Python

lale

Latest version: v0.7.2

PyUp actively tracks 471,271 Python packages for vulnerabilities to keep your Python environments secure.

Scan your dependencies

1.6

3. pretty_print lists a list of external modules in `wrap_imported_operators`.

0.7.2

CI and RASL fixes.

0.7.1

- fixes to autogen schemas
- fix to autoai_libs DateTransformer

0.7.0

* Improves support for partial_fit
* Improves the pretty printer
* Improves support for typed users
* Adds lale.lib.sklearn.perceptron (wrapping sklearn.linear_model.Perceptron)
* RASL (experimental):
- Removes support for Spark Dataframes that don't have an index
- Moves HashingEncoder to category_encoders and improved documentation

0.6.19

Updated version of aif360 during installation.

0.6.18

Adding py.typed marker to enable MyPy on packages that use Lale.

0.6.17

1. fit_transform for lale operators
2. partial_fit for xgboost and lightgbm
3. Minor fixes and updates to README.

0.6.16

Changed the version of black in setup.py compared to 0.6.15.

0.6.15

* Add support for scikit-learn 1.1
* Add lower and upper bound constraints for scikit-learn to help suggest recommended versions
* Add support for newer versions of XGBoost

0.6.14

Updated metrics to handle y as DataFrame.

0.6.13

Release for the KDD'22 tutorial

0.6.12

Hands-on tutorials for KDD'22: https://github.com/IBM/lale/tree/master/examples/kdd22

0.6.11

1. RASL: balanced accuracy, balanced_accuracy_and_di
2. Documentation improvements
3. lale.lib.autoai_libs.DateTransformer

0.6.10

Fixes and changes to RASL, lale.lib.aif360 and import and export from sklearn.

0.6.9

1. rasl fixes
2. a fix for autoai_ts_libs
3. a change to Hyperopt's fit to accept a validation dataset.

0.6.8

1. Batching can handle an iterable or data loader without knowing n_batches.

0.6.7

1. Batching changes to use task graphs
2. Removed autoai_ts_libs operators
3. BatchedTreeEnsemble estimators from SnapML
4. New rasl operators such as BatchedBaggingClassifier and HashingEncoder
5. Spilling in task graphs

0.6.6

1. Bug fixes
2. Improved interface for Monoids
3. Spilling in task graphs
4. multi-column index in SparkWithIndex

0.6.5

1. Fixes a regression (https://github.com/IBM/lale/commit/33d897218edd404ea5ddc4757c719f46fadf4bd8)
2. New lale.lib.rasl operators.

0.6.4

Added a new operator lale.lib.autoai_libs.ColumnSelector.

0.6.3

Release with correct schema updates for xgboost 1.5.1.

0.6.2

A version that is fully tested (almost, without static checks) on Python 3.9.
Contains minor fixes compared to the previous version.

0.6.1

1. New RASL operators: MinMaxScaler, OrdinalEncoder and OneHotEncoder
2. Fixes and changes for autoai-ts-libs
3. Scikit-learn compatibility by creating a `steps` property on lale pipelines and a mechanism to forward attribute access.

0.6.0

1. Schema changes for autoai_ts_libs.
2. `partial_fit` for a pipeline.
3. `diff` of pipelines.
4. Some fixes and other changes.

0.5.11

New release that delivers the `string_indexer` fix for 0.5.x.

0.5.10

1. New RASL operators: MinMaxScaler, OrdinalEncoder and OneHotEncoder
2. Fixes and changes for autoai-ts-libs
3. Scikit-learn compatibility by creating a steps property on lale pipelines and a mechanism to forward attribute access.

0.5.9

Simplified combined fairness and predictive accuracy metrics to use a linear combination.

0.5.8

- schema changes for autoai_ts_libs.
- partial_fit for a pipeline.
- diff of pipelines.
- some fixes and other changes.
- fixes for autoai_ts_libs.

0.5.7

- Making pretty_print() more robust.
- Making fairness support more robust.

0.5.6

1. RASL operator implementation such as Filter, Aggregate, GroupBy, OrderBy etc.
2. Changes for ensembling experiments with lale.lib.aif360.
3. Refactoring of lale.lib.aif360 and creation of a new setup target `fairness`.
4. Customize schemas if the environment has sklearn 1.0.
5. Update of schema constraints based on the "weakest precondition" work.
6. Other changes and bug fixes.

0.5.5

1. Access to 2 multi-table datasets: go_sales and imdb.
2. Improvement in error messages
3. Support for predict_log_proba
4. Bug fixes