modAL 0.2.1

Release notes

New features

• 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.

Fixes

• Checks added to modAL.utils.selection.weighted_random() to avoid division with zero.
• ABC metaclassing now compatible with earlier Python versions (i.e. Python 2.7). Fixes #3 .
• 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.

modAL 0.2.0

Release notes

New features

• Information density measures. With the information_density function in modAL.density, density-based information metrics can be employed.
• Functions for making new utility measures by linear combinations and products. With the function factories in modAL.utils.combination, functions can be transformed into their linear combination and product.

Changes

• ActiveLearner constructor arguments renamed: predictor was renamed to estimator, X_initial and y_initial was renamed to X_training and y_training.
• ActiveLearner, Committee and CommitteeRegressor now also inherits from sklearn.base.BaseEstimator. Because of this, for instance, get_params() and set_params() methods can be used.
• The private attributes of ActiveLearner, Committee and CommitteeRegressor now exposed as public attributes.
• As a result of the previous, the classes now can be cloned with sklearn.base.clone.

First release

modAL 0.1.0

Modular Active Learning framework for Python3

Release notes

modAL is finally released! For its capabilities and documentation, see the page https://cosmic-cortex.github.io/modAL/!

Installation

modAL requires

• Python >= 3.5
• NumPy >= 1.13
• SciPy >= 0.18
• scikit-learn >= 0.18

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