All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Remove debug logs in DeTriTraining.
- Remove some files that are not necessary in the package.
- Fixed bugs in
artificial_ssl_dataset
, now support again pandas DataFrame and y_unlabeled returns the right values
feature_fusion
andprobability_fusion
methods for restricted insslearn.restricted
module.
- CoForest random integer is now compatible with Windows.
- Add a parameter to
artificial_ssl_dataset
to force a minimum of instances. Issue #11 - Add a parameter to
artificial_ssl_dataset
to return indexes. Issue #13
- The
artificial_ssl_dataset
changed the process to generate the dataset, based in indexes. Issue #13
- DeTriTraining now is vectorized and is faster than before.
- Hot fix for avoid problems with Pypi
- Methods now support no unlabeled data. In this case, the method will return the same as the base estimator.
- In OneHotEncoder, the
sparse
parameter is nowsparse_output
to avoid a FutureWarning.
- CoForest now is most similar to the original paper.
- TriTraining can use at least 3 n_jobs. Fixed the bug that allows using as many n_jobs as cpus in the machine.
- Fixed a bug in TriTraining when one of the base estimators has not a random_state parameter.
- Fixed OneVsRestSSL with the random_state parameter.
- Fixed WiWTriTraining when no
instance_group
parameter is not provided. - Fixed a FutureWarning for
sparse
parameter inOneHotEncoder
. Changed tosparse_output
.
- CoTraining support a
threshold
parameter (default to 0.5) to control the threshold for adding new instances in the next iteration.
- Fixed a bug in CoTraining using LabelEncoder.