v1.10.6

Improvements

• Refactor tasks.py to utilize sasctl.pzmm functions.
• Add model_info class to better capture model information.

v1.10.5

Buxfixes

• Updated write_json_files.py to allow for better support for prediction models
• Fixed issues relating to model card support.

v1.10.4

Improvements

• Added example Jupyter notebook for OpenAI models.

Buxfixes

• Dropped support for Python 3.6 and Python 3.7, as those are no longer officially supported versions.
• Added dmcas_misc.json template file for model card generation.
• Updated generation of ModelProperties.json to allow for model card generation immediately upon upload.

v1.10.3

Bugfixes

• Updated all examples to use current versions of sasctl functions
• Fixed bug in generate_model_card that threw an error when trying to generate the dmcas_misc.json file

v1.10.2

Improvements

• Introduced generate_model_card into write_json_files.py to allow for python models to work with planned model card tab in SAS Model Manager.

Bugfixes

• Allow for score code to impute NaN values in tables that have been loaded into SAS Model Manager.
• Fix issue where target_value was not being properly set during score code generation
• Updated pzmm_generate_requrirements_json.ipynb so the requirements file is generated properly.
• Added missing statistics to dmcas_fitstat.json file.

v1.10.1

Improvements

• Introduced ability to specify the target index of a binary model when creating score code.
  • index can be specified in pzmm.import_model.ImportModel.import_model()
  • Relevant examples updated to include target_index.

Bugfixes

• Reworked write_score_code.py to allow for proper execution of single line scoring.
• Added template files for assess_model_bias.py to allow for proper execution

v1.10.0

Prep for release

v1.9.4

Improvements

• Created pytest fixture to begin running Jupyter notebooks within the GitHub automated test actions.
• Updated examples:
  • Custom KPI and model parameters example now checks for the performance job's status.
  • Update H2O example to show model being published and scored using the "maslocal" destination.
  • Updated models to be more realistic for pzmm_binary_classification_model_import.ipynb.

Bugfixes

• Adjust pzmm.ScoreCode.write_score_code() function to be compatible with future versions of pandas.
• Reworked H2O section of pzmm.ScoreCode.write_score_code() to properly call H2OFrame values.
• Fixed call to pzmm.JSONFiles.calculate_model_statistics() in pzmm_binary_classification_model_import.ipynb.

v1.9.3

Improvements

• Refactored gitIntegration.py to git_integration.py and added unit tests for better test coverage.

Bugfixes

• Fixed issue with ROC and Lift charts not properly being written to disk.
• Fixed JSON conversion for Lift charts that caused TRAIN and TEST charts to be incorrect.
• Fixed issue with H2O score code and number of curly brackets.
• Updated score code logic for H2O to account for incompatibility with Path objects.
• Fixed issue where inputVar.json could supply invalid values to SAS Model Manager upon model import.
• Fixed issue with services.model_publish.list_models, which was using an older API format that is not valid in SAS Viya 3.5 or SAS Viya 4.

v1.9.2

Improvements

• Add recursive folder creation and an example.
• Add example for migrating models from SAS Viya 3.5 to SAS Viya 4.

Bugfixes

• Fixed improper json encoding for pzmm_h2o_model_import.ipynb example.
• Set urllib3 < 2.0.0 to allow requests to update their dependencies.
• Set pandas >= 0.24.0 to include df.to_list alias for df.tolist.
• Fix minor errors in h2o score code generation