Snowflake-ml-python

New Features

- Registry: Add support for `catboost` model (`catboost.CatBoostClassifier`, `catboost.CatBoostRegressor`).
- Registry: Add support for `lightgbm` model (`lightgbm.Booster`, `lightgbm.LightGBMClassifier`, `lightgbm.LightGBMRegressor`).

Bug Fixes

- Registry: Fix a bug that leads to relax_version option is not working.

Behavior changes

- Feature Store: update_feature_view takes refresh_freq and warehouse as argument.

Bug Fixes

- Registry: Fix a bug when multiple models are being called from the same query, models other than the first one will
have incorrect result. This fix only works for newly logged model.
- Modeling: When registering a model, only method(s) that is mentioned in `save_model` would be added to model signature
in SnowML models.
- Modeling: Fix a bug that when n_jobs is not 1, model cannot execute methods such as
predict, predict_log_proba, and other batch inference methods. The n_jobs would automatically
set to 1 because vectorized udf currently doesn't support joblib parallel backend.
- Modeling: Fix a bug that batch inference methods cannot infer the datatype when the first row of data contains NULL.
- Modeling: Matches Distributed HPO output column names with the snowflake identifier.
- Modeling: Relax package versions for all Distributed HPO methods if the installed version
is not available in the Snowflake conda channel
- Modeling: Add sklearn as required dependency for LightGBM package.

Behavior Changes

- Registry: `apply` method is no longer by default logged when logging a xgboost model. If that is required, it could
be specified manually when logging the model by `log_model(..., options={"target_methods": ["apply", ...]})`.
- Feature Store: register_entity returns an entity object.
- Feature Store: register_feature_view `block=true` becomes default.

New Features

- Registry: Add support for `sentence-transformers` model (`sentence_transformers.SentenceTransformer`).
- Registry: Now version name is no longer required when logging a model. If not provided, a random human readable ID
will be generated.

New Features

- FileSet: `snowflake.ml.fileset.sfcfs.SFFileSystem` can now be used in UDFs and stored procedures.

Bug Fixes

- Registry: Fix a bug that leads to module in `code_paths` when `log_model` cannot be correctly imported.
- Registry: Fix incorrect error message when validating input Snowpark DataFrame with array feature.
- Model Registry: Fix an issue when deploying a model to SPCS that some files do not have proper permission.
- Model Development: Relax package versions for all inference methods if the installed version
is not available in the Snowflake conda channel

Behavior Changes

- Registry: When running the method of a model, the value range based input validation to avoid input from overflowing
is now optional rather than enforced, this should improve the performance and should not lead to problem for most
kinds of model. If you want to enable this check as previous, specify `strict_input_validation=True` when
calling `run`.
- Registry: By default `relax_version=True` when logging a model instead of using the specific local dependency versions.
This improves dependency versioning by using versions available in Snowflake. To switch back to the previous behavior
and use specific local dependency versions, specify `relax_version=False` when calling `log_model`.
- Model Development: The behavior of `fit_predict` for all estimators is changed.
Firstly, it will cover all the estimator that contains this function,
secondly, the output would be the union of pandas DataFrame and snowpark DataFrame.

New Features

- FileSet: `snowflake.ml.fileset.sfcfs.SFFileSystem` can now be serialized with `pickle`.

Bug Fixes

- Registry: Now when providing Decimal Type column to a DOUBLE or FLOAT feature will not error out but auto cast with
warnings.
- Registry: Improve the error message when specifying currently unsupported `pip_requirements` argument.
- Model Development: Fix precision_recall_fscore_support incorrect results when `average="samples"`.
- Model Registry: Fix an issue that leads to description, metrics or tags are not correctly returned in newly created
Model Registry (PrPr) due to Snowflake BCR [2024_01](https://docs.snowflake.com/en/release-notes/bcr-bundles/2024_01/bcr-1483)

Behavior Changes

- Feature Store: `FeatureStore.suspend_feature_view` and `FeatureStore.resume_feature_view` doesn't mutate input feature
view argument any more. The updated status only reflected in the returned feature view object.

New Features

- Model Development: support `score_samples` method for all the classes, including Pipeline,
GridSearchCV, RandomizedSearchCV, PCA, IsolationForest, ...
- Registry: Support deleting a version of a model.

New Features

- Model Registry: Support providing external access integrations when deploying a model to SPCS. This will help and be
required to make sure the deploying process work as long as SPCS will by default deny all network connections. The
following endpoints must be allowed to make deployment work: docker.com:80, docker.com:443, anaconda.com:80,
anaconda.com:443, anaconda.org:80, anaconda.org:443, pypi.org:80, pypi.org:443. If you are using
`snowflake.ml.model.models.huggingface_pipeline.HuggingFacePipelineModel` object, the following endpoints are required
to be allowed: huggingface.com:80, huggingface.com:443, huggingface.co:80, huggingface.co:443.