Sklearn2pmml

Breaking changes

None.

New features

None.

Minor improvements and fixes

* Updated Java libraries.

Breaking changes

None.

New features

* Added support for in-place file conversion.

If the `estimator` parameter to the `sklearn2pmml.sklearn2pmml(estimator, pmml_path)` utility function is a path-like object (eg. `pathlib.Path` or string), then the Pytho side of the SkLearn2PMML package shall pass it forward to the Java side (without making any efforts to load or modify anything about it).

This opens the door for the safe conversion of legacy and/or potentially harmful Pickle files.

For example, attempting to convert an unknown origin and composition estimator file to a PMML document:

python
from sklearn2pmml import sklearn2pmml

sklearn2pmml("/path/to/estimator.pkl", "estimator.pmml")


Minor improvements and fixes

* Added `--version` command-line option.

Checking the version of the currently installed command-line application:


sklearn2pmml --version


* Fixed the version standardization transformation.

Breaking changes

* Required Python 3.8 or newer.

This requirement stems from underlying package requirements, most notably that of the NumPy package (`numpy>=1.24`).

Portions of the SkLearn2PMML package are still usable with earlier Python versions.
For example, the `sklearn2pmml.sklearn2pmml(estimator, pmml_path)` utlity function should work with any Python 2.7, 3.4 or newer version.

* Migrated setup from `distutils` to `setuptools`.

* Migrated unit tests from `nose` to `pytest`.

Testing the (source checkout of-) package:


python -m pytest .


New features

* Added command-line interface to the `sklern2pmml.sklearn2pmml()` utility function.

Sample usage:


python -m sklearn2pmml --input pipeline.pkl --output pipeline.pmml


Getting help:


python -m sklearn2pmml --help


* Added `sklearn2pmml` command-line application.

Sample usage:


sklearn2pmml -i pipeline.pkl -o pipeline.pmml


Minor improvements and fixes

None.

Breaking changes

None.

New features

* Separated version transformation into two parts - version standardization (from vendor-extended PMML 4.4 to standard PMML 4.4) and version downgrade (from PMML 4.4 to any earlier PMML version).

Minor improvements and fixes

* Eliminated the use of temporary file(s) during version transformation.

* Improved version downgrade.

Breaking changes

* Refactored the downgrading of PMML schema versions.

Previously, the downgrade failed if the generated PMML document was not strictly compatible with the requested PMML schema version. Also, the downgrade failed if there were any vendor extension attributes or elements around (ie. attributes prefixed with `x-` or elements prefixed with `X-`).

The new behaviour is to allow the downgrade to run to completion, and display a grave warning (together with the full list of incompatibilities) in the end.

See [SkLearn2PMML-433](https://github.com/jpmml/sklearn2pmml/issues/433#issuecomment-2442652934)

* Updated logging configuration.

The Java backend used to employ SLF4J's default logging configuration, which prints two lines per logging event - the first line being metadata, and the second line being the actual data.

The new logging configuration prints one line per logging event.
The decision was to drop the leading metadata line in order to de-clutter the console.

New features

* Added support for using `pcre2` module functions in expressions and predicates.

For example, performing text replacement operation on a string column:

python
from sklearn2pmml.preprocessing import ExpressionTransformer

Replace sequences of one or more 'B' characters with a single 'c' character
transformer = ExpressionTransformer("pcre2.substitute('B+', 'c', X[0])")


Minor improvements and fixes

* Added support for single-quoted multiline strings in expressions and predictions.

Breaking changes

* Assume `re` as the default regular expression (RE) flavour.

* Removed support for multi-column mode from `StrngNormalizer` class.
String transformations are unique and rare enough, so that they should be specified on a column-by-column basis.

New features

* Added `MatchesTransformer.re_flavour` and `ReplaceTransformer.re_flavour` attributes.
The Python environment allows to choose between different RE engines, which vary by RE syntax to a material degree.
Unambiguous identification of the RE engine improves the portability of RE transformers between applications (train vs. deployment) and environments.

Supported RE flavours:

| RE flavour | Implementation |
|---|---|
| `pcre` | [PCRE](https://pypi.org/project/python-pcre/) package |
| `pcre2`| [PCRE2](https://pypi.org/project/pcre2/) package |
| `re` | Built-in `re` module |

PMML only supports Perl Compatible Regular Expression (PCRE) syntax.

It is recommended to use some PCRE-based RE engine on Python side as well to minimize the chance of "communication errors" between Python and PMML environments.

* Added `sklearn2pmml.preprocessing.regex.make_regex_engine(pattern, re_flavour)` utility function.

This utility function pre-compiles and wraps the specified RE pattern into a `sklearn2pmml.preprocessing.regex.RegExEngine` object.

The `RegExEngine` class provides `matches(x)` and `replace(replacement, x)` methods, which correspond to PMML's [`matches`](https://dmg.org/pmml/v4-4-1/BuiltinFunctions.html#matches) and [`replace`](https://dmg.org/pmml/v4-4-1/BuiltinFunctions.html#replace) built-in functions, respectively.

For example, unit testing a RE engine:

python
from sklearn2pmml.preprocessing.regex import make_regex_engine

regex_engine = make_regex_engine("B+", re_flavour = "pcre2")

assert regex_engine.matches("ABBA") == True
assert regex_engine.replace("c", "ABBA") == "AcA"


See [SkLearn2PMML-228](https://github.com/jpmml/sklearn2pmml/issues/228)

* Refactored `StringNormalizer.transform(X)` and `SubstringTransformer.transform(X)` methods to support Pandas' Series input and output.

See [SkLearn2PMML-434](https://github.com/jpmml/sklearn2pmml/issues/434)

Minor improvements and fixes

* Ensured compatibility wth Scikit-Learn 1.5.1 and 1.5.2.