Snowflake-ml-python

Behavior Changes

- Model Registry: Prohibit non-snowflake-native models from being logged.
- Model Registry: `_use_local_snowml` parameter in options of `deploy()` has been removed.
- Model Registry: A default `False` `embed_local_ml_library` parameter has been added to the options of `log_model()`.
With this set to `False` (default), the version of the local snowflake-ml-python library will be recorded and used when
deploying the model. With this set to `True`, local snowflake-ml-python library will be embedded into the logged model,
and will be used when you load or deploy the model.

New Features

- Model Registry: A new optional argument named `code_paths` has been added to the arguments of `log_model()` for users
to specify additional code paths to be imported when loading and deploying the model.
- Model Registry: A new optional argument named `options` has been added to the arguments of `log_model()` to specify
any additional options when saving the model.
- Model Development: Added metrics:
- d2_absolute_error_score
- d2_pinball_score
- explained_variance_score
- mean_absolute_error
- mean_absolute_percentage_error
- mean_squared_error

Bug Fixes

- Model Development: `accuracy_score()` now works when given label column names are lists of a single value.

Behavior Changes

- Model Development: Changed Metrics APIs to imitate sklearn metrics modules:
- `accuracy_score()`, `confusion_matrix()`, `precision_recall_fscore_support()`, `precision_score()` methods move from
respective modules to `metrics.classification`.
- Model Registry: The default table/stage created by the Registry now uses "_SYSTEM_" as a prefix.
- Model Registry: `get_model_history()` method as been enhanced to include the history of model deployment.

New Features

- Model Registry: A default `False` flag named `replace_udf` has been added to the options of `deploy()`. Setting this
to `True` will allow overwrite existing UDF with the same name when deploying.
- Model Development: Added metrics:
- f1_score
- fbeta_score
- recall_score
- roc_auc_score
- roc_curve
- log_loss
- precision_recall_curve
- Model Registry: A new argument named `permanent` has been added to the argument of `deploy()`. Setting this to `True`
allows the creation of a permanent deployment without needing to specify the UDF location.
- Model Registry: A new method `list_deployments()` has been added to enumerate all permanent deployments originating
from a specific model.
- Model Registry: A new method `get_deployment()` has been added to fetch a deployment by its deployment name.
- Model Registry: A new method `delete_deployment()` has been added to remove an existing permanent deployment.

Behavior Changes

- Model Registry: `predict()` method moves from Registry to ModelReference.
- Model Registry: `_snowml_wheel_path` parameter in options of `deploy()`, is replaced with `_use_local_snowml` with
default value of `False`. Setting this to `True` will have the same effect of uploading local SnowML code when executing
model in the warehouse.
- Model Registry: Removed `id` field from `ModelReference` constructor.
- Model Development: Preprocessing and Metrics move to the modeling package: `snowflake.ml.modeling.preprocessing` and
`snowflake.ml.modeling.metrics`.
- Model Development: `get_sklearn_object()` method is renamed to `to_sklearn()`, `to_xgboost()`, and `to_lightgbm()` for
respective native models.

New Features

- Added PolynomialFeatures transformer to the snowflake.ml.modeling.preprocessing module.
- Added metrics:
- accuracy_score
- confusion_matrix
- precision_recall_fscore_support
- precision_score

Bug Fixes

- Model Registry: Model version can now be any string (not required to be a valid identifier)
- Model Deployment: `deploy()` & `predict()` methods now correctly escapes identifiers

Behavior Changes

- Use cloudpickle to serialize and deserialize models throughout the codebase and removed dependency on joblib.

New Features

- Model Deployment: Added support for snowflake.ml models.

Behavior Changes

- Standardized registry API with following
- Create & open registry taking same set of arguments
- Create & Open can choose schema to use
- Set_tag, set_metric, etc now explicitly calls out arg name as metric_name, tag_name, metric_name, etc.

New Features

- Changes to support python 3.9, 3.10
- Added kBinsDiscretizer
- Support for deployment of XGBoost models & int8 types of data

Behavior Changes

- Big Model Registry Refresh
- Fixed API discrepancies between register_model & log_model.
- Model can be referred by Name + Version (no opaque internal id is required)

New Features

- Model Registry: Added support save/load/deploy SKL & XGB Models