`1931` by :user:`Jérôme Dockès - `. -- It is now possible to pass additional (dynamically computed) arguments to the - cross-validation splitter used by :class:`DataOp` objects for validation, - hyperparameter search etc. For example, the groups for a - :class:`sklearn.model_selection.GroupKFold` can be computed as part of the - DataOp evaluation and used for splitting. This is achieved by passing the - splitter and its arguments to :meth:`DataOp.skb.mark_as_X`. :pr:`1943` by - :user:`Jérôme Dockès `. -- :func:`selectors.has_nulls` now takes a ``proportion`` parameter, which allows - selecting columns that have a fraction of null values above the given threshold. - :pr:`1881` by :user:`Gabriela Gómez Jiménez `. - - -Changes -------- -- Increased the minimum version of polars from 0.20 to 1.5.0. - :pr:`1897` by :user:`Riccardo Cappuzzo `. -- ``ApplyToCols`` and ``ApplyToFrame`` have been merged into a single class, - :class:`ApplyToCols`,that covers the functionality of both the old classes by - detecting automatically whether the provided transformer should be applied - independently on each column, or on all selected columns as a single dataframe. - As a result, ``ApplyToCols`` and ``ApplyToFrame`` have been removed. - :pr:`1913`, :pr:`1919` and :pr:`1962` by :user:`Riccardo Cappuzzo `. -- The dataset fetcher functions now include a "path" field for each table in the dataset. - For example, the dataset "employee_salaries" now has the field ``employee_salaries_path``. - Additionally, datasets that include a single table have the field ``path``. These - fields contain the paths to the datasets stored in the ``skrub_data`` folder. - The default ``skrub_data`` folder can now be set in the skrub configuration and by setting - the ``SKB_DATA_DIRECTORY`` environment variable. The environment variable ``SKRUB_DATA_DIRECTORY`` - is deprecated and will be removed in a future version of skrub. - :pr:`1852` by :user:`Riccardo Cappuzzo`. Examples in the gallery have - been updated accordingly in :pr:`1940` and :pr:`1964` by :user:`MuditAtrey `. -- :class:`~skrub.core.SingleColumnTransformer` and associated exception - :class:`~skrub.core.RejectColumn` (used internally by many skrub estimators) have - been added to the public API, in the newly-created ``skrub.core`` module. - :pr:`1851` by :user:`Eloi Massoulié `. -- Added the strings ``"None"`` and ``"none"`` to the list of null string values in - :class:`Cleaner`. Also, exposed the list of null string values that will be set - to null by the :class:`Cleaner` as the parameter ``null_strings``. - :pr:`1952` and :pr:`1954` by :user:`Lisa McBride `. -- The configuration parameter "use_table_report" has been removed from the skrub - configuration. Use :meth:`patch_display` instead. - :pr:`1973` by :user:`Riccardo Cappuzzo`. -- Updated how the ``column_filters`` parameter of :class:`TableReport` works. - It now accepts a dictionary where the key is the display name for the - dropdown menu, and the value is a filter of the columns that will be displayed. - Accepts either a list of column indices, a list of column names - or an instance of the :class:`Selector`. - :pr:`1976` by :user:`Lisa McBride `. -- The overplotting of the counts atop the vertical histogram bars in the - :class:`TableReport` has been removed due to formatting issues. - :pr:`1984` by :user:`Lisa McBride`. -- The maximum number of associations that can be displayed in the - :class:`TableReport` has been increased to N=1000, and the associations - are now displayed in a scrollable table. - :pr:`1992` by :user:`Lisa McBride`. - -Bug Fixes --------- -- The :class:`TableVectorizer` now correctly handles the case where one of the - provided encoders is a scikit-learn Pipeline that starts with a skrub - single-column transformer. :pr:`1899` by :user:`Jérôme Dockès ` - and :pr:`1900` by :user:`Jérôme Dockès `. -- Errors raised when a polars LazyFrame is passed where an eager DataFrame is - expected are now clearer. :pr:`1916` by :user:`Jérôme Dockès `. -- :meth:`DataOp.skb.cross_validate` would raise an error when passed - ``return_indices=True``. Now it returns the train and test indices of each - fold in the ``train_indices`` and ``test_indices`` columns of the result - dataframe. :pr:`1953` by :user:`Jérôme Dockès `. -- Polars LazyFrames are no longer collected automatically anywhere in the library; - a ``TypeError`` is now raised instead. - :pr:`1941` by :user:`Mudit Atrey `. - - -Release 0.7.2 -============= - -Changes -------- -- The :class:`StringEncoder` now exposes the ``vocabulary`` parameter from the parent - :class:`TfidfVectorizer`. - :pr:`1819` by :user:`Eloi Massoulié ` -- :func:`compute_ngram_distance` has been renamed to :func:`_compute_ngram_distance` and is now a private function. - :pr:`1838` by :user:`Siddharth Baleja `. - -Bugfixes --------- -- Fixed some issues related to the release of Pandas 3.0. :pr:`1855` by :user:`Riccardo Cappuzzo `. - -Release 0.7.1 -============= - -New features ------------- -- A new dataset, :func:`fetch_california_housing`, has been added to the - :mod:`skrub.datasets` module. It allows to get a redundancy copy of the scikit-learn - :func:`fetch_california_housing` function. - :pr:`1830` by :user:`Guillaume Lemaitre `. - -Bugfixes --------- -- :class:`DropCols` and :class:`SelectCols:` attributes were renamed to end - with an underscore, in order to follow a scikit-learn convention which is - used to determine if an estimator is fitted. :pr:`1813` by :user:`Auguste - Baum `. - -Release 0.7.0 -============= - -New features ------------- -- It is now possible to tune the choices in a :class:`DataOp` with `Optuna - `_. See - :ref:`example_optuna_choices` for an example. - :pr:`1661` by :user:`Jérôme Dockès `. -- :meth:`DataOp.skb.apply` now allows passing extra named arguments to the - estimator's methods through the parameters ``fit_kwargs``, ``predict_kwargs`` - etc. :pr:`1642` by :user:`Jérôme Dockès `. -- TableReport now displays the mean statistic for boolean columns. - :pr:`1647` by :user:`Abdelhakim Benechehab `. -- :meth:`DataOp.skb.get_vars` allows inspecting all the variables, or all the - named dataops, in a :class:`DataOp`. This lets us easily know what keys should - be present in the ``environment`` dictionary we pass to - :meth:`DataOp.skb.eval` or to :meth:`SkrubLearner.fit`, - :meth:`SkrubLearner.predict`, etc. - :pr:`1646` by :user:`Jérôme Dockès `. -- :meth:`DataOp.skb.iter_cv_splits` iterates over the training and testing - environments produced by a CV splitter -- similar to - :meth:`DataOp.skb.train_test_split` but for multiple cross-validation splits. - :pr:`1653` by :user:`Jérôme Dockès `. -- :class:`TableReport` now supports ``np.array``. :pr:`1676` by :user:`Nisma Amjad `. -- :meth:`DataOp.skb.full_report` now accepts a new parameter, ``title``, that is displayed - in the html report. - :pr:`1654` by :user:`Marie Sacksick `. -- :class:`TableReport` now includes the ``open_tab`` parameter, which lets the - user select which tab should be opened when the ``TableReport`` is - rendered. :pr:`1737` by :user:`Riccardo Cappuzzo`. -- :class:`selectors.Selector` now has documentation for its :meth:`selectors.Selector.expand` - and :meth:`selectors.Selector.expand_index` methods, with added information and examples - in the user guide, as well as mentions in the corresponding constructor functions. - :pr:`1841` by :user:`Eloi Massoulié`. - -Changes -------- -- The minimum supported version of Python has been increased to 3.10. Additionally, - the minimum supported versions of scikit-learn and requests are 1.4.2 and 2.27.1 - respectively. Support for python 3.14 has been added. - :pr:`1572` by :user:`Riccardo Cappuzzo`. -- The :meth:`DataOp.skb.full_report` method now deletes reports created with - ``output_dir=None`` after 7 days. :pr:`1657` by :user:`Simon Dierickx `. -- The :func:`tabular_pipeline` uses a :class:`SquashingScaler` instead of a - :class:`StandardScaler` for centering and scaling numerical features - when linear models are used. - :pr:`1644` by :user:`Simon Dierickx ` -- The transformer :class:`ToFloat`, previously called ``ToFloat32``, is now public. - :pr:`1687` by :user:`Marie Sacksick `. -- Improved the error message raised when a Polars lazyframe is passed to - :class:`TableReport`, clarifying that ``.collect()`` must be called first. - :pr:`1767` by :user:`Fatima Ben Kadour `. -- Computing the associations in :class:`TableReport` is now deterministic and can - be controlled by the new parameter ``subsampling_seed`` of the global configuration. - :pr:`1775` by :user:`Thomas S. `. -- Added ``cast_to_str`` parameter to :class:`Cleaner` to prevent unintended - conversion of list/object-like columns to strings unless explicitly enabled. - :pr:`1789` by :user:`PilliSiddharth`. - -Bugfixes --------- -- The :meth:`skrub.cross_validate` function now raises a specific exception if the wrong variable - type is passed. - :pr:`1799` by :user:`Eloi Massoulié` -- Fixed various issues with some transformers by adding ``get_feature_names_out`` - to all single column transformers. - :pr:`1666` by :user:`Riccardo Cappuzzo`. -- Issues occurring when :meth:`DataOp.skb.apply` was passed a DataOp as the - estimator have been fixed in :pr:`1671` by :user:`Jérôme Dockès - `. -- :class:`TableReport` could raise an error while trying to check if Polars - columns with some dtypes (lists, structs) are sorted. It would not indicate - Polars columns sorted in descending order. Fixed in :pr:`1673` by - :user:`Jérôme Dockès `. -- Fixed nightly checks and added support for upcoming library versions, including Pandas - v3.0. :pr:`1664` by :user:`Auguste Baum ` and - :user:`Riccardo Cappuzzo `. -- Fixed the use of :class:`TableReport` and :class:`Cleaner` with Polars dataframes - containing a column with empty string as name. - :pr:`1722` by :user:`Marie Sacksick `. -- Fixed an issue where :class:`TableReport` would fail when computing associations - for Polars dataframes if PyArrow was not installed. - :pr:`1742` by :user:`Riccardo Cappuzzo `. -- Fixed an issue in the Data Ops report generation in cases where the DataOp - contained escape characters or were spanning multiple lines. - :pr:`1764` by :user:`Riccardo Cappuzzo `. -- Added :meth:`get_feature_names_out` to :class:`Cleaner` for consistency with the - :class:`TableVectorizer` and other transformers. :pr:`1762` by - :user:`Riccardo Cappuzzo `. -- Improve error message when :class:`TextEncoder` is used without the optional - transformers dependencies. :pr:`1769` by :user:`Fangxuan Zhou `. -- Accessing ``.skb.applied_estimator`` on a :class:`DataOp` after calling - ``.skb.set_name()``, ``.skb.set_description()``, ``.skb.mark_as_X()`` or - ``.skb.mark_as_y()`` used to raise an error, this has been fixed in :pr:`1782` - by :user:`Jérôme Dockès `. -- Fixed potential issues that could arise in :meth:`ParamSearch.plot_results` - when NaN values were present in the cross-validation results. - :pr:`1800` by :user:`Riccardo Cappuzzo `. - -Release 0.6.2 -============= - -New features ------------- -- The :meth:`DataOp.skb.full_report` now displays the time each node took to - evaluate. :pr:`1596` by :user:`Jérôme Dockès `. - -Changes -------- -- Ken embeddings are now deprecated, the functions :func:`datasets.get_ken_embeddings`, - :func:`datasets.get_ken_table_aliases`, and :func:`datasets.get_ken_types` will be - removed in the next release of skrub. - :pr:`1546` by :user:`Vincent Maladiere `. -- Improved error messages when a DataOp is being sent to dispatched functions. - :pr:`1607` by :user:`Riccardo Cappuzzo`. -- The accepted values for the parameter ``how`` of :meth:`DataOp.skb.apply` have - changed. The new values are ``"auto"`` (unchanged), ``"cols"`` to wrap the - transformer in :class:`ApplyToCols`, ``"frame"`` to wrap the transformer in - :class:`ApplyToFrame`, or ``"no_wrap"`` for no wrapping. The old values are - deprecated and will result in an error in a future release. - :pr:`1628` by :user:`Jérôme Dockès `. -- The parameter ``splitter`` of :meth:`DataOp.skb.train_test_split` has been - renamed ``split_func``. :pr:`1630` by :user:`Jérôme Dockès `. -- KEN embeddings and all the relevant functions have been removed from skrub. - :pr:`1567` by :user:`Riccardo Cappuzzo`. -- The objects ``tabular_learner`` and ``DropIfTooManyNulls`` were removed. Use - :func:`tabular_pipeline` and :class:`DropUninformative` instead. - :pr:`1567` by :user:`Riccardo Cappuzzo`. -- The skrub global configuration now includes a parameter for setting the default - verbosity of the :class:`TableReport`. - :pr:`1567` by :user:`Riccardo Cappuzzo`. - -Bugfixes --------- - -- Fixed a compatibility bug with Polars 1.32.3 that may cause `ToFloat32` to fail - when applied to categorical columns. :pr:`1570` by :user:`Riccardo Cappuzzo`. -- Fixed the display of DataOp objects in google colab cell outputs (no output - was displayed). :pr:`1590` by :user:`Jérôme