ALiPy v1.2.0

ALiPy v1.2.0: This is a bug-fix release with api changes of AURO and AUDI.

Upgrade from pypi

pip install --upgrade alipy

Changelog

alipy.query_strategy.QueryMultiLabelAUDI and alipy.query_strategy.QueryTypeAURO

• API change Add parameter model to AUDI and AURO algorithm who are using LabelRanking model to evaluate unlabeled data. They will train a new LabelRanking model inside the algorithm which may take a lot of time if the the labeled and unlabeled pool is large. Now, user can pass a trained LabelRanking Model to save some time if your base model is a LabelRanking model.

alipy.query_strategy.QueryTypeAURO

• Fix Fix a bug in AURO method which will query labeled entries in the latter iteration.

alipy.query_strategy.QueryInstanceBMDR and alipy.query_strategy.QueryInstanceSPAL

• BMDR and SPAL will relax the constraints and try to solve the QP problem again if solving the original problem is failed.

alipy.query_strategy.multi_label.LabelRankingModel

• LabelRanking model will use the same initialization parameters instead of initializing randomly when re-training.

alipy.index.multi_label_tools.py

• Use relative import in multi_label_tools.py.

alipy.query_strategy.cost_sensitive.py

• Set cost to 1 instead of raising an error if cost is not provided.

Multiple modules

• Optimize code and update comments.

• Fix some warnings.

• Upload the test code. exec pytest in the test folder to run the test.

• Update example code. the labelranking model in multi label setting will be trained in an incremental way which will save a lot of time and make the performance more stable.