Skip to content

v0.14.1
Compare
Choose a tag to compare
@angela97lin angela97lin released this 29 Sep 20:44
· 1499 commits to main since this release
v0.14.1
7dcf640
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

v0.14.1 Sep. 29, 2020

Enhancements

  • Updated partial dependence methods to support calculating numeric columns in a dataset with non-numeric columns #1150
  • Added get_feature_names on OneHotEncoder #1193
  • Added detect_problem_type to problem_type/utils.py to automatically detect the problem type given targets #1194
  • Added LightGBM to AutoMLSearch #1199
  • Updates scikit-learn and scikit-optimize to use latest versions - 0.23.2 and 0.8.1 respectively #1141
  • Added __str__ and __repr__ for pipelines and components #1218
  • Included internal target check for both training and validation data in AutoMLSearch #1226
  • Add ProblemTypes.all_problem_types helper to get list of supported problem types #1219
  • Added DecisionTreeClassifier and DecisionTreeRegressor classes #1223
  • Added ProblemTypes.all_problem_types helper to get list of supported problem types #1219
  • DataChecks can now be parametrized by passing a list of DataCheck classes and a parameter dictionary #1167
  • Added first CV fold score as validation score in AutoMLSearch.rankings #1221
  • Updated flake8 configuration to enable linting on init.py` files #1234
  • Refined make_pipeline_from_components implementation #1204

Fixes

  • Updated GitHub URL after migration to Alteryx GitHub org #1207
  • Changed Problem Type enum to be more similar to the string name #1208
  • Wrapped call to scikit-learn's partial dependence method in a try/finally block #1232

Changes

  • Added allow_writing_files as a named argument to CatBoost estimators. #1202
  • Added solver and multi_class as named arguments to LogisticRegressionClassifier #1202
  • Replaced pipeline's ._transform method to evaluate all the preprocessing steps of a pipeline with .compute_estimator_features #1231
  • Changed default large dataset train/test splitting behavior #1205

Documentation Changes

  • Included description of how to access the component instances and features for pipeline user guide #1163
  • Updated API docs to refer to target as "target" instead of "labels" for non-classification tasks and minor docs cleanup #1160
  • Added Class Imbalance Data Check to api_reference.rst #1190 #1200
  • Added pipeline properties to API reference #1209
  • Clarified what the objective parameter in AutoML is used for in AutoML API reference and AutoML user guide #1222
  • Updated API docs to include skopt.space.Categorical option for component hyperparameter range definition #1228
  • Added install documentation for libomp in order to use LightGBM on Mac #1233
  • Improved description of max_iterations in documentation #1212
  • Removed unused code from sphinx conf #1235
    ###Testing Changes

Breaking Changes

  • DefaultDataChecks now accepts a problem_type parameter that must be specified #1167
  • Pipeline's ._transform method to evaluate all the preprocessing steps of a pipeline has been replaced with .compute_estimator_features #1231
  • get_objectives has been renamed to get_core_objectives. This function will now return a list of valid objective instances #1230
Assets 2
Loading