Pycaret Versions Save

An open-source, low-code machine learning library in Python

2.3.9

2 years ago

Release: PyCaret 2.3.9 | Release Date: March 27th, 2022 (BUG FIXES)

Summary of Changes

2.3.8

2 years ago

Release: PyCaret 2.3.8 | Release Date: March 21st, 2022 (BUG FIXES)

Summary of Changes

2.3.7

2 years ago

Release: PyCaret 2.3.7 | Release Date: March 20th, 2022 (NEW FEATURES, BUG FIXES)

Summary of Changes

2.3.6

2 years ago

Release: PyCaret 2.3.6 | Release Date: Januray 12th, 2022 (NEW FEATURES, BUG FIXES)

Summary of Changes

2.3.5

2 years ago

Release: PyCaret 2.3.5 | Release Date: November 19th, 2021 (NEW FEATURES, BUG FIXES)

Summary of Changes

2.3.4

2 years ago

Release: PyCaret 2.3.4 | Release Date: September 23rd, 2021 (NEW FEATURES, BUG FIXES)

Summary of Changes

2.2.3.1

2 years ago

Release: PyCaret 2.3.3 | Release Date: July 24th, 2021 (NEW FEATURES, BUG FIXES)

Summary of Changes

  • Fixed issues with [full] install by pinning interpret<=0.2.4
  • Added support for S3 folder path in deploy_model() with AWS
  • Enabled experimental Optuna TPESampler options to improve convergence (in tune_model())

2.3.2

2 years ago

Release: PyCaret 2.3.2 | Release Date: July 7th, 2021 (NEW FEATURES, BUG FIXES)

Summary of Changes

2.3.1

3 years ago

Release: PyCaret 2.3.1 | Release Date: April 28, 2021 (SEVERAL BUGS FIXED)

Summary of Changes

  • Fixed an exception with missing variables (display_container etc.) during load_config()
  • Fixed exceptions when using Ridge and RF estimators with cuML (GPU mode)
  • Fixed PyCaret's cuML wrappers not being pickleable
  • Added an extra check to get_all_object_vars_and_properties internal method, fixing exceptions with certain estimators
  • save_model() now supports kwargs, which will be passed to joblib.dump()
  • Fixed an issue with load_model() from AWS (duplicate .pkl extension) - thanks to markgrujic (https://github.com/pycaret/pycaret/pull/1128)
  • Fixed a typo in documentation - thanks to koorukuroo (https://github.com/pycaret/pycaret/pull/1149)
  • Optimized Fix_multicollinearity transformer, drastically reducing the size of the saved pipeline
  • interpret_model() now supports data passed as an argument - thanks to jbechtel (https://github.com/pycaret/pycaret/pull/1184)
  • Removed infer_signature from MLflow logging when log_experiment=True.
  • Fixed a rare issue where binary_multiclass_score_func was not pickleable
  • Fixed edge case exceptions in feature selection
  • Fixed an exception with finalize_model when using GroupKFold CV
  • Pinned mlxtend>=0.17.0, imbalanced-learn==0.7.0, and gensim<4.0.0

2.3.0

3 years ago

Release: PyCaret 2.3.0 | Release Date: February 21, 2021

Modules Impacted: pycaret.classification pycaret.regression pycaret.clustering pycaret.anomaly pycaret.arules pycaret.nlp

Summary of Changes

  • Added new interactive residual plots in the pycaret.regression module. You can now generate interactive residual plots by using residuals_interactive in the plot_model function.
  • Added plot rendering support for streamlit applications. A new parameter display_format is added in the plot_model function. To render plot in streamlit app, set this to streamlit.
  • Revamped Boruta feature selection algorithm. (give it a try)
  • tune_model in pycaret.classification and pycaret.regression is now compatible with custom models.
  • Added low_memory and max_len support to association rules module (https://github.com/pycaret/pycaret/pull/1008)
  • Increased robustness of DataFrame checks (https://github.com/pycaret/pycaret/pull/1005)
  • Improved loading of models from AWS (https://github.com/pycaret/pycaret/pull/1005)
  • Catboost and XGBoost are now optional dependencies. They are not automatically installed with default slim installation. To install optional dependencies use pip install pycaret[full].
  • Added raw_score argument in the predict_model function for pycaret.classification module. When set to True, scores for each class will be returned separately.
  • PyCaret now returns base scikit-learn objects, whenever possible
  • When handle_unknown_categorical is set to False in the setup function, an exception will be raised during prediction if the data contains unknown levels in categorical features.
  • predict_model for multiclass classification now returns labels as an integer.
  • Fixed an edge case where an IndexError would be raised in pycaret. clustering and pycaret. anomaly
  • Fixed text formatting for certain plots in pycaret.classification and pycaret.regression.
  • If a logs.log file cannot be created when setup is initialized, no exception will be raised now (support for more configurable logging to come in the future)
  • User added metrics will not raise exceptions now and instead return 0.0
  • Compatibility with tune-sklearn>=0.2.0
  • Fixed an edge case for dropping NaNs in the target column.
  • Fixed stacked models not being tuned correctly.
  • Fixed an exception with KFold when fold_shuffle=False.