NeuroData's package for exploring and using progressive learning algorithms
python
3.9 compatibility checksblack
formatting on notebooks & scriptsrequirements.txt
Synergistic Forests (SynF)
and Synergistic Networks (SynN)
LifelongClassificationForest
and LifelongClassificationNetwork
now use attributes & functions from ClassificationProgressiveLearner
.CircleCI
now automatically pushes repo releases to pip
.UncertaintyForest
inherit from LifelongClassificationForest
.network_construction_proportion
to default_network_construction_proportion
in LifelongClassificationNetwork.add_task
.keras
imports for notebooks.black
format checks for benchmarks & notebooks.keras
to tensorflow.keras
.NeuralClassificationTransformer
.experiments
& tutorials
.CITATION.cff
for software citations.Zenodo
badge.classes
parameter of TreeClassificationVoter
and KNNClassificationVoter
to be formatted as np.asarray
.lf_
attribute for UncertaintyForest
to the fit
function. Allowed uncertaintyforest_posteriorestimates.ipynb
benchmark to run as intended.set_decider
function of ProgressiveLearner
. Let the decider use its allotted data set by decider_idx = self.task_id_to_decider_idx[task_id]
.numpy
version in requirements.txt
to both resolve version conflicts with tensorflow
and maintain support for Python 3.6.contributing.rst
for better online display.n_estimators
to LifelongClassificationForests add_{task, transformer}
functions. This allows each task to have a different number of trees.finite_sample_correction
with the only possible value of kappa
being 1. We added the ability to set any kappa
.add_{task, transformer}
functions were None. The value of None indicated to use the default model parameter specified in the instantiation of the LifelongClassification{Forest, Network}. But, this meant that a user would be unable to train a new tree transformer to purity (by setting max_depth = None) unless default_max_depth
was None. This is an undesirable restriction. So, we changed the default value of all model parameters to LifelongClassification{Forest, Network} add_{task, transformer}
functions to the string "default" - this indicates to use the default model parameter specified in the instantiation of the LifelongClassification{Forest, Network}.transformer_voter_decider_split
was changed to network_construction_proportion
in network.pyProgLearn v0.0.2 updates the content and presentation of the documentation.
ProgLearn v0.0.1 is the culmination of 12 months worth of work. It contains the initial implementations, tutorials, and documentation of the Progressive Learning package. This version does NOT contain the completed documentation, overview, or tutorials. Development will continue on this branch towards the completion of at least documentation, overview, and tutorials. After the next release, our usage of the various branches will likely change.