Dockès `. -- Fixed an error that occurred when using ``.skb.concat`` with a pandas dataframe - with column names that aren't strings. :pr:`1594` by :user:`Riccardo Cappuzzo`. -- Fixed the range from which :func:`choose_float` and :func:`choose_int` sample - values when ``log=False`` and ``n_steps`` is ``None``. It was between ``low`` - and ``low + high``, now it is between ``low`` and ``high``. :pr:`1603` by - :user:`Jérôme Dockès `. -- DataOp hyperparameter search would raise an error when doing classification - and using the ``scoring`` parameter, when the dataop contained no variables. - Fixed in :pr:`1601` by :user:`Jérôme Dockès `. -- :class:`SkrubLearner` used to do a prediction on the train set during - ``fit()``, this has been fixed. - :pr:`1610` by :user:`Jérôme Dockès `. -- :class:`DataOp` would raise errors when containing subclasses of list, tuple - or dict that cannot be initialized with an instance of the builtin type (such - as classes created by ``collections.namedtuple``), this has been fixed. - DataOps now only recurse into the builtin collections to evaluate their items - (not into their subclasses). If you need the items evaluated (ie if they - contain DataOps or Choices), store them in one of the builtin collections. - :pr:`1612` by :user:`Jérôme Dockès `. -- :meth:`SkrubLearner.report` with ``mode="fit"`` used to display the dataops - themselves, rather than their outputs, in the report. This has been fixed in - :pr:`1623` by :user:`Jérôme Dockès `. -- Fixed a bug that happened when ``get_feature_names_out`` was called on instances - of the :class:`DatetimeEncoder`. :pr:`1622` by :user:`Riccardo Cappuzzo`. - -Release 0.6.1 -=================== - -Bugfixes --------- - -- ``get_feature_names_out`` now works correctly when used by :class:`GapEncoder`, - :class:`DropCols`, :class:`SelectCols:` from within a scikit-learn ``Pipeline``. In - addition, :class:`DropCols`'s ``get_feature_names_out`` method now returns the - names of the columns that are not dropped, rather than the names of the columns - that are dropped. :pr:`1543` by :user:`Riccardo Cappuzzo`. - - -Release 0.6.0 -============= - -Highlights ----------- -- Major feature! Skrub DataOps are a powerful new way of - combining dataframe transformations over multiple tables, and machine learning - pipelines. DataOps can be combined to form compled data plans, that can be used - to train and tune machine learning models. Then, the DataOps plans can be exported - as ``Learners`` (:class:`skrub.SkrubLearner`), standalone objects that can be - used on new data. More detail about the DataOps can be found in the - :ref:`User guide ` and in the - :ref:`examples `. - -- The :class:`TableReport` has been improved with many new features. Series are - now supported directly. It is now - possible to skip computing column associations and generating plots when the - number of columns in the dataframe exceeds a user-defined threshold. Columns with - high cardinality and sorted columns are now highlighted in the report. - -- :mod:`selectors`, :class:`ApplyToCols` and :class:`ApplyToFrame` are now available, - providing utilities for selecting columns to which a transformer should be applied - in a flexible way. For more details, see the :ref:`User guide ` - and the :ref:`example `. - -- The :class:`SquashingScaler` has been added: it robustly rescales and smoothly - clips numeric columns, enabling more robust handling of numeric columns - with neural networks. See the :ref:`example ` - -New features ------------- - -- The skrub DataOps are new mechanism for building machine-learning - pipelines that handle multiple tables and easily describing their - hyperparameter spaces. Main PR: :pr:`1233` by :user:`Jérôme Dockès `. - Additional work from other contributors can be found - `here `_: - :user:`Vincent Maladiere ` provided very important help by - trying the DataOps on many use-cases and datasets, providing feedback and - suggesting improvements, improving the examples (including creating all the - figures in the examples) and adding jitter to the parallel coordinate plots, - :user:`Riccardo Cappuzzo` experimented with the DataOps, - suggested improvements and improved the examples, :user:`Gaël Varoquaux - ` , :user:`Guillaume Lemaitre `, :user:`Adrin Jalali - `, :user:`Olivier Grisel ` and others participated - through many discussions in defining the requirements and the public API. - See :ref:`the examples ` for - an introduction. - -- The :mod:`selectors` module provides utilities for selecting columns to which - a transformer should be applied in a flexible way. The module was created in - :pr:`895` by :user:`Jérôme Dockès ` and added to the public API - in :pr:`1341` by :user:`Jérôme Dockès `. - -- The :class:`DropUninformative` transformer is now available. This transformer - employs different heuristics to detect columns that are not likely to bring - useful information for training a model. - The current implementation includes detection of columns that contain only a - single value (constant columns), only missing values, or all unique values (such - as IDs). :pr:`1313` by :user:`Riccardo Cappuzzo`. - -- :func:`get_config`, :func:`set_config` and :func:`config_context` are now available - to configure settings for dataframes display and expressions. :func:`patch_display` - and :func:`unpatch_display` are deprecated and will be removed in the next release - of skrub. :pr:`1427` by :user:`Vincent Maladiere `. - The global configuration includes the parameter ``cardinality_threshold`` that - controls the threshold value used to warn user if they have high cardinality - columns in their dataset. :pr:`1498` by :user:`rouk1 `. - Additionally, the parameter ``float_precision`` - controls the number of significant digits displayed for floating-point values - in reports. :pr:`1470` by :user:`George S `. - -- Added the :class:`SquashingScaler`, a transformer that - robustly rescales and smoothly clips numeric columns, - enabling more robust handling of numeric columns - with neural networks. :pr:`1310` by :user:`Vincent Maladiere ` and - :user:`David Holzmüller `. - -- :func:`datasets.toy_order` is now available to create a toy dataframe and - corresponding targets for examples. - :pr:`1485` by :user:`Antoine Canaguier-Durand `. - -- :class:`ApplyToCols` and :class:`ApplyToFrame` are now available to apply transformers - on a set of columns independently and jointly respectively. - :pr:`1478` by :user:`Vincent Maladiere`. - - -Changes -------- -.. warning:: - The default high cardinality encoder for both :class:`TableVectorizer` and - :meth:`tabular_learner` (now :meth:`tabular_pipeline`) has been changed from - :class:`GapEncoder` to :class:`StringEncoder`. :pr:`1354` by - :user:`Riccardo Cappuzzo`. - -- The ``tabular_learner`` function has been deprecated in favor of :func:`tabular_pipeline` to honor - its scikit-learn pipeline cultural heritage, and remove the ambiguity with the data - ops Learner. :pr:`1493` by :user:`Vincent Maladiere `. - -- :class:`StringEncoder` now exposes the ``stop_words`` argument, which is passed to the - underlying vectorizer (:class:`~sklearn.feature_extraction.text.TfidfVectorizer`, - or :class:`~sklearn.feature_extraction.text.HashingVectorizer`). :pr:`1415` by - :user:`Vincent Maladiere `. - -- A new parameter ``max_association_columns`` has been added to the - :class:`TableReport` to skip association computation when the number of columns - exceeds the specified value. :pr:`1304` by :user:`Victoria Shevchenko `. - -- The `packaging` dependency was removed. - :pr:`1307` by :user:`Jovan Stojanovic ` - -- :class:`TextEncoder`, :class:`StringEncoder` and :class:`GapEncoder` now compute the - total standard deviation norm during training, which is a global constant, and - normalize the vector outputs by performing element-wise division on all entries. - :pr:`1274` by :user:`Vincent Maladiere `. - -- The :class:`DropIfTooManyNulls` transformer has been replaced by the - :class:`DropUninformative` transformer and will be removed in a future release. - :pr:`1313` by :user:`Riccardo Cappuzzo` - -- The :func:`concat_horizontal` function was replaced with :func:`concat`. Horizontal or vertical concatenation - is now controlled by the `axis` parameter. :pr:`1334` by :user:`Parasa V Prajwal `. - -- The :class:`TableVectorizer` and :class:`Cleaner` now accept a `datetime_format` - parameter for specifying the format to use when parsing datetime columns. - :pr:`1358` by :user:`Riccardo Cappuzzo`. - -- The :class:`SimpleCleaner` has been removed. use :class:`Cleaner` instead. :pr:`1370` by :user:`Riccardo Cappuzzo`. - -- The periodic encoding for the ``day_in_year`` has been removed from the :class:`DatetimeEncoder` as it was - redundant. The feature itself is still added if the flag is set to ``True``. :pr:`1396` by :user:`Riccardo Cappuzzo`. - -- The naming scheme used for the features generated by :class:`TextEncoder`, :class:`StringEncoder`, :class:`MinHashEncoder`, - :class:`DatetimeEncoder` has been standardized. Now features generated by all encoders have indices in the range - ``[0, n_components-1]``, rather than ``[1, n_components]``. Additionally, columns with empty name are assigned a default - name that depends on the encoder used. :pr:`1405` by :user:`Riccardo Cappuzzo`. - -- The optional dependencies 'dev', 'doc', 'lint' and 'test' have been coalesced into - 'dev'. :pr:`1404` by :user:`Vincent Maladiere `. - -- The :class:`TableReport` now supports Series in addition to Dataframes. :pr:`1420` by :user:`Vitor Pohlenz`. - -- The :class:`Cleaner` now exposes a parameter to convert numeric values to float32. :pr:`1440` by - :user:`Riccardo Cappuzzo`. - -- The :class:`TableReport` now shows if columns are sorted. :pr:`1512` by :user:`Dea María Léon`. - - -Bugfixes --------- -- Fixed a bug that caused the :class:`StringEncoder` and :class:`TextEncoder` to raise an exception if the - input column was a Categorical datatype. :pr:`1401` by :user:`Riccardo Cappuzzo`. - -Documentation -------------- -A large number of improvements to the examples, docstrings, and the documentation -website have been made. Contributors include :user:`Vincent Maladiere `, -:user:`Riccardo Cappuzzo`, :user:`Jérôme Dockès `, -:user:`Gael Varoquaux `, :user:`Gabriela Gómez Jiménez `, -:user:`Sylvain Combettes `, :user:`Frits Hermans `, -:user:`Vitor Pohlenz `, :user:`Arturo Amor Quiroz `, -:user:`Marie Sacksick `, :user:`Emilien Battel `, -:user:`George El Haber `, :user:`Antoine Canaguier-Durand `, and -:user:`Lionel Kusch `. - - -Release 0.5.4 -============= - -Maintenance ------------ -* Make ``skrub`` compatible with scikit-learn 1.7. - :pr:`1434` by :user:`Vincent Maladiere `. - - -Release 0.5.3 -============= - -Changes -------- - -- The :class:`SimpleCleaner` has been renamed to :class:`Cleaner`. Use of the - name :class:`SimpleCleaner` is deprecated and will result in an error in some - future release of skrub. :pr:`1275` by :user:`Riccardo Cappuzzo`. - -- A new parameter ``max_plot_columns`` has been added to the - :class:`TableReport` and :func:`patch_display` to skip column plots when the - number of columns exceeds the specified value. :pr:`1255` by :user:`Priscilla - Baah`. - - -Release 0.5.2 -============= - -New features ------------- - -- The :class:`TableReport` now switches its visual theme between light and dark according to the user preferences. - :pr:`1201` by :user:`rouk1 `. - -- Adding a new way to control the location of the data directory, using envar ``SKRUB_DATA_DIRECTORY``. - :pr:`1215` by :user:`Thomas S. ` - -- The :class:`DatetimeEncoder` now supports periodic encoding of datetime features - with trigonometric functions and B-splines transformers. - :pr:`1235` by :user:`Riccardo Cappuzzo`. - -- The :class:`TableReport` now also compute Pearson's correlation for numeric values. - :pr:`1203` by :user:`Reshama Shaikh ` and - :user:`Vincent Maladiere `. - -- The :class:`SimpleCleaner` is now available (⚠️ it was renamed to - :class:`Cleaner` in skrub ``0.5.3``.). This transformer is a lightweight - pre-processor that applies some of the transformations applied by the - :class:`TableVectorizer`, with a simpler interface. :pr:`1266` by - :user:`Riccardo Cappuzzo` and :user:`Jerome Dockes ` . - -Changes -------- - -- The estimator returned by :func:`tabular_learner` now uses spline encoding of - datetime features when the supervised learner is not a model based on decision - trees such as random forests or gradient boosting. :pr:`1264` by - :user:`Guillaume Lemaitre `. - -- The "distribution" tab of the ``TableReport`` now stacks cards horizontally to avoid adding - vertical space. - :pr:`1259` by :user:`Gaël Varoquaux ` - -- Progress messages when generating a ``TableReport`` are now written to stderr instead of stdout. - :pr:`1236` by :user:`Priscilla Baah` - -- Optimize the :class:`StringEncoder`: lower memory footprint and faster execution in some