ModAL Versions Save

A modular active learning framework for Python

0.2.1

6 years ago

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.

0.2.0

6 years ago

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.

0.1.0

6 years ago

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