A modular active learning framework for Python
MIT License
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Published by cosmic-cortex almost 4 years ago
This release includes a fix for a new feature added in 0.4.0
.
Published by cosmic-cortex almost 4 years ago
modAL 0.4.0 is finally here! This new release is made possible by the contributions of @BoyanH, @damienlancry, and @OskarLiew, many thanks to them!
pandas.DataFrame
support, thanks to @BoyanH! This was a frequently requested feature which I was unable to properly implement, but @BoyanH has found a solution for this in #105.on_transformed=True
upon initialization.Committee
sets classes when fitting, this solves the error which occurred when no training data was provided during initialization. This fix was contributed in #100 by @OskarLiew, thanks for that!Published by cosmic-cortex about 4 years ago
Committee.teach()
(#63)Published by cosmic-cortex almost 5 years ago
ActiveLearner
now supports np.nan
and np.inf
in the data by setting force_all_finite=False
upon initialization. #58check_X_y
no longer converts between datatypes. #49modAL.utils.data_vstack
now falls back to numpy.concatenate if possible.Fixes by @zhangyu94:
modAL.selection.shuffled_argmax
#32modAL.batch.ranked_batch
fixed. #30modAL.batch.select_instance
fixed. #29Published by cosmic-cortex almost 6 years ago
random_tie_break=True
to the query strategies first shuffles the pool then uses a stable sorting to find the instances to query. In the case where the maximum utility score is not unique, it is equivalent of randomly sampling from the top scoring instances.modAL.expected_error.expected_error_reduction
runtime improved by omitting unnecessary cloning of the estimator for every instance in the pool.Published by cosmic-cortex almost 6 years ago
In this small release, the expected error and log loss reduction algorithms (Roy and McCallum, 2001) were added.
Published by cosmic-cortex almost 6 years ago
In this release, the focus was on multilabel active learning strategies. The following algorithms were added:
Published by cosmic-cortex about 6 years ago
The new release of modAL is here! This is a milestone in its evolution, because it has just received its first contributions from the open source community! :) Thanks for @dataframing and @nikolay-bushkov for their work! Hoping to see many more contributions from the community, because modAL still has a long way to go! :)
learner.query()
can be used without training the model first..query()
methods changed for BaseLearner
and BaseCommittee
to allow more general arguments for query strategies. Now it can accept any argument as long as the query_strategy
function supports it..score()
method was added for Committee
. Fixes #6.modAL.density
module was refactored using functions from sklearn.metrics.pairwise
. This resulted in a major increase in performance as well as a more sustainable codebase for the module.numpy.vstack
calls replaced with numpy.concatenate
.np.sum(generator)
calls were replaced with np.sum(np.from_iter(generator))
because deprecation of the original one.Published by cosmic-cortex over 6 years ago
ActiveLearner
. Sampling for values are made by strategies estimating the possible gains for each point. Among these, three strategies are implemented currently: probability of improvement, expected improvement and upper confidence bounds.modAL.models.BaseLearner
abstract base class implemented. ActiveLearner
and BayesianOptimizer
both inherit from it.modAL.models.ActiveLearner.query()
now passes the ActiveLearner
object to the query function instead of just the estimator.modAL.utils.selection.multi_argmax()
now works for arrays with shape (-1, )
as well as (-1, 1)
.Published by cosmic-cortex over 6 years ago
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.Published by cosmic-cortex over 6 years ago
predictor
was renamed to estimator
, X_initial
and y_initial
was renamed to X_training
and y_training
.Published by cosmic-cortex almost 7 years ago
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