cases. - :pr:`1248` by :user:`Gaël Varoquaux ` - -Bug fixes ---------- -- :class:`StringEncoder` now works correctly in presence of null values. - :pr:`1224` by :user:`Jérôme Dockès `. - -- The :meth:`TableVectorizer.get_feature_names_out` method now works when used in a - scikit-learn pipeline by exposing the `input_features` parameter. - :pr:`1258` by :user:`Guillaume Lemaitre `. - - -Release 0.5.1 -============= - -New features ------------- -* The :class:`StringEncoder` encodes strings using tf-idf and truncated SVD - decomposition and provides a cheaper alternative to :class:`GapEncoder`. - :pr:`1159` by :user:`Riccardo Cappuzzo`. - -Changes -------- -* New dataset fetching methods have been added: :func:`fetch_videogame_sales`, - :func:`fetch_bike_sharing`, :func:`fetch_flight_delays`, - :func:`fetch_country_happiness`, and removed :func:`fetch_road_safety`. - :pr:`1218` by :user:`Vincent Maladiere ` - -Bug fixes ---------- - -Maintenance ------------ - -Release 0.4.1 -============= - -Changes -------- - -* :class:`TableReport` has `write_html` method. :pr:`1190` by :user:`Mojdeh Rastgoo`. - -* A new parameter ``verbose`` has been added to the :class:`TableReport` to toggle on or off the - printing of progress information when a report is being generated. - :pr:`1182` by :user:`Priscilla Baah`. - -* A parameter ``verbose`` has been added to the :func:`patch_display` to toggle on or off the - printing of progress information when a table report is being generated. - :pr:`1188` by :user:`Priscilla Baah`. - -* :func:`tabular_learner` accepts the alias ``"regression"`` for the option - ``"regressor"`` and ``"classification"`` for ``"classifier"``. - :pr:`1180` by :user:`Mojdeh Rastgoo `. - -Bug fixes ---------- -* Generating a ``TableReport`` could have an effect on the matplotib - configuration which could cause plots not to display inline in jupyter - notebooks any more. This has been fixed in skrub in :pr:`1172` by - :user:`Jérôme Dockès ` and the matplotlib issue can be tracked - `here `_. - -* The labels on bar plots in the ``TableReport`` for columns of object dtypes - that have a repr spanning multiple lines could be unreadable. This has been - fixed in :pr:`1196` by :user:`Jérôme Dockès `. - -* Improve the performance of :func:`deduplicate` by removing some unnecessary - computations. :pr:`1193` by :user:`Jérôme Dockès `. - -Maintenance ------------ -* Make ``skrub`` compatible with scikit-learn 1.6. - :pr:`1169` by :user:`Guillaume Lemaitre `. - -Release 0.4.0 -============= - -Highlights ----------- -* The :class:`TextEncoder` can extract embeddings from a string column with a deep - learning language model (possibly downloaded from the HuggingFace Hub). - -* Several improvements to the :class:`TableReport` such as better support for - other scripts than the latin alphabet in the bar plot labels, smaller report - sizes, clipping the outliers to better see the details of distributions in - histograms. See the full changelog for details. - -* The :class:`TableVectorizer` can now drop columns that contain a fraction of - null values above a user-chosen threshold. - -New features ------------- -* The :class:`TextEncoder` is now available to encode string columns with - diverse entries. - It allows the representation of table entries as embeddings computed by a deep - learning language model. The weights of this model can be fetched locally - or from the HuggingFace Hub. - :pr:`1077` by :user:`Vincent Maladiere `. - -* The :func:`column_associations` function has been added. It computes a - pairwise measure of statistical dependence between all columns in a dataframe - (the same as shown in the :class:`TableReport`). :pr:`1109` by :user:`Jérôme - Dockès `. - -* The :func:`patch_display` function has been added. It changes the display of - pandas and polars dataframes in jupyter notebooks to replace them with a - :class:`TableReport`. This can be undone with :func:`unpatch_display`. - :pr:`1108` by :user:`Jérôme Dockès ` - -Major changes -------------- -* :class:`AggJoiner`, :class:`AggTarget` and :class:`MultiAggJoiner` now require - the `operations` argument. They do not split columns by type anymore, but - apply `operations` on all selected cols. "median" is now supported, "hist" and - "value_counts" are no longer supported. :pr:`1116` by :user:`Théo Jolivet `. - -* The :class:`AggTarget` no longer supports `y` inputs of type list. :pr:`1116` - by :user:`Théo Jolivet `. - -Minor changes -------------- - -* The column filter selection dropdown in the tablereport is smaller and its - label has been removed to save space. :pr:`1107` by :user:`Jérôme Dockès - `. - -* The TableReport now uses the font size of its parent element when inserted - into another page. This makes it smaller in pages that use a smaller font size - than the browser default such as VSCode in some configurations. It also makes - it easier to control its size when inserting it in a web page by setting the - font size of its parent element. A few other small adjustments have also been - made to make it a bit more compact. :pr:`1098` by :user:`Jérôme Dockès - `. - -* Display of labels in the plots of the TableReport, especially for other - scripts than the latin alphabet, has improved. - - - before, some characters could be missing and replaced by empty boxes. - - before, when the text is truncated, the ellipsis "..." could appear on the - wrong side for right-to-left scripts. - - Moreover, when the text contains line breaks it now appears all on one line. - Note this only affects the labels in the plots; the rest of the report did not - have these problems. - :pr:`1097` by :user:`Jérôme Dockès ` - and :pr:`1138` by :user:`Jérôme Dockès `. - -* In the TableReport it is now possible, before clicking any of the cells, to - reach the dataframe sample table and activate a cell with tab key navigation. - :pr:`1101` by :user:`Jérôme Dockès `. - -* The "Column name" column of the "summary statistics" table in the TableReport - is now always visible when scrolling the table. :pr:`1102` by :user:`Jérôme - Dockès `. - -* Added parameter `drop_null_fraction` to `TableVectorizer` to drop columns based - on whether they contain a fraction of nulls larger than the given threshold. - :pr:`1115` and :pr:`1149` by :user:`Riccardo Cappuzzo `. - -* The :class:`TableReport` now provides more helpful output for columns of dtype - TimeDelta / Duration. :pr:`1152` by :user:`Jérôme Dockès `. - -* The :class:`TableReport` now also reports the number of unique values for - numeric columns. :pr:`1154` by :user:`Jérôme Dockès `. - -* The :class:`TableReport`, when plotting histograms, now detects outliers and - clips the range of data shown in the histogram. This allows seeing more detail - in the shown distribution. :pr:`1157` by :user:`Jérôme Dockès `. - -Bug fixes ---------- - -* The :class:`TableReport` could raise an exception when one of the columns - contained datetimes with time zones and missing values; this has been fixed in - :pr:`1114` by :user:`Jérôme Dockès `. - -* In scikit-learn versions older than 1.4 the :class:`TableVectorizer` could - fail on polars dataframes when used with the default parameters. This has been - fixed in :pr:`1122` by :user:`Jérôme Dockès `. - -* The :class:`TableReport` would raise an exception when the input (pandas) - dataframe contained several columns with the same name. This has been fixed in - :pr:`1125` by :user:`Jérôme Dockès `. - -* The :class:`TableReport` would raise an exception when a column contained - infinite values. This has been fixed in :pr:`1150` by :user:`Jérôme Dockès - ` and :pr:`1151` by Jérôme Dockès. - -Release 0.3.1 -============= - -Minor changes -------------- - -* For tree-based models, :func:`tabular_learner` now adds - `handle_unknown='use_encoded_value'` to the `OrdinalEncoder`, to avoid - errors with new categories in the test set. This is consistent with the - setting of `OneHotEncoder` used by default in the - :class:`TableVectorizer`. :pr:`1078` by :user:`Gaël Varoquaux ` - -* The reports created by :class:`TableReport`, when inserted in an html page (or - displayed in a notebook), now use the same font as the surrounding page. - :pr:`1038` by :user:`Jérôme Dockès `. - -* The content of the dataframe corresponding to the currently selected table - cell in the TableReport can be copied without actually selecting the text (as - in a spreadsheet). - :pr:`1048` by :user:`Jérôme Dockès `. - -* The selection of content displayed in the TableReport's copy-paste boxes has - been removed. Now they always display the value of the selected item. When - copied, the repr of the selected item is copied to the clipboard. - :pr:`1058` by :user:`Jérôme Dockès `. - -* A "stats" panel has been added to the TableReport, showing summary statistics - for all columns (number of missing values, mean, etc. -- similar to - ``pandas.info()`` ) in a table. It can be sorted by each column. - :pr:`1056` and :pr:`1068` by :user:`Jérôme Dockès `. - -* The credit fraud dataset is now available with the - :func:`fetch_credit_fraud function`. - :pr:`1053` by :user:`Vincent Maladiere `. - -* Added zero padding for column names in :class:`MinHashEncoder` to improve column ordering consistency. - :pr:`1069` by :user:`Shreekant Nandiyawar `. - -* The selection in the TableReport's sample table can now be manipulated with - the keyboard. :pr:`1065` by :user:`Jérôme Dockès `. - -* The ``TableReport`` now displays the pandas (multi-)index, and has a better - display & interaction of pandas columns when the columns are a MultiIndex. - :pr:`1083` by :user:`Jérôme Dockès `. - -* It is possible to control the number of rows displayed by the TableReport in - the "sample" tab panel by specifying ``n_rows``. - :pr:`1083` by :user:`Jérôme Dockès `. - -* the `TableReport` used to raise an exception when the dataframe contained - unhashable types such as python lists. This has been fixed in :pr:`1087` by - :user:`Jérôme Dockès `. - -* Display's columns name with the HTML representation of the fitted TableVectorizer. - This has been fixed in :pr:`1093` by :user:`Shreekant Nandiyawar `. - -* AggTarget will now work even when y is a Series and not raise any error. - This has been fixed in :pr:`1094` by :user:`Shreekant Nandiyawar `. - -Release 0.3.0 -============= - -Highlights ----------- -* Polars dataframes are now supported across all ``skrub`` estimators. -* :class:`TableReport` generates an interactive report for a dataframe. This - `page `_ regroups some - precomputed examples. - -Major changes -------------- -* The :class:`InterpolationJoiner` now supports polars dataframes. :pr:`1016` - by :user:`Théo Jolivet `. -* The :class:`TableReport` provides an interactive report on a dataframe's - contents: an overview, summary statistics and plots, statistical associations - between columns. It can be displayed in a jupyter notebook, a browser tab or - saved as a static HTML page. :pr:`984` by :user:`Jérôme Dockès `. - -Minor changes -------------- -* :class:`Joiner` and :func:`fuzzy_join` used to raise an error when columns - with the same name appeared in the main and auxiliary table (after adding the - suffix). This is now allowed and a random string is inserted in the duplicate - column to ensure all names are unique. - :pr:`1014` by :user:`Jérôme Dockès `. - -* :class:`AggJoiner` and :class:`AggTarget` could produce outputs whose column - names varied across calls to `transform` in some cases in the presence of - duplicate column names, now the output names are always the same. - :pr:`1013` by :user:`Jérôme Dockès `. - -* In some cases :class:`AggJoiner` and :class:`AggTarget` inserted a column in - the output named "index" containing the pandas index of the auxiliary table. - This has been corrected. - :pr:`1020` by :user:`Jérôme Dockès `. - -Release 0.2.0 -============= - -Major changes -------------- -* The :class:`Joiner` has been adapted to support polars dataframes. :pr:`945` by :user:`Théo Jolivet `. - -* The :class:`TableVectorizer` now consistently applies the same transformation - across different calls to `transform`. There also have been some breaking - changes to its functionality: (i) all transformations are now applied - independently to each column, i.e. it does not perform multivariate - transformations (ii) in ``specific_transformers`` the same column may not be - used twice (go through 2 different transformers). - :pr:`902` by :user:`Jérôme Dockès `. - -* Some parameters of :class:`TableVectorizer` have been renamed: - `high_cardinality_transformer` → `high_cardinality`, - `low_cardinality_transformer` → `low_cardinality`, - `datetime_transformer` → `datetime`, `numeric_transformer` → `numeric`. - :pr:`947` by :user:`Jérôme Dockès `. - -* The :class:`GapEncoder` and :class:`MinHashEncoder` are now a single-column - transformers: their ``fit``, ``fit_transform`` and ``transform`` methods - accept a single column (a pandas or polars