Add Polars materializer

[christianversloot]

Describe changes

This PR adds a basic materializer for Polars (https://pola.rs/), a performant dataframe library inspired by Pandas.

It accepts pl.DataFrame and pl.Series objects (like the Pandas materializer) and writes them to Parquet files via Apache Arrow conversion using pyarrow's ParquetWriter as proposed by Polars docs.

For materializing a pl.Series object (which Polars converts into a pyarrow.array instead of pyarrow.table), a conversion into a pyarrow.table is done first, to allow for writing to Parquet. Without this conversion, the ParquetWriter fails on a lacking table schema. However, to reconvert into a pl.Series on read, a ZenML flag signaling whether the artifact is a pl.Series is added into the table schema before writing.

I tested the integration locally:

• zenml integration install polars works and installs polars and pyarrow.
• Writing both a pl.DataFrame and pl.Series inside a step works.
• Same for reading, example from the sklearn IRIS dataset -> Pandas -> Polars -> write -> read:

shape: (100, 5)
┌───────────────────┬──────────────────┬───────────────────┬──────────────────┬────────┐
│ sepal length (cm) ┆ sepal width (cm) ┆ petal length (cm) ┆ petal width (cm) ┆ target │
│ ---               ┆ ---              ┆ ---               ┆ ---              ┆ ---    │
│ f64               ┆ f64              ┆ f64               ┆ f64              ┆ i32    │
╞═══════════════════╪══════════════════╪═══════════════════╪══════════════════╪════════╡
│ 7.0               ┆ 3.2              ┆ 4.7               ┆ 1.4              ┆ 1      │
│ 6.4               ┆ 3.2              ┆ 4.5               ┆ 1.5              ┆ 1      │
│ 6.9               ┆ 3.1              ┆ 4.9               ┆ 1.5              ┆ 1      │
│ 5.5               ┆ 2.3              ┆ 4.0               ┆ 1.3              ┆ 1      │
│ …                 ┆ …                ┆ …                 ┆ …                ┆ …      │
│ 6.3               ┆ 2.5              ┆ 5.0               ┆ 1.9              ┆ 2      │
│ 6.5               ┆ 3.0              ┆ 5.2               ┆ 2.0              ┆ 2      │
│ 6.2               ┆ 3.4              ┆ 5.4               ┆ 2.3              ┆ 2      │
│ 5.9               ┆ 3.0              ┆ 5.1               ┆ 1.8              ┆ 2      │
└───────────────────┴──────────────────┴───────────────────┴──────────────────┴────────┘
shape: (100,)
Series: 'target' [i32]
[
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
        …
        2
        2
        2
        2
        2
        2
        2
        2
        2
        2
        2
        2
        2
]

Pre-requisites

Please ensure you have done the following:

• I have read the CONTRIBUTING.md document.
• If my change requires a change to docs, I have updated the documentation accordingly.
• I have added tests to cover my changes.
• I have based my new branch on develop and the open PR is targeting develop. If your branch wasn't based on develop read Contribution guide on rebasing branch to develop.
• If my changes require changes to the dashboard, these changes are communicated/requested.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Other (add details above)

Summary by CodeRabbit

• New Features

  • Introduced Polars integration for enhanced data management.
  • New materializers added for handling Polars data frames and series.

• Documentation

  • Updated the user guide with information on new and modified materializers.

• Refactor

  • Expanded the integrations module to include Polars support.

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Walkthrough

ZenML has expanded its integration capabilities by adding support for Polars, a dataframe library. This update introduces new materializers to handle Polars-specific data types, enabling the reading and writing of Polars data frames within the ZenML environment. The changes reflect the addition of Polars as a recognized integration, as well as the necessary classes and methods to facilitate its use.

Changes

File Path	Change Summary
docs/book/.../handle-custom-data-types.md	Updated the table of materializers to include additions, removals, and updates for various integrations, with a focus on data types and storage formats.
src/zenml/integrations/__init__.py	Added PolarsIntegration declaration.
src/zenml/integrations/constants.py	Included a new POLARS constant in the integrations list.
src/zenml/integrations/polars/...	Initialized Polars integration and materializers, with PolarsIntegration class and PolarsMaterializer class for handling Polars data frames.

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[strickvl]

@coderabbitai review

[coderabbitai]

Review Status

Actionable comments generated: 0

Configuration used: .coderabbit.yaml

Commits

Files that changed from the base of the PR and between 88f6bab and d923f01.

Files selected for processing (6)

• docs/book/user-guide/advanced-guide/data-management/handle-custom-data-types.md (1 hunks)
• src/zenml/integrations/init.py (1 hunks)
• src/zenml/integrations/constants.py (1 hunks)
• src/zenml/integrations/polars/init.py (1 hunks)
• src/zenml/integrations/polars/materializers/init.py (1 hunks)
• src/zenml/integrations/polars/materializers/dataframe_materializer.py (1 hunks)

Files skipped from review due to trivial changes (1)

• src/zenml/integrations/constants.py

Additional comments: 5

src/zenml/integrations/polars/materializers/__init__.py (1)

• 16-18: The import statement is correctly using # noqa to bypass linting errors for unused imports, which is standard in __init__.py files.

src/zenml/integrations/polars/__init__.py (1)

• 20-35: The PolarsIntegration class is defined correctly with necessary attributes and an activate method that imports materializers to ensure they are registered when the integration is activated.

src/zenml/integrations/__init__.py (1)

• 49-49: The addition of PolarsIntegration to the integrations __init__.py file is correct and necessary for the integration to be recognized and used within ZenML.

src/zenml/integrations/polars/materializers/dataframe_materializer.py (1)

• 30-119: The PolarsMaterializer class is correctly implemented with appropriate methods for loading and saving Polars dataframes and series, including the conversion to and from pyarrow tables, and proper cleanup of temporary directories.

docs/book/user-guide/advanced-guide/data-management/handle-custom-data-types.md (1)

• 25-25: The documentation has been correctly updated to include the PolarsMaterializer in the table of integration-specific materializers, reflecting the new feature added to ZenML.

[strickvl]

This all looks good to me, pending mypy fixes. The only thing I'd add now would be a test:

https://github.com/zenml-io/zenml/blob/develop/tests/unit/materializers/test_pandas_materializer.py

This gives you an idea of what I reckon would work, though the test you'd write would be located in tests/integration/integrations. (https://github.com/zenml-io/zenml/blob/develop/scripts/install-zenml-dev.sh will then install it by default as it's not in the list of ignored installations).

[christianversloot]

Thanks! A test was added in a54e5b4. Also, in c71ef81, ignores were added for the "missing library stubs or py.typed marker" error to ensure that tests pass (as recommended by mypy docs).

As a bit of extra information, since Polars dataframes do not support indices as far as I know (supported by a quick Google search), I did not add the _with_index_ test available in the Pandas test, only the regular one.

Edit: just realized (it's getting time for weekend) that one of the ignores was added at the wrong line. This was corrected in 6455082.

[strickvl]

Nice! This looks good to go from my side! I think this'll make its way into the release first thing next week, pending @bcdurak's review and tests passing. Thanks as always for the contribution!

[strickvl]

@christianversloot thanks for this contribution! It'll be part of the 0.54.0 release coming soon.

[christianversloot]

Great! Thanks for the quick reviews.