A modular active learning framework for Python
modAL.utils.combination.make_query_strategy
function factory to make the implementation of custom query strategies easier.ActiveLearner
and Committee
models can be fitted using new data only by passing only_new=True
to their .teach()
methods. This is useful when working with models where the fitting does not occur from scratch, for instance tensorflow or keras models.modAL.utils.selection.weighted_random()
to avoid division with zero.sklearn.utils.check_array
calls removed from modAL.models
, performing checks now up to the estimator. As a consequence, images doesn't need to be flattened. Fixes #5 .BaseCommittee
now inherits from sklearn.base.BaseEstimator
.modAL.utils.combination.make_linear_combination
rewritten using genexps, resulting in performance increase.predictor
was renamed to estimator
, X_initial
and y_initial
was renamed to X_training
and y_training
.Modular Active Learning framework for Python3
modAL is finally released! For its capabilities and documentation, see the page https://cosmic-cortex.github.io/modAL/!
modAL requires
You can install modAL directly with pip:
pip install modAL
Alternatively, you can install modAL directly from source:
pip install git+https://github.com/cosmic-cortex/modAL.git