Series). Dataframes and numpy - arrays are not accepted. - :pr:`920` and :pr:`923` by :user:`Jérôme Dockès `. - -* Added the :class:`MultiAggJoiner` that allows to augment a main table with - multiple auxiliary tables. :pr:`876` by :user:`Théo Jolivet `. - -* :class:`AggJoiner` now only accepts a single table as an input, and some of its - parameters were renamed to be consistent with the :class:`MultiAggJoiner`. - It now has a ``key``` parameter that allows to join main and auxiliary tables that share - the same column names. :pr:`876` by :user:`Théo Jolivet `. - -* :func:`tabular_learner` has been added to easily create a supervised - learner that works well on tabular data. :pr:`926` by :user:`Jérôme Dockès - `. - -Minor changes -------------- - -* :class:`GapEncoder` and :class:`MinHashEncoder` used to modify their input - in-place, replacing missing values with a string. They no longer do so. Their - parameter `handle_missing` has been removed; now missing values are always - treated as the empty string. - :pr:`930` by :user:`Jérôme Dockès `. - -* The minimum supported python version is now 3.9 - :pr:`939` by :user:`Jérôme Dockès `. - -* Skrub supports numpy 2. :pr:`946` by :user:`Jérôme Dockès `. - -* :func:`~datasets.fetch_ken_embeddings` now add suffix even with the default - value for the parameter `pca_components`. - :pr:`956` by :user:`Guillaume Lemaitre `. - -* :class:`Joiner` now performs some preprocessing (the same as done by the - :class:`TableVectorizer`, eg trying to parse dates, converting pandas object - columns with mixed types to a single type) on the joining columns before - vectorizing them. :pr:`972` by :user:`Jérôme Dockès `. - -skrub release 0.1.1 -=================== - -This is a bugfix release to adapt to the most recent versions of pandas (2.2) and -scikit-learn (1.5). There are no major changes to the functionality of skrub. - - -skrub release 0.1.0 -=================== - - -Major changes -------------- -* :class:`TargetEncoder` has been removed in favor of - :class:`sklearn.preprocessing.TargetEncoder`, available since scikit-learn 1.3. - -* :class:`Joiner` and :func:`fuzzy_join` support several ways of rescaling - distances; ``match_score`` has been replaced by ``max_dist``; bugs which - prevented the Joiner to consistently vectorize inputs and accept or reject - matches across calls to transform have been fixed. :pr:`821` by :user:`Jérôme - Dockès `. - -* :class:`InterpolationJoiner` was added to join two tables by using - machine-learning to infer the matching rows from the second table. - :pr:`742` by :user:`Jérôme Dockès `. - -* Pipelines including :class:`TableVectorizer` can now be grid-searched, since - we can now call `set_params` on the default transformers of :class:`TableVectorizer`. - :pr:`814` by :user:`Vincent Maladiere ` - -* :func:`to_datetime` is now available to support pandas.to_datetime - over dataframes and 2d arrays. - :pr:`784` by :user:`Vincent Maladiere ` - -* Some parameters of :class:`Joiner` have changed. The goal is to harmonize - parameters across all estimator that perform join(-like) operations, as - discussed in `#751 `_. - :pr:`757` by :user:`Jérôme Dockès `. - -* :func:`dataframe.pd_join`, :func:`dataframe.pd_aggregate`, - :func:`dataframe.pl_join` and :func:`dataframe.pl_aggregate` - are now available in the dataframe submodule. - :pr:`733` by :user:`Vincent Maladiere ` - -* :class:`FeatureAugmenter` is renamed to :class:`Joiner`. - :pr:`674` by :user:`Jovan Stojanovic ` - -* :func:`fuzzy_join` and :class:`FeatureAugmenter` can now join on datetime columns. - :pr:`552` by :user:`Jovan Stojanovic ` - -* :class:`Joiner` now supports joining on multiple column keys. - :pr:`674` by :user:`Jovan Stojanovic ` - -* The signatures of all encoders and functions have been revised to enforce - cleaner calls. This means that some arguments that could previously be passed - positionally now have to be passed as keywords. - :pr:`514` by :user:`Lilian Boulard `. - -* Parallelized the :class:`GapEncoder` column-wise. Parameters `n_jobs` and `verbose` - added to the signature. :pr:`582` by :user:`Lilian Boulard ` - -* Introducing :class:`AggJoiner`, a transformer performing - aggregation on auxiliary tables followed by left-joining on a base table. - :pr:`600` by :user:`Vincent Maladiere `. - -* Introducing :class:`AggTarget`, a transformer performing - aggregation on the target y, followed by left-joining on a base table. - :pr:`600` by :user:`Vincent Maladiere `. - -* Added the :class:`SelectCols` and :class:`DropCols` transformers that allow - selecting a subset of a dataframe's columns inside of a pipeline. :pr:`804` by - :user:`Jérôme Dockès `. - - -Minor changes -------------- -* :class:`DatetimeEncoder` doesn't remove constant features anymore. - It also supports an 'errors' argument to raise or coerce errors during - transform, and a 'add_total_seconds' argument to include the number of - seconds since Epoch. - :pr:`784` by :user:`Vincent Maladiere ` - -* Scaling of ``matching_score`` in :func:`fuzzy_join` is now between 0 and 1; it used to be between 0.5 and 1. Moreover, the division by 0 error that occurred when all rows had a perfect match has been fixed. :pr:`802` by :user:`Jérôme Dockès `. - -* :class:`TableVectorizer` is now able to apply parallelism at the column level rather than the transformer level. This is the default for univariate transformers, like :class:`MinHashEncoder`, and :class:`GapEncoder`. - :pr:`592` by :user:`Leo Grinsztajn ` - -* ``inverse_transform`` in :class:`SimilarityEncoder` now works as expected; it used to raise an exception. :pr:`801` by :user:`Jérôme Dockès `. - -* :class:`TableVectorizer` propagate the `n_jobs` parameter to the underlying - transformers except if the underlying transformer already set explicitly `n_jobs`. - :pr:`761` by :user:`Leo Grinsztajn `, :user:`Guillaume Lemaitre `, - and :user:`Jerome Dockes `. - - -* Parallelized the :func:`deduplicate` function. Parameter `n_jobs` - added to the signature. :pr:`618` by :user:`Jovan Stojanovic ` - and :user:`Lilian Boulard ` - -* Functions :func:`datasets.fetch_ken_embeddings`, :func:`datasets.fetch_ken_table_aliases` - and :func:`datasets.fetch_ken_types` have been renamed. - :pr:`602` by :user:`Jovan Stojanovic ` - -* Make `pyarrow` an optional dependencies to facilitate the integration - with `pyodide`. - :pr:`639` by :user:`Guillaume Lemaitre `. - -* Bumped minimal required Python version to 3.10. :pr:`606` by - :user:`Gael Varoquaux ` - -* Bumped minimal required versions for the dependencies: - - numpy >= 1.23.5 - - scipy >= 1.9.3 - - scikit-learn >= 1.2.1 - - pandas >= 1.5.3 :pr:`613` by :user:`Lilian Boulard ` - -* You can now pass column-specific transformers to :class:`TableVectorizer` - using the `specific_transformers` argument. - :pr:`583` by :user:`Lilian Boulard `. - -* Do not support 1-D array (and pandas Series) in :class:`TableVectorizer`. Pass a - 2-D array (or a pandas DataFrame) with a single column instead. This change is for - compliance with the scikit-learn API. - :pr:`647` by :user:`Guillaume Lemaitre ` - -* Fixes a bug in :class:`TableVectorizer` with `remainder`: it is now cloned if it's - a transformer so that the same instance is not shared between different - transformers. - :pr:`678` by :user:`Guillaume Lemaitre ` - -* :class:`GapEncoder` speedup :pr:`680` by :user:`Leo Grinsztajn ` - - - Improved :class:`GapEncoder`'s early stopping logic. The parameters `tol` and `min_iter` - have been removed. The parameter `max_no_improvement` can now be used to control the - early stopping. - :pr:`663` by :user:`Simona Maggio ` - :pr:`593` by :user:`Lilian Boulard ` - :pr:`681` by :user:`Leo Grinsztajn ` - - - Implementation improvement leading to a ~x5 speedup for each iteration. - - - Better default hyperparameters: `batch_size` now defaults to 1024, and `max_iter_e_steps` - to 1. - -* Removed the `most_frequent` and `k-means` strategies from the :class:`SimilarityEncoder`. - These strategy were used for scalability reasons, but we recommend using the :class:`MinHashEncoder` - or the :class:`GapEncoder` instead. :pr:`596` by :user:`Leo Grinsztajn ` - -* Removed the `similarity` argument from the :class:`SimilarityEncoder` constructor, - as we only support the ngram similarity. :pr:`596` by :user:`Leo Grinsztajn ` - -* Added the `analyzer` parameter to the :class:`SimilarityEncoder` to allow word counts - for similarity measures. :pr:`619` by :user:`Jovan Stojanovic ` - -* skrub now uses modern type hints introduced in PEP 585. - :pr:`609` by :user:`Lilian Boulard ` - -* Some bug fixes for :class:`TableVectorizer` ( :pr:`579`): - - - `check_is_fitted` now looks at `"transformers_"` rather than `"columns_"` - - the default of the `remainder` parameter in the docstring is now `"passthrough"` - instead of `"drop"` to match the implementation. - - uint8 and int8 dtypes are now considered as numeric columns. - -* Removed the leading "<" and trailing ">" symbols from KEN entities - and types. - :pr:`601` by :user:`Jovan Stojanovic ` - -* Add `get_feature_names_out` method to :class:`MinHashEncoder`. - :pr:`616` by :user:`Leo Grinsztajn ` - -* Removed `requests` from the requirements. :pr:`613` by :user:`Lilian Boulard ` - -* :class:`TableVectorizer` now handles mixed types columns without failing - by converting them to string before type inference. - :pr:`623`by :user:`Leo Grinsztajn ` - -* Moved the default storage location of data to the user's home folder. - :pr:`652` by :user:`Felix Lefebvre ` and - :user:`Gael Varoquaux ` - -* Fixed bug when using :class:`TableVectorizer`'s `transform` method on - categorical columns with missing values. - :pr:`644` by :user:`Leo Grinsztajn ` - -* :class:`TableVectorizer` never output a sparse matrix by default. This can be changed by - increasing the `sparse_threshold` parameter. :pr:`646` by :user:`Leo Grinsztajn ` - -* :class:`TableVectorizer` doesn't fail anymore if an inferred type doesn't work during transform. - The new entries not matching the type are replaced by missing values. :pr:`666` by :user:`Leo Grinsztajn ` - -- Dataset fetcher :func:`datasets.fetch_employee_salaries` now has a parameter - `overload_job_titles` to allow overloading the job titles - (`employee_position_title`) with the column `underfilled_job_title`, - which provides some more information about the job title. - :pr:`581` by :user:`Lilian Boulard ` - -* Fix bugs which was triggered when `extract_until` was "year", "month", "microseconds" - or "nanoseconds", and add the option to set it to `None` to only extract `total_time`, - the time from epoch. :class:`DatetimeEncoder`. :pr:`743` by :user:`Leo Grinsztajn ` - -Before skrub: dirty_cat -======================== - -Skrub was born from the `dirty_cat `__ -package. - -Dirty-cat release 0.4.1 -========================== - -Major changes -------------- -* :func:`fuzzy_join` and :class:`FeatureAugmenter` can now join on numeric columns based on the euclidean distance. - :pr:`530` by :user:`Jovan Stojanovic ` - -* :func:`fuzzy_join` and :class:`FeatureAugmenter` can perform many-to-many joins on lists of numeric or string key columns. - :pr:`530` by :user:`Jovan Stojanovic ` - -* :func:`GapEncoder.transform` will not continue fitting of the instance anymore. - It makes functions that depend on it (:func:`~GapEncoder.get_feature_names_out`, - :func:`~GapEncoder.score`, etc.) deterministic once fitted. - :pr:`548` by :user:`Lilian Boulard ` - -* :func:`fuzzy_join` and :class:`FeatureAugmenter` now perform joins on missing values as in `pandas.merge` - but raises a warning. :pr:`522` and :pr:`529` by :user:`Jovan Stojanovic ` - -* Added :func:`get_ken_table_aliases` and :func:`get_ken_types` for exploring - KEN embeddings. :pr:`539` by :user:`Lilian Boulard `. - - -Minor changes -------------- -* Improvement of date column detection and date format inference in :class:`TableVectorizer`. The - format inference now tries to find a format which works for all non-missing values of the column, and only - tries pandas default inference if it fails. - :pr:`543` by :user:`Leo Grinsztajn ` - :pr:`587` by :user:`Leo Grinsztajn ` - - - -Dirty-cat Release 0.4.0 -========================= - -Major changes -------------- -* `SuperVectorizer` is renamed as :class:`TableVectorizer`, a warning is raised when using the old name. - :pr:`484` by :user:`Jovan Stojanovic ` - -* New experimental feature: joining tables using :func:`fuzzy_join` by approximate key matching. Matches are based - on string similarities and the nearest neighbors matches are found for each category. - :pr:`291` by :user:`Jovan Stojanovic ` and :user:`Leo Grinsztajn ` - -* New experimental feature: :class:`FeatureAugmenter`, a transformer - that augments with :func:`fuzzy_join` the number of features in a main table by using information from auxiliary tables. - :pr:`409` by :user:`Jovan Stojanovic ` - -* Unnecessary API has been made private: everything (files, functions, classes) - starting with an underscore shouldn't be imported in your code. :pr:`331` by :user:`Lilian Boulard ` - -* The :class:`MinHashEncoder` now supports a `n_jobs` parameter to parallelize - the hashes computation. :pr:`267` by :user:`Leo Grinsztajn ` and :user:`Lilian Boulard `. - -* New experimental feature: deduplicating misspelled categories using :func:`deduplicate` by clustering string distances. - This function works best when there are significantly more duplicates than underlying categories. - :pr:`339` by :user:`Moritz Boos `. - -Minor changes -------------- -* Add example `Wikipedia embeddings to enrich the data`. :pr:`487` by :user:`Jovan Stojanovic ` - -* **datasets.fetching**: contains a new function :func:`get_ken_embeddings` that can be used to download Wikipedia - embeddings and filter them by type. - -* **datasets.fetching**: contains a new function :func:`fetch_world_bank_indicator` that can be used to download indicators - from the World Bank Open Data platform. - :pr:`291` by :user:`Jovan Stojanovic ` - -* Removed example `Fitting scalable, non-linear models on data with dirty categories`. :pr:`386` by :user:`Jovan Stojanovic ` - -* :class:`MinHashEncoder`'s :func:`minhash` method is no longer public. :pr:`379` by :user:`Jovan Stojanovic ` - -* Fetching functions now have an additional argument ``directory``, - which can be used to specify where to save and load from datasets. - :pr:`432` by :user:`Lilian Boulard ` - -* Fetching functions now have an additional argument ``directory``, - which can be used to specify where to save and load from datasets. - :pr:`432` and :pr:`453` by :user:`Lilian Boulard ` - -* The :class:`TableVectorizer`'s default `OneHotEncoder` for low cardinality categorical variables now defaults - to `handle_unknown="ignore"` instead of `handle_unknown="error"` (for sklearn >= 1.0.0). - This means that categories seen only at test time will be encoded by a vector of zeroes instead of raising an error. :pr:`473` by :user:`Leo Grinsztajn ` - -Bug fixes ---------- - -* The :class:`MinHashEncoder` now considers `None` and empty strings as missing values, rather - than raising an error. :pr:`378` by :user:`Gael Varoquaux ` - -Dirty-cat Release 0.3.0 -========================== - -Major changes -------------- - -* New encoder: :class:`DatetimeEncoder` can transform a datetime column into several numeric columns - (year, month, day, hour, minute, second, ...). It is now the default transformer used - in the :class:`TableVectorizer` for datetime columns. :pr:`239` by :user:`Leo Grinsztajn ` - -* The :class:`TableVectorizer` has seen some major improvements and bug fixes: - - - Fixes the automatic casting logic in ``transform``. - - To avoid dimensionality explosion when a feature has two unique values, the default encoder (:class:`~sklearn.preprocessing.OneHotEncoder`) now drops one of the two vectors (see parameter `drop="if_binary"`). - - ``fit_transform`` and ``transform`` can now return unencoded features, like the :class:`~sklearn.compose.ColumnTransformer`'s behavior. Previously, a ``RuntimeError`` was raised. - - :pr:`300` by :user:`Lilian Boulard ` - -* **Backward-incompatible change in the TableVectorizer**: - To apply ``remainder`` to features (with the ``*_transformer`` parameters), - the value ``'remainder'`` must be passed, instead of ``None`` in previous versions. - ``None`` now indicates that we want to use the default transformer. :pr:`303` by :user:`Lilian Boulard ` - -* Support for Python 3.6 and 3.7 has been dropped. Python >= 3.8 is now required. :pr:`289` by :user:`Lilian Boulard ` - -* Bumped minimum dependencies: - - - scikit-learn>=0.23 - - scipy>=1.4.0 - - numpy>=1.17.3 - - pandas>=1.2.0 :pr:`299` and :pr:`300` by :user:`Lilian Boulard ` - -* Dropped support for Jaro, Jaro-Winkler and Levenshtein distances. - - - The :class:`SimilarityEncoder` now exclusively uses ``ngram`` for similarities, - and the `similarity` parameter is deprecated. It will be removed in 0.5. :pr:`282` by :user:`Lilian Boulard ` - -Notes ------ - -* The ``transformers_`` attribute of the :class:`TableVectorizer` now contains column - names instead of column indices for the "remainder" columns. :pr:`266` by :user:`Leo Grinsztajn ` - - -Dirty-cat Release 0.2.2 -========================= - -Bug fixes ---------- - -* Fixed a bug in the :class:`TableVectorizer` causing a :class:`FutureWarning` - when using the :func:`get_feature_names_out` method. :pr:`262` by :user:`Lilian Boulard ` - - -Dirty-cat Release 0.2.1 -========================== - -Major changes -------------- - -* Improvements to the :class:`TableVectorizer` - - - Type detection works better: handles dates, numerics columns encoded as strings, or numeric columns containing strings for missing values. - - :pr:`238` by :user:`Leo Grinsztajn ` - -* :func:`get_feature_names` becomes :func:`get_feature_names_out`, following changes in the scikit-learn API. - :func:`get_feature_names` is deprecated in scikit-learn > 1.0. :pr:`241` by :user:`Gael Varoquaux ` - -* Improvements to the :class:`MinHashEncoder` - - It is now possible to fit multiple columns simultaneously with the :class:`MinHashEncoder`. - Very useful when using for instance the :func:`~sklearn.compose.make_column_transformer` function, - on multiple columns. - - :pr:`243` by :user:`Jovan Stojanovic ` - - -Bug-fixes ---------- - -* Fixed a bug that resulted in the :class:`GapEncoder` ignoring the analyzer argument. :pr:`242` by :user:`Jovan Stojanovic ` - -* :class:`GapEncoder`'s `get_feature_names_out` now accepts all iterators, not just lists. :pr:`255` by :user:`Lilian Boulard ` - -* Fixed :class:`DeprecationWarning` raised by the usage of `distutils.version.LooseVersion`. :pr:`261` by :user:`Lilian Boulard ` - -Notes ------ - -* Remove trailing imports in the :class:`MinHashEncoder`. - -* Fix typos and update links for website. - -* Documentation of the :class:`TableVectorizer` and the :class:`SimilarityEncoder` improved. - -Dirty-cat Release 0.2.0 -========================= - -Also see pre-release 0.2.0a1 below for additional changes. - -Major changes -------------- - -* Bump minimum dependencies: - - - scikit-learn (>=0.21.0) :pr:`202` by :user:`Lilian Boulard ` - - pandas (>=1.1.5) **! NEW REQUIREMENT !** :pr:`155` by :user:`Lilian Boulard ` - -* **datasets.fetching** - backward-incompatible changes to the example - datasets fetchers: - - - The backend has changed: we now exclusively fetch the datasets from OpenML. - End users should not see any difference regarding this. - - The frontend, however, changed a little: the fetching functions stay the same - but their return values were modified in favor of a more Pythonic interface. - Refer to the docstrings of functions `dirty_cat.datasets.fetch_*` - for more information. - - The example notebooks were updated to reflect these changes. :pr:`155` by :user:`Lilian Boulard ` - -* **Backward incompatible change to** :class:`MinHashEncoder`: The :class:`MinHashEncoder` now - only supports two dimensional inputs of shape (N_samples, 1). - :pr:`185` by :user:`Lilian Boulard ` and :user:`Alexis Cvetkov `. - -* Update `handle_missing` parameters: - - - :class:`GapEncoder`: the default value "zero_impute" becomes "empty_impute" (see doc). - - :class:`MinHashEncoder`: the default value "" becomes "zero_impute" (see doc). - - :pr:`210` by :user:`Alexis Cvetkov `. - -* Add a method "get_feature_names_out" for the :class:`GapEncoder` and the :class:`TableVectorizer`, - since `get_feature_names` will be depreciated in scikit-learn 1.2. :pr:`216` by :user:`Alexis Cvetkov ` - -Notes ------ - -* Removed hard-coded CSV file `dirty_cat/data/FiveThirtyEight_Midwest_Survey.csv`. - - -* Improvements to the :class:`TableVectorizer` - - - Missing values are not systematically imputed anymore - - Type casting and per-column imputation are now learnt during fitting - - Several bugfixes - - :pr:`201` by :user:`Lilian Boulard ` - -Dirty-cat Release 0.2.0a1 -============================ - -Version 0.2.0a1 is a pre-release. -To try it, you have to install it manually using:: - - pip install --pre dirty_cat==0.2.0a1 - -or from the GitHub repository:: - - pip install git+https://github.com/dirty-cat/dirty_cat.git - -Major changes -------------- - -* Bump minimum dependencies: - - - Python (>= 3.6) - - NumPy (>= 1.16) - - SciPy (>= 1.2) - - scikit-learn (>= 0.20.0) - -* :class:`TableVectorizer`: Added automatic transform through the - :class:`TableVectorizer` class. It transforms - columns automatically based on their type. It provides a replacement - for scikit-learn's :class:`~sklearn.compose.ColumnTransformer` simpler to use on heterogeneous - pandas DataFrame. :pr:`167` by :user:`Lilian Boulard ` - -* **Backward incompatible change to** :class:`GapEncoder`: The :class:`GapEncoder` now only - supports two-dimensional inputs of shape (n_samples, n_features). - Internally, features are encoded by independent :class:`GapEncoder` models, - and are then concatenated into a single matrix. - :pr:`185` by :user:`Lilian Boulard ` and :user:`Alexis Cvetkov `. - - -Bug-fixes ---------- - -* Fix `get_feature_names` for scikit-learn > 0.21. :pr:`216` by :user:`Alexis Cvetkov ` - - -Dirty-cat Release 0.1.1 -======================== - -Major changes -------------- - -Bug-fixes ---------- - -* RuntimeWarnings due to overflow in :class:`GapEncoder`. :pr:`161` by :user:`Alexis Cvetkov ` - - -Dirty-cat Release 0.1.0 -========================= - -Major changes -------------- - -* :class:`GapEncoder`: Added online Gamma-Poisson factorization through the - :class:`GapEncoder` class. This method discovers latent categories formed - via combinations of substrings, and encodes string data as combinations of - these categories. To be used if interpretability is important. :pr:`153` by :user:`Alexis Cvetkov ` - -Bug-fixes ---------- - -* Multiprocessing exception in notebook. :pr:`154` by :user:`Lilian Boulard ` - - -Dirty-cat Release 0.0.7 -======================== - -* **MinHashEncoder**: Added ``minhash_encoder.py`` and ``fast_hast.py`` files - that implement minhash encoding through the :class:`MinHashEncoder` class. - This method allows for fast and scalable encoding of string categorical - variables. - -* **datasets.fetch_employee_salaries**: change the origin of download for employee_salaries. - - - The function now return a bunch with a dataframe under the field "data", - and not the path to the csv file. - - The field "description" has been renamed to "DESCR". - -* **SimilarityEncoder**: Fixed a bug when using the Jaro-Winkler distance as a - similarity metric. Our implementation now accurately reproduces the behaviour - of the ``python-Levenshtein`` implementation. - -* **SimilarityEncoder**: Added a `handle_missing` attribute to allow encoding - with missing values. - -* **TargetEncoder**: Added a `handle_missing` attribute to allow encoding - with missing values. - -* **MinHashEncoder**: Added a `handle_missing` attribute to allow encoding - with missing values. - -Dirty-cat Release 0.0.6 -========================= - -* **SimilarityEncoder**: Accelerate ``SimilarityEncoder.transform``, by: - - - computing the vocabulary count vectors in ``fit`` instead of ``transform`` - - computing the similarities in parallel using ``joblib``. This option can be - turned on/off via the ``n_jobs`` attribute of the :class:`SimilarityEncoder`. - -* **SimilarityEncoder**: Fix a bug that was preventing a :class:`SimilarityEncoder` - to be created when ``categories`` was a list. - -* **SimilarityEncoder**: Set the dtype passed to the ngram similarity - to float32, which reduces memory consumption during encoding. - -Dirty-cat Release 0.0.5 -======================== - -* **SimilarityEncoder**: Change the default ngram range to (2, 4) which - performs better empirically. - -* **SimilarityEncoder**: Added a `most_frequent` strategy to define - prototype categories for large-scale learning. - -* **SimilarityEncoder**: Added a `k-means` strategy to define prototype - categories for large-scale learning. - -* **SimilarityEncoder**: Added the possibility to use hashing ngrams for - stateless fitting with the ngram similarity. - -* **SimilarityEncoder**: Performance improvements in the ngram similarity. - -* **SimilarityEncoder**: Expose a `get_feature_names` method. diff --git a/skrub/_docs/CONTRIBUTING.rst b/skrub/_docs/CONTRIBUTING.rst deleted file mode 100644 index 8a6dfe829..000000000 --- a/skrub/_docs/CONTRIBUTING.rst +++ /dev/null @@ -1,498 +0,0 @@ -.. _contributing: - -Contributing guide -================== - -First off, thank you for taking the time to contribute! - -Below are some guidelines to help you get started. - - -Have a question? ----------------- - -If you have any questions, feel free to reach out: - -- Join our community on `Discord `_ for general chat and Q&A. -- Alternatively, you can `start a discussion on GitHub `_. - -What to know before you begin ------------------------------ - -To understand the purpose and goals behind skrub, please read our -`vision statement `_. - -If you're interested in the research behind skrub, -we encourage you to explore these papers: - -- `Similarity Encoding for Learning with Dirty - Categorical Variables `_ -- `Encoding High-Cardinality String Categorical - Variables `_. - -How can I contribute? ---------------------- - -Reporting bugs -~~~~~~~~~~~~~~ - -Using the library is the best way to discover bugs and limitations. If you find one, -please: - -1. **Check if an issue already exists** - by searching the `GitHub issues `_ - - - If **open**, leave a 👍 on the original message to signal that you are also affected. - - If closed, check for one of the following: - - A **merged pull request** may indicate the bug is fixed. Update your - skrub version or note if the fix is pending a release. - - A **wontfix label** or reasoning may be provided if the issue was - closed without a fix. -2. If the issue does not exist, `create a new one `_. - -How to submit a bug report? