GilesStrong/lumin: v0.8.0 - Mistake not...

v0.8.0 - Mistake not... Important changes GNN architectures generalised into feature extraction and graph collapse stages, see details below and updated tutorial Breaking Additions GravNet GNN head and GravNetLayer sub-block Qasim, Kieseler, Iiyama, & Pierini, 2019 Includes optional self-attention SelfAttention and OffsetSelfAttention Batchnorm: LCBatchNorm1d to run batchnorm over length x channel data Additional bn_class arguments to blocks, allowing the user to choose different batchnorm implementations 1, 2, & 3D Running batchnorm layers from fastai (https://github.com/fastai/course-v3) GNNHead encapsulating head for feature extraction, using AbsGraphFeatExtractor classes, and graph collapsing, using GraphCollapser classes New callbacks: AbsWeightData to weight folds of data based on their inputs or targets EpochSaver to save the model to a new file at the end of every epoch CycleStep combines OneCycle and step-decay of optimiser hyper-parameters New CNN blocks: AdaptiveAvgMaxConcatPool1d, AdaptiveAvgMaxConcatPool2d, AdaptiveAvgMaxConcatPool3d use average and maximum pooling to reduce data to specified number sizes per channel SEBlock1d, SEBlock2d, SEBlock3d apply squeeze-excitation to data channels BackwardHook for recording telemetric data during backwards passes New losses: WeightedFractionalMSE, WeightedBinnedHuber, WeightedFractionalBinnedHuber Options for log x & y axis in plot_feat Removals Scheduled removal of depreciated methods and functions from old model and callback system: OldAbsCallback OldCallback OldAbsCyclicCallback OldCycleLR OldCycleMom OldOneCycle OldBinaryLabelSmooth OldBinaryLabelSmooth SequentialReweight SequentialReweightClasses OldBootstrapResample OldParametrisedPrediction OldGradClip OldLsuvInit OldAbsModelCallback OldSWA OldLRFinder OldEnsemble OldAMS OldMultiAMS OldBinaryAccuracy OldRocAucScore OldEvalMetric OldRegPull OldRegAsProxyPull OldAbsModel OldModel fold_train_ensemble OldMetricLogger fold_lr_find old_plot_train_history _get_folds Unnecessary pred_cb argument in train_models Fixes Bug when trying to use batchnorm in InteractionNet Bug in FoldFile.save_fold_pred when predictions change shape and try to overwrite existing predictions Changes padding argument in conv 1D blocks renamed to pad Graph nets: generalised into feature extraction for features per vertex and graph collapsing down to flat data (with optional self-attention) Renamed FowardHook to ForwardHook Abstract classes no longer inherit from ABC, but rather have metaclass=ABCMeta in order to be compatible with py>=3.7 Updated the example of binary classification of signal & background to use the model and training resulting from https://iopscience.iop.org/article/10.1088/2632-2153/ab983a Also changed the multi-target regression example to use non-densely connected layers, and the multi-target classification example to use a cosine annealed cyclical LR Updated the single-target regression example to use WeightedBinnedHuber as a loss Changed from torch.tensor import Tensor to from torch import Tensor for compatibility with latest PyTorch Depreciations OldInteractionNet replaced in favour of InteractionNet feature extractor. Will be removed in v0.