Snowpark ML release notes¶
This article contains the release notes for the Snowpark ML, including the following when applicable:
Behavior changes
New features
Customer-facing bug fixes
Note
These notes do not include changes in features that have not been publicly released.
Version 1.1.2 (2023-12-18)¶
New features and updates¶
Model development updates:
Implemented
precision_score
metric in SQL.
Bug fixes¶
Fixed an issue where stack trace was being hidden by telemetry.
Model development fixes:
Inferring model signatures no longer materializes the full dataframe in memory.
Version 1.1.1 (2023-12-6)¶
New features and updates¶
Designated Snowpark ML Modeling API as Generally Available.
New
passthrough_col
parameter in the Modeling API allows you to exclude specific columns, like index columns, during training or inference when not explicitly specifyinginput_cols
.
Bug fixes¶
Model development fixes:
confusion_matrix
provided incorrect results when the row number could not be divided by the batch size.
Version 1.1.0 (2023-12-1)¶
New features and updates¶
GridSearchCV
andRandomizedSearchCV
execution is now distributed on multi-node warehouses.
Bug fixes¶
Model development fixes:
Columns were being excluded if their normalized names did not match the names specified in
output_columns
inOrdinalEncoder
andLabelEncoder
. Output columns no longer need to be valid Snowflake identifiers.
Version 1.0.12 (2023-11-15)¶
New features and updates¶
None
Bug fixes¶
Model development fixes:
Increased the column capacity of
OrdinalEncoder
.
Version 1.0.11 (2023-10-28)¶
New features and updates¶
Add support for
kneighbors
.Support
DecimalType
as a data type.
Bug fixes¶
Model development fixes:
Fix support for XGBoost and LightGBM models using SKLearn Grid Search and Randomized Search model selectors.
Fix metrics compatibility with Snowpark DataFrames that use Snowflake identifiers
Version 1.0.10 (2023-10-15)¶
New features and updates¶
precision_score
,recall_score
,f1_score
,fbeta_score
,precision_recall_fscore_support
,mean_absolute_error
,mean_squared_error
, andmean_absolute_percentage_error
metric calculations are now distributed.
Bug fixes¶
Model development fixes:
Fix UTF-8 decoding errors when using modeling modules on Windows.
Fix alias definitions causing
SnowparkSQLUnexpectedAliasException
in inference.
Version 1.0.9 (2023-09-28)¶
New features and updates¶
Calculation of
log_loss
metric is now distributed.
Bug fixes¶
Model development fixes:
Building images no longer fails with some Docker setups.
Embedding local ML library no longer fails when the library is imported by zipimport.
Update incorrect documentation about platform argument in the
deploy
function.
Version 1.0.8 (2023-09-15)¶
New features and updates¶
None
Bug fixes¶
Model development fixes:
Ordinal encoder can be used with mixed input column types.
Fix an issue when the sklearn default value is
np.nan
.
Version 1.0.7 (2023-09-05)¶
New features and updates¶
Allow disabling telemetry.
Bug fixes¶
Model development fixes:
Fix an error related to
pandas.io.json.json_normalizer
.
Version 1.0.6 (2023-09-01)¶
New features and updates¶
Model development: Size of metrics result can exceed previous 8MB limit.
Bug fixes¶
Model development fixes:
Fixed a bug when using simple imputer with NumPy >= 1.25.
Fixed a bug when inferring the type of label columns.
Version 1.0.5 (2023-08-17)¶
This release contains internal changes only.
Version 1.0.4 (2023-07-28)¶
New features and updates¶
Model development: Input dataframes can now be joined against data loaded from staged files.
Model development: Added support for non-English languages.
Bug fixes¶
None
Version 1.0.3 (2023-07-14)¶
This release contains internal changes only.
Version 1.0.2 (2023-06-22)¶
New features and updates¶
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 that are lists of single values.
Version 1.0.1 (2023-06-16)¶
Behavior changes¶
Model development: Changed Metrics APIs to follow scikit-learn metrics modules:
accuracy_score
,confusion_matrix
,precision_recall_fscore_support
, andprecision_score
methods move tometrics.classification
.
New features and updates¶
Model development: Added metrics:
f1_score
fbeta_score
recall_score
roc_auc_score
roc_curve
log_loss
precision_recall_curve
Version 1.0.0 (2023-06-09)¶
Initial public preview release.