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To help us resolve the issue quickly, please include: - -- A **clear and descriptive title**. -- A **summary of the expected result**. -- Any **additional details** where the bug might occur or doesn't occur unexpectedly. -- A **code snippet** that reproduces the issue, if applicable. -- **Version information** for Python, skrub, and relevant dependencies (e.g., scikit-learn, numpy, pandas). - -How to write an example? -^^^^^^^^^^^^^^^^^^^^^^^^^ -We highly encourage contributors to add examples to the documentation -when they add new features, or if they have a use case that is not yet covered -in the documentation. - -You can find a guide on how to write examples in the :ref:`example guide `. - - -Suggesting enhancements -~~~~~~~~~~~~~~~~~~~~~~~ - -If you have an idea for improving skrub, whether it's a fix -or a new feature, first: - -- **Check if it has been proposed or implemented** by reviewing - `open pull requests `_. -- If not, `submit a new issue `_ - with your proposal before writing any code. - -How to submit an enhancement proposal? -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -When proposing an enhancement: - -- **Use a clear and descriptive title**. -- **Explain the goal** of the enhancement. -- Provide a **detailed step-by-step description** of the proposed change. -- **Link to any relevant resources** that may support the enhancement. - - -If the enhancement proposal is validated -'''''''''''''''''''''''''''''''''''''''' - -Once your enhancement proposal is approved, let the maintainers know the following: - -- **If you will write the code and submit a Pull Request (PR)**: - Contributing the feature yourself is the quickest way to see it implemented. - We're here to guide you through the process if needed! To get started, - refer to the section :ref:`writing-your-first-pull-request`. -- **If you won't be writing the code**: - A developer can then take over the implementation. - However, please note that we cannot guarantee how long - it will take for the feature to be added. - - -If the enhancement is refused -''''''''''''''''''''''''''''' - -Although many ideas are great, not all will align with the objectives -of skrub. - -If your enhancement is not accepted, consider implementing it -as a separate package that builds on top of skrub! - -We would love to see your work, and in some cases, we might even -feature your package in the official repository. - - -.. _writing-your-first-pull-request: - -Writing your first Pull Request -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Preparing the ground -^^^^^^^^^^^^^^^^^^^^ - - -Before writing any code, make sure you have discussed your plans with the maintainers. -You can do this by opening a new issue to discuss a specific improvement (as -described above), or by commenting on an existing issue to express your interest in working on it. - -Be sure to get approval from the maintainers before you start coding, and especially -before opening any new pull requests (PRs). This helps prevent issues such as multiple -people working on the same problem independently, or working on an issue that is -not clearly defined. Without prior discussion, your PR may be closed for being out -of scope or unrelated to the problem at hand. - -Every PR should link to the issue it addresses. - -Setting up the environment -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To setup your development environment, you need to follow the steps in "From Source" tab -present in :ref:`Installing from source` page. -After that, you can return to this page to continue. - -Now that the development environment is ready, you may create a new branch and start working on -the new issue. - -.. code:: sh - - # fetch latest updates and start from the current head - git fetch upstream - git checkout -b my-branch-name-eg-fix-issue-123 - # make some changes - git add ./the/file-i-changed - git commit -m "my message" - git push --set-upstream origin my-branch-name-eg-fix-issue-123 - -At this point, if you visit again the `pull requests -page `__ github should show a -banner asking if you want to open a pull request from your new branch. - - -.. _implementation guidelines: - -Implementation Guidelines -^^^^^^^^^^^^^^^^^^^^^^^^^ - -When contributing, keep these project goals in mind: - -- **Pure Python code**: Avoid using binary extensions, Cython, or other compiled languages. -- **Production-friendly code**: - - Target the widest possible range of Python versions and dependencies. - - Minimize the use of external dependencies. - - Ensure backward compatibility as much as possible. -- **Performance over readability**: - Optimized code may be less readable, so please include clear and detailed comments. - Refer to this `best practice guide `_. -- **Explicit variable/function names**: Use descriptive, verbose names for clarity. -- **Document public API components**: - - Document all public functions, methods, variables, and class signatures. - - The public API refers to all components available for import and use by library users. Anything that doesn't begin with an underscore is considered part of the public API. - -Checking the quality of your code contribution ----------------------------------------------- - -Testing the code -~~~~~~~~~~~~~~~~ - -Tests for files in a given folder should be located in a sub-folder -named ``tests``: tests for skrub objects are located in ``skrub/tests/``, -tests for the dataframe API are in ``skrub/_dataframe/tests/`` and so on. - -Tests should check all functionalities of the code that you are going to -add. If needed, additional tests should be added to verify that other -objects behave correctly. - -Consider an example: your contribution is for the -``AmazingTransformer``, whose code is in -``skrub/_amazing_transformer.py``. The ``AmazingTransformer`` is added -as one of the default transformers for ``TableVectorizer``. - -As such, you should add a new file testing the functionality of -``AmazingTransformer`` in ``skrub/tests/test_amazing_transformer.py``, -and update the file ``skrub/tests/test_table_vectorizer.py`` so that it -takes into account the new transformer. - -Additionally, you might have updated the internal dataframe API in -``skrub/_dataframe/_common.py`` with a new function, -``amazing_function``. In this case, you should also update -``skrub/_dataframe/tests/test_common.py`` to add a test for the -``amazing_function``. - -Run each updated test file using ``pytest`` -(`pytest docs `_): - -.. code:: sh - - pytest -vsl skrub/tests/test_amazing_transformer.py \ - skrub/_dataframe/tests/test_common.py \ - skrub/_dataframe/tests/test_table_vectorizer.py - -The ``-vsl`` flag provides more information when running the tests. - -It is also possible to run a specific test, or set of tests using the -commands ``pytest the_file.py::the_test``, or -``pytest the_file.py -k 'test_name_pattern'``. This is helpful to avoid -having to run all the tests. - -If you work on Windows, you might have some issues with the working -directory if you use ``pytest``, while ``python -m pytest ...`` should -be more robust. - -Once you are satisfied with your changes, you can run all the tests to make sure -that your change did not break code elsewhere: - -.. code:: sh - - pytest -s skrub/tests - -Finally, sync your changes with the remote repository and wait for CI to run. - -Checking coverage on the local machine -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Checking coverage is one of the operations that is performed after -submitting the code. As this operation may take a long time online, it -is possible to check whether the code coverage is high enough on your -local machine. - -Run your tests with the ``--cov`` and ``--cov-report`` arguments: - -.. code:: sh - - pytest -vsl skrub/tests/test_amazing_transformer.py --cov=skrub --cov-report=html - -This will create the folder ``htmlcov``: by opening -``htmlcov/index.html`` it is possible to check what lines are covered in -each file. - -Updating doctests -~~~~~~~~~~~~~~~~~ - -If you alter the default behavior of an object, then this might affect -the docstrings. Check for possible problems by running - -.. code:: sh - - pytest skrub/path/to/file - - -Formatting and pre-commit checks -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Formatting the code well helps with code development and maintenance, -which why is skrub requires that all commits follow a specific set of -formatting rules to ensure code quality. - -Luckily, these checks are performed automatically by the ``pre-commit`` -tool (`pre-commit docs `__) before any commit -can be pushed. Something worth noting is that if the ``pre-commit`` -hooks format some files, the commit will be canceled: you will have to -stage the changes made by ``pre-commit`` and commit again. - -Ensuring the documentation builds -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. - Inspired by: https://github.com/scikit-learn/scikit-learn/blob/main/doc/developers/contributing.rst - -First, make sure you have properly installed the development version of skrub. -You can follow the :ref:`installation_instructions` > "From source" section, if needed. - -To build the documentation, you need to be in the ``doc`` folder: - -.. code:: bash - - cd doc - -To generate the full documentation, including the example gallery, -run the following command: - -.. code:: bash - - make html - -On Windows, use: - -.. code:: bat - - make.bat html - -.. note:: - - If you are working on Windows, building the example ``1131_optuna_choices`` - may fail with a permission error when running ``make.bat html``. This is - because optuna uses symlinks for file locking, which requires admin - privileges on Windows by default. The rest of the documentation build - should run without problem, so it is safe to ignore this error if your - contribution does not touch that particular example. - -The documentation will be generated in the ``_build/html/`` directory -and are viewable in a web browser, for instance by opening the local -``_build/html/index.html`` file. - -Running all the examples can take a while, so if you only want to generate -specific examples, you can use the following command with a regex pattern: - -.. code:: bash - - make html EXAMPLES_PATTERN=your_regex_goes_here - -On Windows, use: - -.. code:: bat - - make.bat html EXAMPLES_PATTERN=your_regex_goes_here - -This is especially helpful when you're only modifying or checking a few examples. - -It is also possible to build the documentation without running the examples -without running the examples by using the following command: - -.. code:: bash - - make html-noplot - -On Windows, use: - -.. code:: bat - - make.bat html-noplot - -This command generates the documentation without re-executing the examples, which can -take a long time. This is useful if you are only modifying the documentation itself, such as fixing -typos or improving explanations. - - -**Using pixi** - -You can download and install pixi from `here `_. - -From the repository root: - -.. code:: bash - - # Build documentation without running examples (faster) - pixi run build-doc-quick - - # Build the full documentation, including examples - pixi run build-doc - - # Clean previously built documentation - pixi run clean-doc - -The documentation will be generated in the ``doc/_build/html/`` directory. -You can view it by opening the local ``doc/_build/html/index.html`` file. - -.. warning:: - - On Intel-based macOS systems (``osx-64``), some pixi environments may not - resolve correctly due to missing upstream package builds (e.g., for PyTorch). - If you encounter issues, you can always fall back to using ``make`` as - described above. - -Editing the API reference documentation -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**All public functions and classes must be documented in the API -reference**, hence when adding a public function or class, a new entry must be -added, as detailed just above. - -To add a new entry to the :ref:`API reference documentation` or change its -content, head to ``doc/api_reference.py``. This data is then used by ``doc/conf.py`` -to render templates located at ``doc/reference/*.rst.template``. - - -Submitting your code --------------------- - -Once you have pushed your commits to your remote repository, you can submit -a PR by clicking the "Compare & pull request" button on GitHub, -targeting the skrub repository. - -Updating the changelog -~~~~~~~~~~~~~~~~~~~~~~ -Any user-facing change to the codebase needs to be reported in the changelog, -found in the ``CHANGES.rst`` file in the root of the repository. A user-facing -change is any change to a functionality of skrub that users are expected to interact -with: for example, adding or removing a parameter, adding a new transformer, -deprecating a function, etc. - -Changes made in the test suite, or changes made in the -private parts of the library, should not be reported, unless they bring some benefit -to the user (such as performance improvements). Normally, changes made to the -documentation, such as typo or formatting fixes, are not reported either, while -new examples usually can be added. -Depending on the nature of the PR, a maintainer may add the "no -changelog needed" label to skip the corresponding check if a changelog entry isn't -relevant. - -Changelog entries need to follow a specific format: the change should be described -in sufficient detail for users to understand how they may be affected, and the -entry must list both the PR number and the GitHub username of the author(s) of the -PR. - -Here is an example: - -.. code:: bash - - - :meth:`DataOp.skb.apply` now allows passing extra named arguments to the - estimator's methods through the parameters ``fit_kwargs``, ``predict_kwargs`` - etc. :pr:`1642` by :user:`Jérôme Dockès `. - -The PR number is reported with the directive ``:pr:`NUMBER```, and the author -of the PR uses the directive ``:user:`AUTHOR NAME ```. - -Missing changelog entries, or changelog entries that do not follow the format, -will fail the changelog check in the CI. - -Continuous Integration (CI) -~~~~~~~~~~~~~~~~~~~~~~~~~~~ -After creating your PR, CI tools will run proceed to run all the tests on all -configurations supported by skrub. - -- **Github Actions**: - Used for testing skrub across various platforms (Linux, macOS, Windows) - and dependencies. -- **CircleCI**: - Builds and verifies the project documentation. - -If any of the following markers appears in the commit message, the following -actions are taken. - - ====================== =================== - Commit Message Marker Action Taken by CI - ---------------------- ------------------- - [ci skip] CI is skipped completely - [skip ci] CI is skipped completely - [skip github] CI is skipped completely - [deps nightly] CI is run with the nightly builds of dependencies - [doc skip] Docs are not built - [doc quick] Docs built, but excludes example gallery plots - [doc build] Docs built including example gallery plots (longer) - ====================== =================== - -Note that by default the documentation is built, but only the examples that are -directly modified by the pull request are executed. - -CI is testing all possible configurations supported by skrub, so tests may fail -with configurations different from what you are developing with. If this is the -case, it is possible to run the tests in the environment that is failing by -using `pixi `_. For example if the env is ``ci-py309-min-optional-deps``, it is -possible to replicate it using the following command: - -.. code:: sh - - pixi run -e ci-py309-min-optional-deps pytest skrub/tests/path/to/test - -This command downloads the specific environment on the machine, so you can test -it locally and apply fixes, or have a clearer idea of where the code is failing -to discuss with the maintainers. - -Finally, if the remote repository was changed, you might need to run - ``pre-commit run --all-files`` to make sure that the formatting is - correct. - -Integration -^^^^^^^^^^^ - -Community consensus is key in the integration process. Expect a minimum of -1 to 3 reviews from maintainers depending on the size of the change before we consider -merging the PR. diff --git a/skrub/_docs/RELEASE_PROCESS.rst b/skrub/_docs/RELEASE_PROCESS.rst deleted file mode 100644 index 4fcc7db19..000000000 --- a/skrub/_docs/RELEASE_PROCESS.rst +++ /dev/null @@ -1,157 +0,0 @@ -Release process -=============== - -Target audience ---------------- - -This document is aimed at established contributors to the project. - -Process -------- - -Going further, we assume you have write-access to both the repository, PyPI and -conda-forge project page. - -.. note:: We follow scikit-learn versioning conventions: - - - Major/Minor releases are numbered X.Y.0. - - Bug-fix releases are done as needed between major/minor releases and only apply to - the last stable version. These releases are numbered X.Y.Z. - -To release a new minor version of skrub (e.g., from 0.1.0 to 0.2.0), here are the main -steps and appropriate resources: - -Preparing the release branch -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- Create the ``0.2.X`` branch, branching from upstream/main, and push it upstream - (it may already exist). You can also use the GitHub UI to create the branch if you - disabled ``git push upstream`` in your local git config. -- Edit CHANGES.rst: replace "ongoing development" with ``0.2.0`` -- Edit VERSION.txt: replace ``0.2.dev0`` with ``0.2.0`` -- Build the wheel and test it: - - - ``rm -r dist skrub.egg-info`` - - ``python -m build`` (may need ``pip install build``) - - ``twine check dist/*`` (may need ``pip install twine``) - - In a directory outside of the skrub repo: - - - Install the wheel in a fresh virtualenv - - Run all tests with ``pytest --pyargs skrub`` - -- git commit the changes done to CHANGES.rst and VERSION.txt -- If we are doing a bugfix release (``0.2.X`` already existed before) we need to rebase - on the existing ``0.2.X``. - - - Run ``git rebase -i upstream/0.2.X`` - - All commits that have been made on main that we want to keep will be replayed on - top of the last release's tag in ``0.2.X``. - -- Open a PR targeting ``0.2.X``. This will update the doc for the stable release. While - the update runs, we can prepare a PR on the main branch to be merged after the - release, see the next section. - -Meanwhile, preparing the post-released PR -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- For a major/minor (not a patch) release: - - VERSION.txt: update to ``0.3.dev0`` (the next minor). - - CHANGES.rst: create a header for the new entries ("ongoing development"). - - doc/version.json: update the version numbers of the stable release and dev branch. - Don't forget to add an entry for the previously stable version. - - -The doc update has succeeded -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- Merge the PR targeting ``0.2.X``, **without squashing the commits**. - -.. warning:: - - This PR should be merged with the rebase mode instead of the usual squash mode - because we want to keep the history in the ``0.2.X`` branch close to the history of - the main branch, which will help for future bug fix releases. - - By default, only the squash & merge option is available to merge PRs on the main - branch. So, when releasing, we need to temporarily enable the rebase option. - To do so, head to Settings -> General -> Pull request, enable rebasing, merge the - PR targeting ``0.2.X`` with the rebase option, then disable the setting again. - -- Check the rendering of the doc for the built ``0.2.X`` branch, the examples and the - changelog. Ideally, we should go over all features and double check that the docs are - being rendered correctly, because issues there often go unnoticed. - - -Next, we'll build the wheel and push it to Pypi! - - -Pushing the wheel to Pypi -^^^^^^^^^^^^^^^^^^^^^^^^^ - -- Checkout to the release candidate branch: - - .. code:: shell - - git fetch upstream - git checkout upstream/0.2.X - -- Build the wheel and test it: - - - ``rm -r dist skrub.egg-info`` - - ``python -m build`` (may need ``pip install build``) - - ``twine check dist/*`` (may need ``pip install twine``) - - In a directory outside of the skrub repo: - - - Install the wheel in a fresh virtualenv - - Run all tests with ``pytest --pyargs skrub`` - -- If test passed successfully, upload to Pypi: ``twine upload dist/*``. -- Tag the release commit and push the tag: - - - ``git tag -s '0.2.0'``, ``-s`` is for signing and is optional. - - ``git push upstream tag 0.2.0`` - -- Check that your version is now on Pypi. -- Merge the post-release PR -- For major/minor releases only, in the documentation branches repository - https://github.com/skrub-data/skrub-data.github.io, update the documentation symlink - to stable version, here from 0.1 to 0.2: - - .. code:: shell - - rm stable - ln -s 0.2 stable - - ``stable`` should point on the latest number release. - - -Update the conda-forge recipe -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- Create the branch ``release-0.2.0`` in - `skrub-feedstock `_ -- Edit ``recipe/meta.yml``, which is the only file we edit manually in that repo: - - Update the version number. - - Update the sha256 using Pypi hash. - - If needed, reset the build number to 0. - - If needed, update the requirements. - - - Check the new requirements with: - - .. code:: shell - - git checkout 0.2.0 - git diff 0.1.0 -- pyproject.toml - -- Open a PR targeting ``upstream/skrub-feedstock`` main branch. -- Use the the checklist posted in the PR template. In particular, it asks to post a - comment asking a bot to re-render the recipe. Make sure to wait until it has finished. -- Merge the PR. It takes up to an hour for the package to be available from the - conda-forge channel. -- When it becomes available, install it in a fresh environment and run tests. - -.. note:: - - You can add new maintainers to that repo by listing them at the end of meta.yml. - -- If the new recipe works fine, announce the release on social network channels 🎉! diff --git a/skrub/_docs/_templates/base.rst b/skrub/_docs/_templates/base.rst deleted file mode 100644 index b3200116b..000000000 --- a/skrub/_docs/_templates/base.rst +++ /dev/null @@ -1,37 +0,0 @@ -{{ objname | escape | underline(line="=") }} - -{% if objtype == "module" -%} - -.. automodule:: {{ fullname }} - -{%- elif objtype == "function" -%} - -.. currentmodule:: {{ module }} - -.. autofunction:: {{ objname }} - -.. minigallery:: {{ module }}.{{ objname }} - :add-heading: Gallery examples - :heading-level: - - -{%- elif objtype == "class" -%} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - :members: - :inherited-members: - :special-members: __call__ - :exclude-members: get_metadata_routing, set_fit_request - -.. minigallery:: {{ module }}.{{ objname }} {% for meth in methods %}{{ module }}.{{ objname }}.{{ meth }} {% endfor %} - :add-heading: Gallery examples - :heading-level: - - -{%- else -%} - -.. currentmodule:: {{ module }} - -.. auto{{ objtype }}:: {{ objname }} - -{%- endif -%} diff --git a/skrub/_docs/_templates/data_op_class.rst b/skrub/_docs/_templates/data_op_class.rst deleted file mode 100644 index 1b88b949c..000000000 --- a/skrub/_docs/_templates/data_op_class.rst +++ /dev/null @@ -1,6 +0,0 @@ -{{ objname | escape | underline(line="=") }} - -.. currentmodule:: {{ module }} - -.. autoclass:: {{ objname }} - :exclude-members: skb, __call__ diff --git a/skrub/_docs/_templates/numpydoc_docstring.rst b/skrub/_docs/_templates/numpydoc_docstring.rst deleted file mode 100644 index fd6a35f76..000000000 --- a/skrub/_docs/_templates/numpydoc_docstring.rst +++ /dev/null @@ -1,16 +0,0 @@ -{{index}} -{{summary}} -{{extended_summary}} -{{parameters}} -{{returns}} -{{yields}} -{{other_parameters}} -{{attributes}} -{{raises}} -{{warns}} -{{warnings}} -{{see_also}} -{{notes}} -{{references}} -{{examples}} -{{methods}} diff --git a/skrub/_docs/includes/big_toc_css.rst b/skrub/_docs/includes/big_toc_css.rst deleted file mode 100644 index 6008fce8e..000000000 --- a/skrub/_docs/includes/big_toc_css.rst +++ /dev/null @@ -1,160 +0,0 @@ -.. - File to ..include in a document with a big table of content, to give - it 'style' - -.. raw:: html - - - - diff --git a/skrub/_docs/sg_execution_times.rst b/skrub/_docs/sg_execution_times.rst deleted file mode 100644 index 33493fba4..000000000 --- a/skrub/_docs/sg_execution_times.rst +++ /dev/null @@ -1,88 +0,0 @@ - -:orphan: - -.. _sphx_glr_sg_execution_times: - - -Computation times -================= -**11:40.190** total execution time for 18 files **from all galleries**: - -.. container:: - - .. raw:: html - - - - - - - - .. list-table:: - :header-rows: 1 - :class: table table-striped sg-datatable - - * - Example - - Time - - Mem (MB) - * - :ref:`sphx_glr_auto_examples_03_joining_0070_join_aggregation.py` (``../examples/03_joining/0070_join_aggregation.py``) - - 02:24.175 - - 528.7 - * - :ref:`sphx_glr_auto_examples_01_encoding_0020_text_with_string_encoders.py` (``../examples/01_encoding/0020_text_with_string_encoders.py``) - - 02:19.025 - - 1152.5 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1120_multiple_tables.py` (``../examples/02_data_ops/1120_multiple_tables.py``) - - 01:23.995 - - 407.4 - * - :ref:`sphx_glr_auto_examples_01_encoding_0010_encodings.py` (``../examples/01_encoding/0010_encodings.py``) - - 00:57.927 - - 368.5 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1131_optuna_choices.py` (``../examples/02_data_ops/1131_optuna_choices.py``) - - 00:34.782 - - 735.6 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1130_choices.py` (``../examples/02_data_ops/1130_choices.py``) - - 00:34.396 - - 352.5 - * - :ref:`sphx_glr_auto_examples_03_joining_0040_fuzzy_joining.py` (``../examples/03_joining/0040_fuzzy_joining.py``) - - 00:31.762 - - 349.4 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1160_pytorch.py` (``../examples/02_data_ops/1160_pytorch.py``) - - 00:27.060 - - 349.9 - * - :ref:`sphx_glr_auto_examples_03_joining_0080_interpolation_join.py` (``../examples/03_joining/0080_interpolation_join.py``) - - 00:24.162 - - 1904.7 - * - :ref:`sphx_glr_auto_examples_03_joining_0060_multiple_key_join.py` (``../examples/03_joining/0060_multiple_key_join.py``) - - 00:23.482 - - 1524.7 - * - :ref:`sphx_glr_auto_tutorials_0000_getting_started.py` (``tutorials/0000_getting_started.py``) - - 00:15.634 - - 360.2 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1140_subsampling.py` (``../examples/02_data_ops/1140_subsampling.py``) - - 00:15.468 - - 355.8 - * - :ref:`sphx_glr_auto_examples_02_data_ops_0100_squashing_scaler.py` (``../examples/02_data_ops/0100_squashing_scaler.py``) - - 00:14.470 - - 349.4 - * - :ref:`sphx_glr_auto_examples_01_encoding_0030_datetime_encoder.py` (``../examples/01_encoding/0030_datetime_encoder.py``) - - 00:14.035 - - 356.4 - * - :ref:`sphx_glr_auto_tutorials_1110_data_ops_intro.py` (``tutorials/1110_data_ops_intro.py``) - - 00:13.458 - - 351.8 - * - :ref:`sphx_glr_auto_examples_0010_apply_to_cols.py` (``../examples/0010_apply_to_cols.py``) - - 00:12.338 - - 415.9 - * - :ref:`sphx_glr_auto_examples_02_data_ops_1150_use_case.py` (``../examples/02_data_ops/1150_use_case.py``) - - 00:07.995 - - 457.2 - * - :ref:`sphx_glr_auto_examples_0050_deduplication.py` (``../examples/0050_deduplication.py``) - - 00:06.024 - - 349.9 From 6a19cf72c9a1483da2685ea66b36d3cee34f6f73 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 25 Jun 2026 10:54:22 +0200 Subject: [PATCH 26/28] updating changelog --- CHANGES.rst | 7 ++++--- README.rst | 4 ++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/CHANGES.rst b/CHANGES.rst index b91285f15..dadbfe8ba 100644 --- a/CHANGES.rst +++ b/CHANGES.rst @@ -65,9 +65,10 @@ Changes :pr:`2048` by :user:`Riccardo Cappuzzo `. - The minimum required version of matplotlib has been increased from 3.4.3 to 3.6.1. :pr:`2159` by :user:`Riccardo Cappuzzo `. -- The package build has been updated and improved to reduce its size and include the - user guide and examples with the package, so that it is now possible to access - it directly from the wheel rather than having to rely on the online docs. +- The package build has been updated to include the user guide and examples with + the package, so that it is now possible to access it directly from the wheel + rather than having to rely on the online docs. Docs and examples are now stored + in ``skrub/_docs``, rather than in the root of the repository. :pr:`2173` by :user:`Riccardo Cappuzzo `. Bugfixes diff --git a/README.rst b/README.rst index 794b32baf..a959ecaa7 100644 --- a/README.rst +++ b/README.rst @@ -27,8 +27,8 @@ Website: https://skrub-data.org/ See our `examples `_, or check out the `learning materials `_. -The documentation (in Markdown format) is also bundled with the package itself. -After installing, you can find it at: +Documentation and examples are bundled with the package itself, in +``skrub/_docs``. After installing, you can find it at: .. code-block:: python From 79b2690ad5075c4c9a07a45893e574df676fc545 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 25 Jun 2026 11:04:17 +0200 Subject: [PATCH 27/28] restoring old files --- .../default_wrangling/apply_to_cols.rst | 29 +++++++++---------- .../multi_column_operations/selectors.rst | 14 ++++----- .../type_of_selectors.rst | 1 + 3 files changed, 21 insertions(+), 23 deletions(-) diff --git a/doc/modules/default_wrangling/apply_to_cols.rst b/doc/modules/default_wrangling/apply_to_cols.rst index fc9b6e07c..c25eeb418 100644 --- a/doc/modules/default_wrangling/apply_to_cols.rst +++ b/doc/modules/default_wrangling/apply_to_cols.rst @@ -2,7 +2,6 @@ .. |ApplyToCols| replace:: :class:`ApplyToCols` .. |TableVectorizer| replace:: :class:`TableVectorizer` -.. |selectors| replace:: :mod:`skrub.selectors` .. |s.string| replace:: :meth:`~skrub.selectors.string` .. |s.numeric| replace:: :meth:`~skrub.selectors.numeric` .. |RejectColumn| replace:: :class:`core.RejectColumn` @@ -15,7 +14,7 @@ .. _user_guide_multiple_columns: -Transforming only some columns with |ApplyToCols| +Transforming selected columns with |ApplyToCols| =========================================================== Very often and for various reasons, transformers must be applied only to some of the @@ -23,22 +22,22 @@ columns in a dataframe. For example, all numeric columns in a dataframe may need to be scaled at the same time, while string columns should be left alone. While the heuristics used by the :class:`TableVectorizer` are usually good enough to apply the proper transformers to different datatypes, using it may not be an -option in all cases. +option in all cases. In scikit-learn pipelines, the column selection operation can +be done with the :class:`~sklearn.compose.ColumnTransformer`. -|ApplyToCols| (optionally paired with the |selectors|) allows to transform specific -columns with a large degree of control: |ApplyToCols| maps a transformer to columns -in a dataframe, so that all columns that satisfy a certain condition are transformed, -while the others are left untouched. |ApplyToCols| and the |selectors| are similar -to scikit-learn's :class:`~sklearn.compose.ColumnTransformer`. +Skrub provides the |ApplyToCols| transformer to achieve the same results with +a larger degree of control over which columns are being transformed. +|ApplyToCols| maps a transformer to columns in a dataframe, so that all +columns that satisfy a certain condition are transformed, while the others are +left untouched. +.. tip:: -Using selectors to choose or exclude columns -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + If a skrub transformer has a ``cols`` parameter to specify a column list, + that can be a selector as well. Selectors give more control over which columns + are being transformed: they are discussed at length in the + :ref:`selectors user guide`. -If a skrub transformer has a ``cols`` parameter to specify a column list, -that can be a selector as well. Selectors give more control over which columns -are being transformed: they are discussed at length in the -:ref:`selectors user guide`. |ApplyToCols| can be used to transform a subset of columns in a dataframe, while leaving the non-selected columns unchanged. In this example, we want to apply @@ -111,7 +110,7 @@ id city_Madrid city_Paris city_Rome date_year date_month date_day date_to Note that the column "id" was not encoded and was instead left as-is. -Rejecting columns that cannot be handled by a transformer +Dealing with columns that cannot be handled by a transformer ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |ApplyToCols| can allow the underlying encoder to decide which columns it can be applied to. diff --git a/doc/modules/multi_column_operations/selectors.rst b/doc/modules/multi_column_operations/selectors.rst index 1e5004d8d..73c0ab987 100644 --- a/doc/modules/multi_column_operations/selectors.rst +++ b/doc/modules/multi_column_operations/selectors.rst @@ -108,8 +108,8 @@ name, data type, contents, or according to arbitrary user-provided rules:: * :ref:`user_guide_advanced_selectors` -Selectors can be combined with the set operators ------------------------------------------------- +Combining selectors +------------------- The available operators are ``|``, ``&``, ``-``, ``^`` with the meaning of usual python sets, and ``~`` to invert a selection: @@ -146,8 +146,8 @@ following selector won't compute the cardinality of non-categorical columns: (categorical() & cardinality_below(10)) .. _user_guide_selectors_expand: -Using selectors with dataframe libraries ----------------------------------------- +Visualizing a selector +---------------------- All selectors have the :meth:`expand` method, which allows dataframe manipulation outside of a skrub workflow: applying it to any dataframe will return the list @@ -180,10 +180,8 @@ The :meth:`expand_index` method also exists: rather than returning a list of col Using selectors with other skrub transformers ------------------------------------------------- -Skrub selectors are designed to be used in conjunction with :class:`~skrub.ApplyToCols`, -:class:`skrub.SelectCols`, and :class:`skrub.DropCols`, as well as -:func:`~skrub.DataOp.skb.apply` to improve their versatility in how they modify -columns. +Skrub transformers are designed to be used in conjunction with other transformers +that operate on columns to improve their versatility. For example, it is possible to drop columns that have more unique values than a certain amount by combining :func:`~skrub.selectors.cardinality_below` with diff --git a/doc/modules/multi_column_operations/type_of_selectors.rst b/doc/modules/multi_column_operations/type_of_selectors.rst index 87d598187..75df623a6 100644 --- a/doc/modules/multi_column_operations/type_of_selectors.rst +++ b/doc/modules/multi_column_operations/type_of_selectors.rst @@ -79,6 +79,7 @@ Selectors based on column data types - :func:`~skrub.selectors.any_date`: Select columns with date or datetime data types - :func:`~skrub.selectors.categorical`: Select columns with categorical data types - :func:`~skrub.selectors.string`: Select columns with string data types +- :func:`~skrub.selectors.object`: Select columns with the ``object`` (pandas) or ``pl.Object`` (polars) dtype - :func:`~skrub.selectors.boolean`: Select columns with boolean data types Selectors based on column content and properties From 6f96af6e8b5eb573b9fe6a9801a17a8dbef917f8 Mon Sep 17 00:00:00 2001 From: Riccardo Cappuzzo Date: Thu, 25 Jun 2026 11:07:23 +0200 Subject: [PATCH 28/28] some comments --- doc/Makefile | 10 ---------- skrub/__init__.py | 6 +++--- skrub/conftest.py | 2 ++ 3 files changed, 5 insertions(+), 13 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index c252f6dc6..5a6982785 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -63,16 +63,6 @@ markdown-noplot: @echo @echo "Markdown build (no plot) finished. The markdown files are in $(BUILDDIR)/markdown." -# skrub/_docs is the single source of truth for guide/content RST files. -# They are synced into doc/ automatically at build time by conf.py. -# Use this target to verify the two trees are in sync (no-op if they match). -check-docs-sync: - diff -rq --exclude="*.pyc" ../skrub/_docs/ . \ - --exclude="CHANGES.rst" --exclude="CONTRIBUTING.rst" \ - --exclude="RELEASE_PROCESS.rst" \ - $(addprefix --exclude=,$(notdir $(wildcard ../skrub/_docs/*.rst))) \ - && echo "skrub/_docs and doc/ are in sync" || true - # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile diff --git a/skrub/__init__.py b/skrub/__init__.py index 1576bcf52..de0f2c2ce 100644 --- a/skrub/__init__.py +++ b/skrub/__init__.py @@ -7,8 +7,8 @@ ready for scikit-learn or other ML frameworks. Bundled docs: ``skrub.__docs_dir__`` -Bundled getting started: ``skrub.__docs_dir__ / "auto_tutorials"`` -Bundled examples: ``skrub.__docs_dir__ / "auto_examples"`` +Bundled getting started: ``skrub.__docs_dir__ / "tutorials"`` +Bundled examples: ``skrub.__docs_dir__ / "examples"`` Online docs: https://skrub-data.org/stable/reference/index.html Source: https://github.com/skrub-data/skrub/ @@ -16,7 +16,7 @@ from pathlib import Path as _Path -#: Path to the Markdown documentation bundled with the package. +#: Path to the documentation bundled with the package. #: Use ``skrub.__docs_dir__`` to access it programmatically. __docs_dir__ = _Path(__file__).parent / "_docs" diff --git a/skrub/conftest.py b/skrub/conftest.py index 01ff21d64..3e5f7b661 100644 --- a/skrub/conftest.py +++ b/skrub/conftest.py @@ -34,6 +34,8 @@ def _example_data_dict(): } +# this is needed to ignore the skrub/_docs folder when running pytest +# otherwise, the examples in the folder are executed any time test discovery is run collect_ignore_glob = ["_docs/**/*.py"]